Microsoft Viva Blog articles

Research Drop: Tracking the Rise of Perceived AI Value at Work

Megan_Benzing — Tue, 14 Apr 2026 16:02:50 GMT

Research Drop in Brief:

Most AI measurements focus on adoption, but adoption alone doesn't tell us whether AI is actually changing how work feels and functions. RIVA (Realized Individual Value of AI) captures what usage metrics miss: whether AI is meaningfully improving employees' work quality, speed, decision-making, and stress at the individual level.
Across three cross-industry panel surveys spanning two years, RIVA favorability rose 9-percentage points among AI-using managers and ICs – directional evidence that employees are moving from experimentation toward integrated, value-generating use.
But this rise isn't uniform. Industry context, organization size, job level, and department all shape who benefits, and in some cases, those gaps are actively widening.

Tracking AI access and usage has become relatively straightforward for most organizations. What’s harder to measure – and far less often tracked – is whether employees are actually experiencing value from the AI they use. Adoption without perceived value is a fragile foundation for transformation.

Metrics vary across the adoption and impact phase, and longitudinal employee-reported value data is rare. Where trend data does exist, it often tracks usage or infers value from productivity or sentiment proxies. Without understanding whether employees feel value from AI, organizations risk scaling tools that are technically adopted but experientially underwhelming.

The People Science team has conducted three cross-industry panel surveys on AI over the past two years (April 2024, July 2025, and November 2025), enabling us to look for signals of AI value trends over time. Though these are not repeated samples of employees in each time point, comparing these separate samples sheds directional insight on how perceived value of AI has shifted for employees that use AI at work.

The high-level takeaway? In our snapshots of AI-using managers and individual contributors, perceived value of AI has consistently risen across the three time periods, which is directional evidence that perceived value is rising as organizations move from experimentation toward more routine, integrated use.

But the nuance is that this pattern is not a one-size-fits-all for different groups of employees. This month’s Research Drop explores trend differences between various categories of employees: AI usage level, job level, organization size, industry, and department.

Rising employee perceptions of realized individual value of AI

Among the many signals organizations can track, employee perception offers something uniquely valuable: a direct window into whether AI is changing how work feels and functions at the individual level. These employee voices are the closest to the day-to-day reality of integration and keeping them front-and-center ensures that the transformation is happening with people, not just to them.

People Science measures perceived AI value at the individual employee level with our RIVA scale (Realized Individual Value of AI). This scale consists of six survey items related to individual benefits one might see from using AI at work (see note at the end of the blog for how we calculate RIVA favorability):

AI improves the quality of my work or output
AI allows me to complete tasks faster
AI simplifies my complex work tasks
AI helps me be more productive at work
AI helps me make better decisions
AI helps reduce my overall work-related stress

Over the three points, RIVA favorability rose 9-percentage points (pp) for managers and individual contributors who use AI.

The more employees are exposed to, gaining familiarity with, and using AI at work, the more likely they are to perceive value of AI at an individual level. The concepts captured with RIVA (time saved, reduced complexity, improved decision) are benefits that can increase with skill and workflow integration – signaling that employees are maturing in their AI use.

This maturation mirrors what we see in other skill development contexts, where value tends to compound as familiarity deepens. Early AI use is often exploratory and focused on simple task offloading. As employees build fluency, they begin applying AI to more complex, higher‑stakes work, where perceived returns and value grow accordingly.

It’s worth noting that RIVA captures the individual experience of AI, and a rising score doesn’t tell the whole story of organizational transformation. Even as individual value perceptions climb, some research suggests that true transformative business impact is still stalling at many organizations¹. But individual value is a meaningful piece of the puzzle, and employee experience often precedes and enables broader organizational impact.

So, what’s driving individual value perceptions up? One clear pattern is usage itself.

The intertwined nature of AI usage and value perception

Employees who use AI more frequently are also more likely to report higher RIVA favorability – a relationship that is likely reinforcing, not causal. People who perceive value from AI may be more motivated to use it more often and for more complex tasks, which in turn deepens that value perception. The dynamic likely flows both ways.

But what’s encouraging isn’t just the correlation, it’s the trajectory. Across time, the perceived value for each usage group remained relatively stable, but more employees are moving into higher frequency categories than before. The relationship between usage and value is holding, and the population experiencing it is growing.

For transformation leaders, this reframes the challenge: it’s less about getting employees access to AI and more about building the conditions for repeatable workflow integration that sustains and deepens that cycle.

Job level shapes the pace of AI value realization

For comparing these time points, we focused solely on managers and individual contributors – excluding leaders (VP/Director+). We have consistently found in our research that leaders report the highest level of favorability when it comes to perceptions of AI readiness, adoption, and impact, and that a gap exists between leaders, managers, and individual contributors within this transformation.

Though leaders are generally the most positive, we also typically find that managers report higher levels of perceived value than individual contributors. We found this to be true most of the time, except in July 2025, this value gap seemed to have closed momentarily.

By late 2025, managers reported a 9-percentage point (pp) lift in RIVA favorability compared to 2024 and an average of 13pp higher RIVA favorability than individual contributors. One plausible interpretation is that AI value may be surfacing faster in managerial work, where coordination-heavy tasks such as scheduling, review, and orchestration create frequent opportunities for AI to improve how work gets done².

Individual contributors, by contrast, saw their gains earlier (+15pp), with little additional movement across 2025. Rather than signaling a hard ceiling on IC value, this pattern may indicate that the next wave of AI impact is becoming more workflow- and team-dependent³. If so, the leadership implication is important: organizations may need to move beyond individual enablement to invest more intentionally in shared team practices, not just personal productivity (See our 2025 Agentic Teaming & Trust Research Report for early team-level insights).

Organizational agility may support faster employee value realization

Two employees with the same AI tools and the same enthusiasm can have vastly different experiences of AI value, simply based on where they work. Organization size is one of the most significant structural factors shaping that gap. That means the same investment in AI can produce very different returns for employees, depending on the organization they’re in.

We found that employees in midsize organizations (1,000 to 4,999 employees) consistently report more perceived value than those in large (5,000 to 14,999 employees) or enterprise (15,000+ employees) organizations.

Midsize employees have also continuously seen jumps in their perceived value (74% > 77% > 85%), while large and enterprise employees, though still increasing in perceived value, have generally more subtle changes in RIVA favorability.

The size of midsize organizations may work to their advantage in that they can translate AI access into day-to-day workflow integration faster than enterprises, which often face friction when scaling. Enterprise organizations will inherently have more siloed and disparate governance and permissioning models, which will involve more moving pieces to work through when deploying and scaling AI and AI agents. Regardless of size, it’s not an easy feat to fully integrate AI, and only 8% of companies have truly scaled AI across their entire enterprise⁴.

It’s important for organizations to keep transformation momentum so their employees don’t feel the lag in their personal AI experience as the company itself tries to push through the friction. Focusing on experimentation and continuous learning opportunities can help employees stay invested in the change even in specific roll-out milestones take longer than employees’ desire.

Industry context shapes how AI becomes valuable to employees

Just as organization size shapes the pace of AI integration, industry context shapes its direction. Different industries are turning to AI to solve fundamentally different problems, such as streamlining administrative burden in healthcare to managing compliance workflows in financial services. And layered on top of those distinct use cases are equally distinct policy and regulatory environments that govern how AI can be used, by whom, and under what conditions.

Industry context shapes not just how much value employees report from AI, but how quickly that value is felt and under what conditions. In our latest snapshot from late 2025, employees in the technology industry realized the highest AI value at 86% favorability. However, other industries weren’t too far off, signaling that technology isn’t overly dominant in value perception. Given the sample sizes within each industry, these patterns are best read as directional signals.

Different sectors are applying AI to different kinds of work, and they operate under different regulatory, operational, and cultural constraints. As a result, the path the value is unlikely to look the same across industries.

Across these directional snapshots, the clearest signal is not which industry is "winning," but that industries appear to be moving along different paths to value. Some, like healthcare, seem to be gaining momentum as AI becomes more workable in day-to-day workflows. Others, like financial and professional services, appear to face more friction in translating access into clear employee benefit. This may reflect differences in how directly AI is reshaping the core work of each industry, rather than a uniform difference in adoption or capability.

The leadership implication is that AI transformation is unlikely to succeed with a one-size-fits-all playbook. Industry conditions shape the use cases employees can trust, adopt, and benefit from. Organizations that align enablement, governance, and workflow design to the realities of their industry are more likely to turn AI access into meaningful value.

AI value may appear first for the functions closest to the transformation

An employee’s department at work will also influence their AI experience. Within any given organization, not all employees are experiencing AI transformation from the same vantage point. Where you sit and what your team is responsible for fundamentally shapes your proximity to AI decisions, your access to pilot groups, and ultimately how much value you’re able to extract from AI.

Across time points, IT employees have consistently reported the highest RIVA favorability scores. However, a big jump for HR employees brings their favorability almost on par with employees in IT.

Employees in IT and HR are not just users of AI – they are often owners and enablers of AI transformation across their organization. That position gives them earlier access, deeper experimentation opportunities, and clearer value understanding. These employees will likely continue to be at the top of value perception as they are “in the room” when AI transformation decisions are being made, increasing their dedication to AI’s success and deepening their knowledge of impactful AI use cases.

For HR, there was a 13-percentage point (pp) lift between the July 2025 and November 2025 samples. Employees HR teams navigate some of the most stringent privacy and security guardrails at work, given the sensitivity of employee data. But sustained investment in AI-enabled HR technology has helped bridge that gap. Research on the future of HR points to AI increasingly taking on the high-volume administrative work that has long defined the function (e.g., recruitment, onboarding, performance management, and workforce analytics) freeing HR professionals to focus on more strategic, people-centered work⁵. The 13pp lift in RIVA favorability for HR employees may reflect exactly that shift.

One department with consistent growth is research & development (R&D)/engineering, with a 7pp lift across each time point. Engineering is often discussed as an “at-risk” role due to AI’s ability to write code, and these employees are often more exposed to AI than other employees due to the direct nature of AI’s impact to their work. AI can increase up to 26% in developer productivity, and less experienced developers have higher adoption rates and greater productivity gains⁶. R&D/Engineering employees are on the frontlines of seeing their work transformed by AI and the data suggests they are finding genuine value in that transformation. But its worth acknowledging that being on the frontlines of AI’s impact isn’t without anxiety. When AI can write code, the question of what that means for one’s role and identity at work is real and valid.

Rising value perception and professional uncertainty can, and often do, coexist. Organizations can support these employees by framing AI as a skill amplifier rather than a replacement and investing in job crafting opportunities that help them evolve their roles alongside the technology.

Overall, employees are realizing the value of AI in their day-to-day work more than ever before. What’s changing now is where that value shows up. As agentic AI evolves, the impact will extend beyond individual productivity into how work gets coordinated, decisions get made, and teams operate.

But this shift will not happen evenly by default. Experiences still vary across roles, functions, and levels of access. Without intentional design, the benefits of AI risk concentrating among those already closest to it. As we move beyond scaling technology to truly reshaping work, organizations that focus on embedding AI into real workflows, building trust through clear guardrails, and enabling employees to develop new ways of working will be the ones that see lasting impact.

The question is no longer whether AI creates value. It is how intentionally that value is distributed, adopted, and sustained across the workforce.

Stay tuned for our May Research Drop to keep up with what the Microsoft People Science team is learning! 

This month’s Research Drop analyzed data from 2,331 employees across three separate panel surveys: AI Readiness Study (n = 910, global representation), Agentic Teaming & Trust Study (n = 811, global representation), and AI Outcome Study (n = 610, US-only representation). Participants were only included in the analysis if they were a manager or an individual contributor. Included participants had to be AI users, even if less than once a month. These samples do not represent repeated participants and are point-in-time snapshots. Any trend data is directional only and does not imply causation or changes of employees within each isolated sample. Employees respond to RIVA items on a 5-point Likert scale ranging from ‘Strongly Disagree’ to ‘Strongly Agree’, and we compare differences in favorability (‘Agree’, ‘Strongly Agree’ responses) across samples and between groups. RIVA favorability is calculated by averaging the favorability percentage of the six individual RIVA items.

¹MIT Sloan Management Review. (January 27, 2026). AI Use is Rising at Work but Productivity Gains Lag.

²Harvard Business Review. (July-August 2025). How AI is Redefining Managerial Roles.

³Dell'Acqua, F., Ayoubi, C., Lifshitz, H., Sadun, R., Mollick, E., Mollick, L., ... & Lakhani, K. (2025). The cybernetic teammate: A field experiment on generative AI reshaping teamwork and expertise (No. w33641). National Bureau of Economic Research.

⁴McKinsey. (November 5, 2025). The state of AI in 2025: Agents, innovation, and transformation.

⁵Gibbard, K., (September 16, 2025). Rethinking HR’s purpose alongside AI. SAP.

⁶Cui, Z., Demirer, M., Jaffe, S., Musolff, L., & Salz, T. (June 2025). The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers. Microsoft Research.

Research Drop: Evolving Transformational Leadership in the Age of AI

Megan_Benzing — Tue, 10 Mar 2026 15:00:00 GMT

Research Drop in Brief:

Employees who feel supported to integrate agentic AI are 1.8x as likely to be high-frequency AI users (use AI at least daily), yet there is a 28-percentage point gap in perceived support between leaders and ICs, a potential sign that substantive elements of support are getting lost in the rush to adopt AI.
Transformational leadership remains a strong foundation, but effective AI leadership requires evolving how these behaviors show up in practice.
When leaders encourage experimentation, offer personalized support, articulate a compelling vision, and model AI use – the four dimensions of transformational leadership – the effects are measurable: from nearly doubling employees' meta-cognition around AI to a 30-percentage point boost in trust in agentic AI.

AI is changing how work gets done, but it hasn’t changed what employees need most from their leaders. As organizations accelerate AI transformation, employees are looking for stability, clarity, and guidance to help them navigate what’s new, uncertain, and rapidly evolving. Technology may automate tasks and augment decision-making, but leadership remains the critical human force that shapes how change is experienced. Employees who feel supported by their leaders to integrate agentic AI into their work are 1.8x as likely to be high frequency AI users than those who do not feel that support.

This points to an important reality: in the AI era, the “what” of effective leadership hasn’t changed, but the “how” has. While many management processes are being automated or augmented, employees still need the personal, human side of leadership. The behaviors that help employees think critically, stay motivated, and grow through change are more important than ever.

To understand how leadership must evolve without losing its foundation, this month’s Research Drop revisits one of the most widely studied frameworks in organizational psychology: transformational leadership. By examining how its core dimensions show up in the context of AI, we can better understand what leaders must double down on – and what they must adapt – to support successful, human-centered AI transformation.

A Leadership Framework for Times of Change

Transformational leadership is a well-established leadership approach focused on inspiring followers to perform beyond expectations by shaping how they think, feel, and act¹. It provides a framework for us to guide our thinking on the changing responsibilities of leadership. Transformational leadership has four core behavioral dimensions that are empirically grounded anchors to leader and manager effectiveness:

Intellectual Stimulation	Encouraging critical thinking and novel problem-solving
Individualized Consideration	Personalizing coaching and developmental support
Inspirational Motivation	Communicating a compelling vision and organizational direction
Idealized Influence	Modeling values and taking principled positions

These dimensions describe what great leaders do to inspire strong productivity and outputs from their employees. Research debates how to measure these behaviors, but the underlying logic is robust – that leaders who challenge thinking, develop individuals, communicate purpose, and model values produce high engagement and performance². As AI reshapes how work gets done, these four dimensions offer a useful structure for understanding how leadership behaviors must adapt, rather than disappear.

Expanding Transformational Leadership in the Era of AI

As employees undergo change, they often look to their leaders to guide their own behavior. Employees may interpret their leaders’ actions and attitudes as signals of organizational expectations. There are early empirical signals that transformational leadership and perceived organizational support positively predict AI adoption³, further suggesting that leadership plays a critical role not only in encouraging AI use, but in shaping how employees experiment with, trust, and ultimately derive value from AI tools. Yet how this leadership style shows up in practice in the AI-era is shifting, and each dimension of transformation leadership offers a distinct lens on what this looks like in practice.

Intellectual Stimulation: Teaching Teams How to Think With AI

When leaders encourage critical thinking, questioning assumptions, and novel problem-solving, it helps employees be creative and innovative at work. The intellectual stimulation dimension relies on leaders showcasing strong judgment and curiosity. The leadership task is no longer to provide answers, but to model how to question them.

This approach is critical when using AI at work, as employees need to build their skills on questioning AI outputs and getting creative with AI agents. Research continues to show the potential negative impact that AI can have on human judgment, such as the concept of AI surrender⁴, where users accept AI outputs with limited scrutiny, bypassing their own judgment entirely. We found that 60% of employees skip accuracy checks when using AI and 56% of employees close AI tools if they feel the response is inaccurate, rather than adjusting their prompt. This places leaders at the center of developing disciplined, thoughtful AI use – where curiosity and skepticism coexist.

Leaders are a guidepost for thinking critically about AI, teaching followers how to evaluate AI-generated answers. Developing meta-cognition skills around AI has become a critical capability for employees at all levels. Meta-cognition around AI – the practice of both knowing how to use AI effectively and actively regulating how you execute those tasks – helps employees shift from passive AI users to deliberate cognitive partners. This can make the difference of whether AI usage consists of solely copy-pasting prompts and outputs, or intentionally questioning the use case, inputs, and desired outcome. Leaders have a strong role to play in helping their employees grow their meta-cognition. When leaders set clear expectations about how to use AI at work, employees nearly double their self-reported meta-cognition around AI – how they assess their own knowledge of AI methods and their habits of regulating AI use in practice.

Meta-cognition around AI increases when leaders share their expectations and provide examples and scenarios to guide their employees. This can look like:

Defining what “good” AI use looks like for the team (e.g., specifying that AI should inform and draft rather than finalize, keeping human review a non-negotiable step)
Naming the moments where human judgment should override AI (e.g., when the stakes of error are high, when outputs are plausible but unverified)
Asking reflective questions that build habit over time (e.g., “what would you have concluded without AI?”, “what did you have to correct or reframe in this output?”)

These leadership behaviors demonstrate not only the what behind adoption but also the how, encouraging a culture where critical thinking and human judgment remain the centerpiece of the workplace.

Individualized Consideration: Personalizing Support Beyond AI

Employees look to their leaders and managers for support across their career. Individualized consideration includes coaching, developmental attention, and attending to individual needs – where leaders take a personalized approach to supporting their employees.

With the emergence of AI technology at work, many AI tools enable personalized delivery at scale, so the “human touch” of consideration must shift to what AI cannot replicate. Leaders can leverage AI to surface trainings, suggest readings, and recap meetings, but the unique value of leaders lies in having the context to connect those resources to their followers’ broader goals and needs, the ability to address the genuine human concerns that AI transformation can surface, and the capability to help followers make sense of where to focus their attention in the midst of AI uncertainty. Employees are concerned about job security, the pressure to learn new ways of working, and more, which are exactly the kinds of concerns that AI can surface but not sit with – they require a leader who can acknowledge fear directly, contextualize change honestly, and adapt support to where each person actually is.

AI personalization does not replace individualized consideration and direct managerial support. Few things beat your manager sponsoring you for a project that perfectly aligns with your skills or sharing an AI use case that resonated directly with a problem you have been grappling with. This level of support can ingrain you into your team and your work, feeling understood and cared for. However, AI-specific support from human leaders is uneven. When we look on the surface, we found that 65% of all levels of employees report that they have the support to integrate agentic AI. However, a deeper look reveals a 28-percentage point gap: 78% of leaders feel they have the support they need to integrate agentic AI, compared to just 50% of individual contributors. This gap suggests that substantive elements of support may be getting lost in the rush to adopt AI – and the behavior we see clearly shows that: only 19% of managers have provided 1:1 coaching or mentoring on AI use. What leaders believe is sufficient is not always what employees experience in practice.

As the role of the manager changes with the introduction of AI, coaching, development, and mentoring from leaders becomes more important⁵. Yet only half of individual contributors feel supported to integrate agentic AI, individualized, human-led guidance on AI remains largely out of reach for the people who need it most.

While AI can tailor content, leaders can personalize meaning and support throughout AI transformation. For example, leaders can provide structure to vibe coding experimentation sessions by helping followers channel their upskilling into something that will contribute to their personal growth or a direct value add to the business to help them shine. As AI automates personalization at scale, leaders must focus their attention on the individualized support only humans can provide.

Inspirational Motivation: Creating Purpose During AI Transformation

A main responsibility of leaders is to set and communicate the strategy of the organization. Inspirational motivation includes articulating a compelling vision, conveying optimism, and expressing confidence in followers to motivate them to act and provide purpose to their work. Employees who are aligned with leadership are 78% more motivated than those who feel unaligned and employees who are highly optimistic about the future of their role are 101% more motivated than those who are not optimistic⁶.

Inspiration doesn’t come from more AI messages; it comes from leaders sharing how AI is changing their own work. Employees may feel overwhelmed and inundated with AI-related changes and rollouts without a clear understanding of the vision. When messaging is “lip service,” employees can see through it. They are craving tangible modeling experiences, where leaders are transparently showing the what, when, why, and how of their own AI use. The more leaders can present an authentic perspective on how AI will change the workplace, the greater the trust and buy-in from the employees. Inspirational motivation in the AI era is less about the frequency of communication and more about sharing their personal goals for AI.

This could include leaders sharing their own AI use, their own AI uncertainty, and their own moments of discovery with AI. Leaders’ openness and authenticity can communicate compelling vision of what AI makes possible for their team and their organization – cascading motivation from leader to employee. While 85% of leaders are motivated to integrate agentic AI into their work, only 56% of individual contributors feel the same, a 29-percentage point gap that mirrors the divide in perceived support. Leaders may have better access and understanding of the vision of AI for the organization, but the limited support and vision sharing can leave employees lacking the drive to adopt AI and sustain that adoption.

When employees are motivated by their leaders, it can instill a level of optimism that is a catalyst for AI adoption. Employees with high agentic AI readiness (e.g., skills, opportunity) report high levels of AI optimism – an average of 83% favorability. This drops to 47% for neutral readiness employees, and 13% for low readiness employees.

With low optimism and readiness for AI integration, employees are not set up effectively to adopt and perceive value from using AI. Leaders can try to mitigate against these downstream impacts by focusing on communicating a compelling vision for what AI makes possible and how it can elevate employees’ work, rather than replace it.

Idealized Influence: Leading by Example in Human‑AI Collaboration

Influence during change is a core mechanism for failure or success of the initiative. Idealized influence consists of displaying conviction, modeling core values, and taking principled positions on important decisions.

Throughout AI transformation, employees are watching leaders closely and their AI adoption actions will mirror their leaders’ approach. Leaders need to model what credible human-AI leadership looks like: whether and when they use it, when they override it, how they acknowledge its limits, and how they openly share when it falls short. A leader who openly says “I ran this through AI, it missed the nuance, here’s how I caught it” likely does more to normalize critical AI use than any policy or training could. This can look like a leader walking through their prompting process in a team meeting, acknowledging in a 1:1 that they're still figuring out where AI fits in their own workflow, or openly crediting AI for a first draft while explaining what they changed and why. Leaders who model thoughtful, intentional AI use create the conditions for their teams to experiment with confidence, adopt AI critically rather than passively, and ultimately realize the full value of human-AI collaboration.

Leadership role modeling directly shapes how much value employees realize from AI – employees who agree their leaders role model effective AI use report a 17-percentage point uplift in realized value from agentic AI. When leaders engage with AI intentionally, it creates conditions for their teams to do the same. Role modeling has always mattered and in an AI-first workplace, it's the difference between a team that experiments and one that waits.

AI transformation is not just a technological shift; it is a leadership one. The four dimensions of transformational leadership offer a powerful lens for understanding how leaders can shape not only AI adoption, but also trust, optimism, and long-term value creation. As AI becomes embedded in everyday work, leaders who intentionally model thoughtful AI use, personalize support beyond what technology can deliver, communicate purpose with authenticity, and guide employees through uncertainty will define what successful human-AI collaboration looks like.

Transformational leadership remains a strong foundation, but effective AI leadership requires evolving how these behaviors show up in practice. Employees are already watching how their leaders engage with AI. The question is whether what they see encourages critical thinking, confidence, and growth, or hesitation and disengagement.

As organizations continue to navigate AI-driven change, leaders who focus not just on adoption, but on how people experience and make sense of that change, will be best positioned to unlock meaningful and lasting impact.

Stay tuned for our April Research Drop to keep up with what the Microsoft People Science team is learning! 

This month’s Research Drop analyzed 1,800 global employees from the Microsoft People Science Agentic Teaming & Trust Survey from July 2025.

¹Bass, B. M. (1985). Leadership and Performance Beyond Expectations. Free Press.

²Wang, G., Oh, I.-S., Courtright, S. H., & Colbert, A. E. (2011). Transformational leadership and performance across criteria and levels: A meta-analytic review of 25 years of research. Group & Organization Management, 36(2), 223–270. https://doi.org/10.1177/1059601111401017

³Wang, X., Liu, Y., & Ao, M. (2025). Transformational leadership and employee AI usage: The role of perceived organizational support and competitive workplace climate. Frontiers in Psychology, 16, Article 1581337. https://doi.org/10.3389/fpsyg.2025.1581337

⁴Shaw, S. D. & Nave, G. (2026). Thinking – Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender. Available at SSRN 6097646. https://dx.doi.org/10.2139/ssrn.6097646

⁵Deloitte. (May 9, 2025). Reinventing the manager’s role for the future of work with AI and human collaboration. 2025 Global Human Capital Trends.

⁶PwC. (November 12, 2025). Rewiring the future of work. PwC’s Global Workforce Hopes and Fears Survey 2025.

Research Drop: Fighting AI Slop with Meta-Cognition around AI

Megan_Benzing — Tue, 10 Feb 2026 23:44:53 GMT

Research Drop in Brief:

When employees aren’t intentional or thoughtful with their AI use, it can drive quality issues and increase “workslop,” and 60% of employees report skipping accuracy checks.
Employees need to build strong meta-cognition skills, including knowledge about effective AI use and regulation of their AI use.
Meta‑cognition around AI strongly predicts perceived value, correlating with RIVA (r = .748) and RTVA (r = .597), meaning that when employees think critically about their AI approach and output, they get more value from using AI.
Strong levers for meta-cognition around AI aren’t necessarily technical: When leaders role model AI use (boosts meta‑cognition by 22-percentage points) and teams feel psychologically safe (boost meta-cognition by 16-percentage points), employees engage more thoughtfully with AI.

Across industries, organizations are accelerating their investment in AI tools and encouraging employees to incorporate them into daily workflows. In some cases, AI use is becoming an explicit expectation – employees are encouraged, nudged, or even monitored to demonstrate consistent AI activity. In some cases, AI licenses are reallocated if usage is low, and team metrics increasingly reflect “AI integration.”

But “adoption for adoption’s sake” overlooks a critical factor: the quality of AI use matters as much as the quantity. AI use without intentional habits can degrade quality, erode trust, and even harm team performance.

We’re now seeing the rise of “workslop,” which is low quality AI generated output produced quickly but without depth, accuracy, or care¹. Not only does workslop undermine performance, it also erodes trust between colleagues and damages reputations. Concerningly, our research shows that 60% of employees skip accuracy checks when using AI, which accelerates the AI slop prevalence.

Low intent-in, low-quality out. The remedy isn’t more adoption, it’s better habits and more disciplined practices. For this month’s Research Drop, we explore a concept often referenced in learning science but rarely applied to workplace AI: meta-cognition.

Thinking about thinking: Introducing meta-cognition around AI

Meta‑cognition, at its simplest, is the act of thinking about your own thinking.² It’s the idea that someone can notice what they are doing and then regulate how they are doing it. In learning research, we see that meta-cognition helps people not only execute tasks but regulate how they execute them. It strengthens judgment, improves quality, and drives continuous improvement. Meta‑cognition is what turns a repeated behavior into a refined skill.

When it comes to AI use, we adapted this theory and applied it to employees’ AI use. This two-step meta-cognition around AI process emerged:

Knowledge of how to use AI
- Acknowledge skill level
- Understand methods (e.g., retrieval vs. synthesis vs. planning)
- Choose the right approach for the task
Ongoing regulation of AI use
- Select an appropriate plan (e.g., decompose task; choose tools)
- Review output for relevance and accuracy
- Evaluate success and iterate

Increased knowledge of how to use AI helps employees avoid defaulting to “generic prompting” and instead make intentional decisions about how AI should support the task at hand. Then regulating their AI use transforms AI from a passive response generator into an active collaborator.

Using these meta-cognition practices together, employees can evolve from being bystanders of AI use to becoming cognitive partners with AI, building judgment and skill as they go. This process roots AI use in intent rather than convenience.

When meta-cognition turns adoption into value

Our research found that meta-cognition around AI goes beyond a framework – it predicts meaningful differences in AI value.

Employees who are intentional about their AI use can leverage that reflection to refine and improve their output. In our research, we found that when employees know their “what” and their “how” they are more likely to see the value from using AI – beyond just time savings. There is a strong correlation between meta-cognition around AI and Realized Individual Value of AI (RIVA; r = .748).

This matters because organizations often focus on adoption metrics (e.g., logins, prompt counts, volume of use), when another driver of ROI lies in the quality of the interaction. High intent users get better output, make fewer mistakes, and build durable skills over time.

And it’s not just individual value that is positively impacted – there is also a strong correlation between meta-cognition around AI and Realized Team Value of AI (RTVA; r = .597).

Teams with strong meta‑cognitive habits share better prompts, collaborate more effectively, and align more consistently on accuracy and quality standards. AI becomes a mechanism for collective improvement, not just personal productivity.

These good habits scale – and when they do, so can impact.

Leveraging culture to build meta-cognition muscles

The use of AI is rapidly growing, and employees need structures that help them navigate new expectations and evolving norms. It becomes increasingly important to make sure employees feel supported throughout the change and leaders/managers play a powerful role in shaping this experience.

Leaders must role model intentional AI use

Many organizational changes are inherently social – as new tools, processes, or structures are rolled out, there is a socialization process that occurs. Formal socialization can take shape as corporate communications, guides, and documentations. But often informal socialization can make the most impact. Employees look to leaders, peers, and influential team members to understand not just what a change is, but how it should be done.

Yet when it comes to AI, one study found that only 41% of employees reported receiving encouragement from leadership without detailed instructions or having the contextual understanding of how to use AI in a meaningful way³. And our latest research shows that role modeling itself is inconsistent: 74% of leaders report seeing effective role modeling compared to just 61% of managers and 49% of ICs. Leaders need to go beyond blanketed encouragement to tangible role modeling so create that clarify and investment for their employees.

The impact of true modeling is substantial: Employees who observe leaders demonstrating effective AI use show a 22-percentage point increase in meta-cognition around AI compared to those who don’t.

Without knowing a deeper what/how, employees are left on their own to figure out how to best implement AI into their work and what output standards might look like, which can result in a lack of proper usage and AI slop. However, if leaders can be transparent and share:

what task they used AI for
why they chose that approach
how they evaluated output
what they adjusted

This gives employees a blueprint for building their own meta‑cognitive habits and approaches to leveraging AI at work. Role modeling can help create a consistent standard for output expectations – providing thoughtful guidance on how employees should think about weighing time savings, quality, and depth when working with AI. This iterative experience also helps leaders continue build upon their own meta-cognition muscles, creating a learning cycle throughout the organization.

Psychological safety enables honest conversations about AI use

AI isn’t just a tool, it reshapes collaboration. When employees use AI to generate work, draft content, or prototype ideas, that output enters team workflows. If an employee receives low-effort, AI-generated work, their opinion of their colleague lowers, such as their perception of their reliability and trustworthiness¹. And even if the work isn’t poor quality, one study found that 57% of employees admit to hiding their AI use at work⁴. When employees are not openly discussing their AI use at work, it prevents learning opportunities and cross-team upskilling. It can also broaden knowledge gaps between employees, where some team members are speeding ahead in AI fluency and practice, while others are being left behind.

Psychological safety can help reverse this dynamic. This environment not only increases effective AI adoption, but it can create trust between colleagues. Teams can:

share their AI approaches
critique and refine outputs together
discuss missteps without fear
co‑develop higher‑quality workflows

Through these processes, AI becomes a shared learning process rather than an individual performance risk.

We found a 16-percentage point difference in meta-cognition around AI between employees who feel psychologically safe at work and those who don’t.

When employees are open about their AI use, they can share best practices and teach each other to regulate and critically think about their use cases. They can also hold each other accountable to quality and avoid unintentionally sending AI slop downstream. AI transformation is a social process and requires safe team cultures that encourage sharing and iterating together.

When leaders and teams are intentional and transparent in their AI use, employees across the organization can build their meta-cognition skills and shift their perspective of AI from one of low-intent to one of high-intent.

AI will continue reshaping work, but the value coming from these tools will depend on the human habits that we build behind the tools. Employees who think critically about their AI use can generate better outcomes, help their team to develop stronger practices, and cultivate an AI culture of quality, security, and trust. Adoption is the first step, but intentionality is the multiplier.

Stay tuned for our March Research Drop to keep up with what the Microsoft People Science team is learning! 

This month’s Research Drop analyzed 1,800 global employees from the Microsoft People Science Agentic Teaming & Trust Survey from July 2025.

¹Liebscher, A., Rapuano, K., & Hancock, J. T. (September 22, 2025). AI-Generated “Workslop” is Destroying Productivity. Harvard Business Review.

²Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460-475.

³Niederhoffer, K., Robichaux, A. & Hancock, J. T. (January 16, 2026). Why People Create AI “Workslop” and How to Stop It. Harvard Business Review.

⁴KPMG. (2025). Trust, attitudes, and use of artificial intelligence: A global study 2025.

Research Drop: Designing AI Feedback Systems by Industry

Megan_Benzing — Tue, 13 Jan 2026 16:00:00 GMT

Research Drop in Brief:

Employees want to meaningfully shape AI transformation, yet 34% still lack opportunities to give feedback on their AI experience.
High frequency AI users are more likely than low frequency AI users to report regular requests for input, suggesting that listening and adoption positively reinforce each other.
AI feedback is social: Informal conversation and team meetings are the most utilized feedback channels across industries, making them critical early-signal channels for AI rollout success.

Big transformations don’t stick by default; they stick by active listening and iteration. Organizations are networks of distinct communities and workflows, so a one‑size‑fits‑all change rollout rarely lands. The fastest way to de‑risk change is to give employees real ways to shape it: invite feedback early, often, and in the flow of work. Research shows that employee voice and involvement are significant drivers of change readiness and commitment to change – and their resistance and cynicism around the change is reduced¹.

The opportunity for listening in AI transformation is vast. In our research, 66% of employees feel they have adequate opportunities to share feedback on their AI experience and specifically among individual contributors (ICs), that drops to 52%. Increasing feedback opportunities matter as high frequency AI users (use AI daily) are more likely than low frequency AI users (use AI monthly) to report regular requests for input (e.g., company‑wide surveys, in‑product prompts), signaling that listening and adoption positively reinforce each other.

Integrating employee feedback can be a multiplier on change ROI. When organizations listen across channels and act on what they hear, they can accelerate AI from pilot programs to organizational value at scale. This level of iteration creates an agile change process that can adapt to the consistent technology and culture changes associated with AI transformation.

This month’s Research Drop dives into building an AI feedback system for transformation and outlines various feedback channels to explore. We’ll also map industry differences in preferred feedback channels to learn how different industries are meeting employees where they are in the flow of work. Because integrating AI tools is fundamentally a behavioral change², not just a technological one, context matters: the right channel in one industry may be the wrong one in another.

High frequency AI users have a feedback advantage

When you involve employees in change, you signal their experience matters and earn buy‑in as the transformation unfolds. The question is: how well are organizations actually doing this?

The answer depends on who you ask. Seventy-five percent of executives say their company is employee‑centric, yet only 23% of individual contributors (ICs) agree³. In our 2025 Agentic Teaming & Trust Research Report, this leader-IC gap shows up in enablement too: 78% of leaders report feeling supported to integrate AI agents into their work, compared to 50% of ICs. Closing this gap is critical, as employee‑centric organizations were found to be 7x more likely to succeed with AI³.

A practical way to narrow that gap is to build a positive feedback loop. The more avenues employees have to share their experience, the more involved they feel, and the faster teams can iterate toward fit. In our data, high frequency AI users are markedly more likely than low frequency users to report receiving:

Quarterly company‑wide surveys on AI (93% vs. 71%)
Monthly pulse surveys on AI (83% vs. 50%)
Weekly in‑product feedback requests (53% vs. 23%)

This is how the flywheel turns: listen → act → adoption → deeper listening. By asking people to reflect on what’s working (and what isn’t), organizations uncover friction early, improve the rollout, and strengthen the employee experience, especially for ICs who often feel least supported in agentic AI integration.

Listening where work happens: Industry patterns in AI feedback

Workflow redesign – and the value AI ultimately delivers – will look different across workplaces and industries. Roles, risk profiles, and communication norms vary widely, so the feedback system that informs change should vary, too. Think desk‑based flows and scheduled meetings in one sector versus shift‑based, patient‑ or customer‑facing work in another.

Depending on existing workflows, different industries lean on different ways of gathering employee input. Some rely more on traditional online surveys, while others surface feedback organically through standups, team huddles, or informal conversations. The point: there’s no one ‘right’ channel – what matters is building consistent, reliable ways for employees to signal what’s working and what isn’t. To understand how this plays out across industries in our sample, we examined industry-level patterns in feedback channel usage. In our data, most industries cluster around two‑thirds of employees who feel they have adequate opportunities to give feedback on their AI experience. By contrast, healthcare sits closer to half, underscoring the need for innovative channels tailored to shift‑based routines, clinical workflows, and higher privacy constraints.

Differences by industry likely reflect frontline mix, regulatory burden, and maturity of AI transformation as some sectors simply started experimenting earlier and have more established listening practices.

So, what changes by industry? It’s not just the cadence; it’s the mix of channels that meet employees where work actually happens.

We found differences in top‑utilized AI feedback channels across industries. The headline: AI feedback is social. Informal conversations and team meetings rank #1 and #2 nearly everywhere – teams process change together, and quick back‑and‑forths create the fastest path to signal and iteration.

Other interesting patterns:

Internal social forums are popular in construction, healthcare, and transportation/travel/hospitality. These are frontline‑heavy sectors where mobile‑friendly, non‑desk access makes forums easy to reach between tasks or at shift hand‑offs.
Pulse surveys show up strongly in manufacturing and transportation/travel/hospitality. A potential explanation is that short, targeted check‑ins fit shift schedules, can be team specific, and support rapid action loops without burden.
In‑product feedback tools are common in construction, financial & professional services, retail/food/beverage, and technology. Their workflows are often anchored in specific applications that make embedded prompts at the point of experience both relevant and efficient.

Different work, different ways to listen. Build a channel mix employees will actually use, one that is fast, familiar, and in the flow. This meets people where they work so feedback follows. Keep it easy, visible, and routine; these small, habitual touchpoints beat big, occasional asks.

Make employee feedback the engine of AI change

Change succeeds when we understand employees and their experience. Providing listening opportunities also enables employees to lean into the change by feeling involved and actively participating in the change. This process also helps change leaders learn in real time where AI initiatives are landing versus where they’re struggling.

Our data shows that feedback is social – so it’s time to build systems around social feedback. Treat hallway conversations, team huddles, and quick chats as consistent signal:

Capture it in the moment with lightweight prompts (e.g., “what’s one friction point you’ve had with this tool today?”) or notes tied to the task/tool.
Route it automatically to the owners of AI transformation (product, IT, HR) with the context needed to act.
Close the loop visibly so employees see their input become decisions.

Change moves faster when listening is woven into how work gets done. Surveys and pulses provide the backbone of organizational memory, while social feedback in meetings and everyday conversations adds the nuance that keeps AI transformation grounded in reality. Keep those signals connected in the flow of work, and the transformation becomes something employees actively shape, not something that just happens to them.

Stay tuned for our February Research Drop to keep up with what the Microsoft People Science team is learning! 

This month’s Research Drop analyzed 1,800 global employees from the Microsoft People Science Agentic Teaming & Trust Survey from July 2025. Leaders, managers, and individual contributors were represented across the industries included in this blog: Construction, Financial & Professional Services, Healthcare, Manufacturing, Retail, Food, & Beverage, Technology, and Transportation, Travel, & Hospitality.

¹Potnuru, R. K. G., Sharma, R., & Sahoo, C. K. (2021). Employee voice, employee involvement, and organizational change readiness: Mediating role of commitment-to-change and moderating role of transformational leadership. Business Perspectives and Research, 11(3), 355-371.

²De Cremer, D., Schweitzer, S., McGuire, J. J., & Narayanan, D. (November 19, 2025). How behavioral science can improve the return on AI investments. Harvard Business Review.

³Lovich, D., Meier, S., & Taylor, C. (November 26, 2025). Leaders assume employees are excited about AI: They’re wrong. Harvard Business Review.

Research Drop: Reframing AI Friction as a Mastery Challenge to Drive Impact

Megan_Benzing — Tue, 09 Dec 2025 16:00:00 GMT

Research Drop in Brief:

More than half of employees say agentic AI integration feels overwhelming and that they are struggling with the pace of change.
Employees who lean into friction – navigating new AI tools even when it feels overwhelming – show up to +23pp agentic AI readiness, +18pp realized value, and are 1.9x as likely to be high-frequency AI users.
Viewing friction as a mastery challenge versus a barrier hindrance can shape AI adoption outcomes and building trust, increasing recognition, and providing training can support this mindset shift.

Integrating AI and AI agents isn’t just a technical upgrade – it’s a cultural shift. Organizations are rethinking workflows, decision-making, and even what knowledge and expertise looks like in human-AI teams. This transition is likely to feel messy. It challenges deeply ingrained habits and mental models, creating inevitable friction. One way this friction shows up is with employees who struggle with the speed and complexity of AI adoption and integration. In our data we found that:

57% of employees feel overwhelmed by the idea of integrating agentic AI into their work
55% of employees struggle to keep up with the pace of change around agentic AI

This month’s Research Drop explores how to balance these feelings of strain with continued growth and impact.

The unexpected benefit of AI adoption friction

While we may initially assume that friction in AI transformation is something to be avoided, not all friction is equal. Technical breakdowns? Bad friction. Employees stretching beyond their comfort zone? Good friction.

Employees who are reporting this friction while navigating new AI tools and ways of working report up to 23 percentage points higher agentic AI readiness. They’re more likely to understand how to integrate agentic AI, feel motivated and confident in their skills, have opportunities to apply it, and perceive greater value in doing so. Interestingly, those feeling overwhelmed and struggling with the pace are often the ones facing these changes head-on – making them more ready to integrate AI agents, even when the process feels daunting. In fact, they’re 1.9x as likely to be high-frequency agentic AI users, showing that leaning into friction can accelerate adoption and impact.

These employees also report up to 18 percentage points higher realized individual value of agentic AI (RIVA). In other words, leaning into the challenge pays off: when employees avoid engaging with AI tools, they miss opportunities to build readiness and capture value. “Ignorance is bliss” may feel easier, but it delivers less impact.

This dynamic maps onto well‑established theory. Flow theory¹ suggests learning happens at the edge of our comfort zones; we need genuine challenge to grow. When employees encounter complexity and uncertainty, they are developing a new capacity to build relevant skills and knowledge. Mastery emerges when challenge meets skill, so without this friction, AI transformation might stall.

But not all friction and stress are created equal. Research shows there’s a difference between stressors that energize and signal opportunity versus those that drain and feel like obstacles. This distinction is called the Challenge–Hindrance Stressor Framework². When employees encounter friction with AI, they can interpret it as either type of stress – and that perception shapes whether they lean in or disengage.

A recent study³ applying this framework to AI found early signals that these interpretations matter:

Mastery challenge stressors (e.g., learning new skills, tackling complex problems with AI) spark positive emotions, which boost adoption intention.
Barrier hindrance stressors (e.g., technical breakdowns, unclear requirements) drive AI anxiety and reduce adoption intention.

If we want employees to move from overwhelmed to ready and confident, we need to shape the experience so agentic AI is perceived as a challenge (growth, mastery, value) rather than a hindrance (block, threat, anxiety).

Helping employees frame AI transformation as a mastery challenge

To better understand how to support employees facing friction during AI transformation, we dove deeper into the data and found three main levers that leaders and transformation change agents can pull: build trust, increase recognition, and provide training opportunities.

Build trust in the systems and the journey

Employees who are overwhelmed but still seeing value from AI agents are 1.7x as likely to trust AI agents than those who are overwhelmed and not seeing value. This higher level of trust may reduce employees’ tendency to categorize AI as a hindrance (e.g., fewer structural barriers, less anxiety). In the 2025 Agentic Teaming & Trust Report, we found that trust can be built by leadership role modeling, reliable systems, and peer advocacy. When we think about building trust in the face of challenge, consider:

Encourage leaders and peers to share clear “why” and “how” stories of AI use that include failures and lean into continuous role modeling
Invest in reliability and structure (e.g., consistent, well‑documented workflows and guardrails)
Create peer advocacy opportunities, such as shadowing or live/recorded demonstrations, so employees see the “line of sight” from effort to outcome

Reliable systems and social proof can help shift employee appraisals from hindrance to challenge, increasing agentic AI adoption, readiness, and value.

Recognize and reward investing in development/upskilling

We found that employees who are overwhelmed but still seeing value from AI agents are also 1.7x as likely to report they are recognized or rewarded for using AI compared with those who are overwhelmed and not seeing value. For these employees, this level of recognition and incentives can increase positivity around experimenting, trying new things, and pushing themselves out of their comfort zone. For rewards and recognition, consider:

Spotlighting success stories that celebrate learning and progress, not just perfection
Rewarding employees who get involved in peer learning groups or pilot cohorts that test out new features and processes
Tying recognition to experimentation, not just “getting it right” (e.g., “tried X, learned Y, improved Z”)

Recognition acts as a powerful signal, as it tells employees that effort matters as much as outcomes. When people see that experimenting and learning are valued, they’re more likely to embrace discomfort as part of growth rather than interpret it as risk.

Provide training that feels like a bridge, not a burden

Lastly, employees who are overwhelmed but are still seeing value from AI agents are 1.8x as likely to say they are adequately trained in how to use AI compared with those who are overwhelmed and not seeing value. Having training opportunities can support employees in understanding AI agents and their capabilities, which may turn the tide from an employee perceiving this transformation as a hindrance to a surmountable challenge. When considering training in the face of friction, consider:

Delivering just‑in‑time, role‑specific learning modules/opportunities tied to real work outcomes (demonstrating the “why” and long-term ROI)
Pairing training with coaching and on‑the‑job use, so the “learn” and “do” loops are closely intertwined
Offering skill ladders/micro‑credentials that let people see and celebrate progress

Closing the AI skill gap is critical. Training helps employees feel supported as they stretch into new technology and ways of working. One research study found that 3 in 5 employees reported being more likely to use AI at work if proper training were available⁴and technical self-efficacy was found to strengthen adoption pathways so increasing self-efficacy should also further push AI adoption and impact³.

Think about friction as fuel for growth, rather than failure. Organizations that frame AI as a challenge, not a threat, and reinforce trust, recognition, and training can turn moments of strain into opportunities for readiness and impact.

Stay tuned for our January Research Drop to keep up with what the Microsoft People Science team is learning! 

This month’s Research Drop analyzed 1,800 global employees from the Microsoft People Science Agentic Teaming & Trust Survey from July 2025. Employees were grouped in this analysis based on their favorability on two survey items (“I am overwhelmed by the idea of integrating agentic AI into my work” and “I am struggling to keep up with the pace of change surrounding agentic AI at work”) and composite scores on our Realized Individual Value of Agentic AI scale.

¹Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience. New York: Harper & Row.

²Cavanaugh, M. A., Boswell, W. R., Roehling, M. V., & Boudreau, J. W. (2000). An empirical examination of self-reported work stress among US managers. Journal of Applied Psychology, 85(1), 65.

³Chang, P. C., Zhang, W., Cai, Q., & Guo, H. (2024). Does AI-driven technostress promote or hinder employees’ artificial intelligence adoption intention? A moderated mediation model of affective reactions and technical self-efficacy. Psychology Research and Behavior Management, 413-427.

⁴BBC. (May 18, 2025). Workers optimistic but overwhelmed by AI – study.

Research Drop: Empowering Managers for an AI-First Future

Megan_Benzing — Tue, 11 Nov 2025 16:00:00 GMT

Research Drop in Brief:

Managers are a catalyst of AI transformation as they bridge strategy and execution, but only 56% of managers feel they have the support they need to integrate AI agents into their work.
Enabling managers during AI transforming scales impact as role modeling drives +17pp realized value of agentic AI, +22pp critical thinking on AI, and +30pp agentic AI trust.
Managers who create psychological safety during this change are key, as employees who feel psychologically safe not only report up to +20pp higher AI readiness and AI impact, but they are also 1.4x as likely to be high frequency agentic AI users.

For the future of AI-integrated organizations, human-AI teaming is poised to become the default experience. To reach this future state, managers play a critical role. They serve as a bridge between strategy and execution, enabling scalable transformation where leaders and employees align around shared goals.

Traditionally, managers have guided teams through organizational shifts and changes¹. But with the rise of AI, many companies are moving toward flatter structures, asking employees to do more with less by leveraging AI tools to meet productivity targets. This makes it essential to redesign the manager’s position to fit the needs of shifting organizations. Yet progress is lagging: recent research reveals a 66% gap between those who acknowledge the importance of reinventing the role of the middle manager and those who are making real progress in addressing it².

This month’s Research Drop explores the evolving role of the manager in an AI-first organization – where they are best suited to play an active role within AI transformation, rather than a diminishing role.

Cementing the Purpose of Managers

Research consistently shows that managers are multipliers of positive change. When managers role model AI use, experimentation, and learning, team adoption and value accelerate. Employees who report their leadership encourages AI experimentation save 55% more time a day with AI than those who don’t³.

When it comes to integrating AI agents into team workflows, managers are optimistic:

73% believe AI agents will give teams a competitive advantage
62% are advocates of integrating AI agents
64% believe AI agents could help their team achieve new goals

But optimism alone is not enough to enable managers to drive change. Half of managers (50%) reported feeling overwhelmed by the idea of integrating AI agents, and 49% struggle to keep up with the pace of change surrounding AI agents. More than half (55%) have faced at least two burnout indicators at work in the past six months (e.g., overwhelming workload), and only 56% feel they have the support they need to integrate AI agents effectively.

With the right support, managers can translate organizational strategy into team-level execution, shaping culture and mindset around key changes. They can transform urgency into opportunity and guide their teams confidently through this new era. But increasing manager support must come first.

The Evolving Manager Role in the Era of Agentic AI

In the evolving role of the manager, we are reimagining the mindset, knowledge, and skills of managers from traditional management to management in the era of AI. Going from control to curiosity, operational know-how to systems intelligence, and from telling and delegating to role modeling and orchestrating.

But how and to what extent we can achieve these shifts relies on enabling managers through resources and upskilling opportunities and empowering them to create a culture of experimentation and psychological safety.

Enable Managers to Turn Knowledge into AI Impact

Managers are in a prime position to support role redesign and knowledge transfer on their teams. They have unique visibility into daily work activities and team skill sets, making them well suited to identify the most impactful agentic AI use cases and capabilities. With managers acting as translators, AI impact can truly flourish.

However, managers aren’t getting the support they need. Fewer than two-thirds say they have the tools and resources (62%) or the opportunity (60%) to integrate AI agents into their work. Organizations are missing a critical opportunity – recent research shows that people managers are the least commonly targeted groups when it comes to training opportunities, chosen by fewer than 5% of leaders⁴.

This lack of training depth is limiting managers’ ability to codify and create processes around AI for their teams:

Only 20% of managers have created prompt libraries or “how-to” guides
Just 19% have provided 1:1 coaching or mentoring on AI use

Providing managers with the right training and resources enables them to transfer knowledge, build confidence, and establish repeatable practices for their teams, but less than 30% of managers have attended training on AI in the past six months. This suggests that a gap is emerging on manager-specific upskilling opportunities. When this investment does exist, it can create a foundation for lasting impact, where AI adoption is a continuous advantage rather than a one-time shift. Organizations should design for scalability, where manager support moves beyond ad-hoc resources to structured programs that help managers grow.

Leading by Example to Drive AI Success

When preparing their teams to integrate AI and agents into workflows, managers can adopt a coach/player mindset. They not only need to communicate enterprise-wide changes clearly and empathetically, but they must also integrate those changes themselves. And the more transparently they do it, the better.

Transparency accelerates upskilling and peer learning as teams design their day-to-day work. In our study, two ways managers showed up for their teams were by proactively sharing success stories (31%) and running small trial projects/experiments (31%). Active participation in AI transformation builds comfort and confidence for employees.

And when leaders role model, the impact is clear. For those who agree that their leaders role model effective AI use, they see:

+17pp uplift in realized value from agentic AI
+22pp increase in critical thinking about AI
+30pp boost in trust in agentic AI

Organizations should empower managers to be effective role models. Provide them the time, tools, and frameworks to experiment openly and share learnings back with their team. Managers can help their teams to set short-term goals around re-designing workflows and systems, managing dependencies, and facilitating connection points between tools. They can also formalize experimentation processes, such as building reflection into team rituals and rewarding insight generation as much as outcomes. When managers lead by example, they don’t just drive adoption – they lay the groundwork for trust, learning, and continuous improvement.

Building Psychological Safety into AI Integration

With any change comes tension and uneasiness as teams adapt to new expectations and ways of working. This is where psychological safety becomes critical. A manager’s role in fostering psychological safety creates an environment where employees embrace a growth mindset, take smart risks, and trust their team – building resilience in the face of change.

When it comes to AI, psychological safety also drives adoption. Employees who feel psychologically safe are 1.4x as likely to be high-frequency agentic AI users. They feel comfortable experimenting and asking questions. And when psychological safety is present, employees report greater readiness to integrate AI agents and perceive stronger impact.

Managers can foster psychological safety by creating human–AI teaming norms, clarifying roles between humans and AI, and encouraging open dialogue. They can create spaces for employees to speak up, ask questions, and learn from each other. Yet only 41% of employees say they’re asked for feedback on their AI experience in weekly team meetings – a missed opportunity for shared learning. Managers may want to make these conversations routine to build a culture of trust and collaboration.

Managers can guide teams to harness AI technology in ways that amplify human potential and create lasting impact. When organizations invest in manager enablement, they unlock a ripple effect: stronger role modeling, greater psychological safety, and faster team adoption and impact. The future of AI-integrated work depends on managers leading with confidence, transparency, and trust to move AI from a tool to a true competitive advantage.

Stay tuned for our December Research Drop to keep up with what the Microsoft People Science team is learning!  

This month’s Research Drop analyzed 1,800 global employees from the Microsoft People Science Agentic Teaming & Trust Survey from July 2025, including 819 leaders (C-Suite, VP, Director), 520 managers, 461 individual contributors.

References

¹Bevilacqua, S., Masárová, J., Perotti, F. A., & Ferraris, A. (2025). Enhancing top managers' leadership with artificial intelligence: insights from a systematic literature review. Review of Managerial Science, 1-37.

²Deloitte. (March 24, 2025). Is there still value in the role of managers?

³Atlassian. (November 19, 2025). AI Collaboration Report: “Using” AI is not enough – here’s what your organization is missing.

⁴i4cp. (2025). i4cp Pulse Survey Results: Agentic AI.

Ultimate Guide to the new Workplace Patterns Report in Viva Glint

craigramsay — Mon, 10 Nov 2025 18:11:30 GMT

Imagine having key data sources like employee engagement, meeting hours, and peer connections data in one place. That’s now a reality with the introduction today of the Workplace Patterns Report, a power tool that integrates point-in-time Viva Insights workplace habit data with point-in-time Viva Glint survey sentiment data. These two data sources empower analysts and admins to identify actionable patterns between how people work (workplace habits), and how they feel (survey sentiment).

To help you get a quick start, we’ve put together a playbook: Unlocking the Power of the Workplace Patterns Report which includes:

An introduction to building a holistic listening system.
FAQs from “What is the Workplace Patterns Report?” to “How do I interpret heatmaps?” and “Does a strong relationship prove causation?”
Top 10 workplace metrics tracked by Viva Insights and integrated into Viva Glint.
Case studies of two real-world scenarios highlighting how you can use the Workplace Patterns Report to surface relationships between habits like meeting hours, focus time, and experiences like work-life balance, and resources.
Tips, tricks, and recommendations for making the most of your data, protecting confidentiality, and customizing your reports.
Appendices with handy tables mapping common sentiment - workplace metric pairings.

The Workplace Patterns Report and holistic listening

Because employee attitudes and behaviors change rapidly and periodic surveys won’t give you timely insights. The Workplace Patterns Report (WPR) is all about holistic listening – connecting employee feedback with actual work patterns.

The playbook introduces holistic listening as a multi-channel, integrated approach. It’s not just about employee polls; it’s about combining active listening (like engagement surveys and manager feedback) with ambient signals (like collaboration patterns and meeting behaviors). The goal? To uncover patterns, anticipate needs, and inform focused interventions that can actually make a difference.

This Playbook is packed with FAQs about the report, how to interpret findings, and how to get the best from your data and report capabilities. Find out more in the Playbook!

Case Studies using real-World examples

Here are two case studies that illustrate the kinds of diagnoses you can do with the Workplace Patterns Report:

Case Study #1 Video: At what point are more meetings impacting work-life balance?

We see a common pattern, the “cliff” relationship, in the first case study. Work-life balance scores stay high until meeting hours hit 10–20 per week, then they drop off. So, yes—too many meetings can be detrimental, but a few are necessary for company life. The heatmap visuals make it easy to spot the tipping point.

Case Study #2 Video: Where’s the “sweet spot” for uninterrupted focus time?

Sometimes a relationship is not so obvious at a high level. One group may like more focus time, another prefers less. When averaged together, the relationship type may display “peaks” in odd places. Case study two disaggregates by worker type as individual contributors typically need more uninterrupted focus time than managers. The report shows a “spikey” pattern for resources, with ICs scoring highest when they have 12+ hours of focus time per week. Managers, on the other hand, peak at 4–12 hours. Filtering by role reveals these differences, and the playbook recommends checking comments for more context.

The Metrics: What’s Being Measured?

Here’s a sneak peek at the 10 workplace metrics you’ll find. Each metric is explained in detail in the Playbook:

After Hours Collaboration – How much time people spend working outside regular hours.
Call Hours – Time spent in Teams calls.
Collaboration Hours – Total time in meetings, emails, IMs, and calls.
Email Hours – Time spent on email.
Internal Network Size – Number of people with whom someone has reciprocal interactions.
Meeting Hours – Time spent in meetings.
Meeting Hours with Manager 1:1 – One-on-one time with the boss.
Multitasking Time – Time spent sending emails or IMs during meetings.
Uninterrupted Hours (Focus Time) – Blocks of deep work with no interruptions.
Meeting Hours with Skip Level – Time spent in meetings with the manager’s manager.

Tips & Tricks on how to use the report

The playbook is full of practical advice for making the most of your Workplace Patterns Report:

Start with a hypothesis.
Don’t just analyze everything—focus on what you think is happening in your team, then use the report to test your ideas.
Delve deeper.
Use filters to segment by role, department, or other attributes. Compare groups to see where the strongest relationships are hiding.
Look for actionable insights.
The report is a hypothesis-generating tool, not a magic wand. Use it to spot trends, then experiment with changes (like blocking more focus time or reducing after-hours work) and see what happens.
Protect confidentiality.
Only aggregate data is shown, and strict thresholds are enforced to keep individual info private.
Customize your view.
Filter by survey topics, workplace metrics, or subpopulations (like engineers or new hires) to uncover meaningful patterns.

More tips and tricks in the Playbook!

Further recommendations to increase the validity of your analyses

To get the best results, the playbook suggests:

Focus on groups using Microsoft software regularly.
Consider contextual factors (like remote vs. in-person work, busy seasons, or org changes).
Expect differences across populations—don’t lump everyone together.
Keep the employee experience front and center. There’s no “right” way to work; the goal is happiness and success for every team.

Take a deeper dive on these ideas in the Playbook!

What’s next with the Workplace Patterns Report?

Leaders will soon get access to continuous metrics (like Work-Life Balance and Collaboration) in a new tab, with email alerts to stay updated between survey cycles.

Research Drop: Amplifying Clarity and Impact of AI Agents by Aligning on Desired Outcomes

Megan_Benzing — Thu, 30 Oct 2025 15:37:00 GMT

Research Drop in Brief:

Employees on teams that align on both activities and outcomes they want to achieve with AI agents are 1.9x as likely to be high-frequency agentic AI users compared to teams focused only on activities.
Outcome-aligned teams report greater up to 22 percentage-points higher clarity when using multiple agents, such as clearer use cases and deeper understanding of capabilities.
When teams plan for outcomes, they report greater team-based transformation by introducing AI agents: improved knowledge sharing, problem-solving, and more.
Hear from Wipro, a leading AI-powered technology services and consulting company, on how they are anchoring on expected outcomes of agents to solve critical business problems.

“Task automation”, “Process optimization”, “Job redesign” – all top-of-mind concepts and phrases that surface when we think of agentic AI transformation. We continue to iterate and test the boundaries of AI agents at work and often start with thinking about what tasks we want them to do. Will they augment or automate? Which responsibilities are best suited for an AI agent?

But as AI agents turn AI technology from a personal productivity tool into a team value generator, it’s time to expand our thinking from not just the activities we want them to accomplish, but also the outcomes we want to achieve. This involves taking our thinking beyond how the “parts” work, to how they work together and whether we can achieve new outcomes by agent involvement¹.

In this month’s Research Drop, we unpack how expanding from considering solely tasks to also evaluating outcomes unlocks new ways of working not only with our teams, but with the agents themselves. And the data tells a clear story: when teams align on outcomes, not just activities, they unlock higher adoption, greater clarity, and amplified impact.

Driving adoption and clarity by aligning on goals

When teams take an outcome-driven approach to integrating AI agents into their work, it kicks off a flywheel of adoption to impact. By investing in strategic discussions and dedicating time to planning for this transformation, employees are more likely to lean into agentic AI. Employees on teams who are aligned not only on the activities they want AI agents to tackle, but also the outcomes they want AI agents to achieve are 1.9x as likely to be high-frequency agentic AI users than teams who have only discussed activities.

Employees on these teams also report a sharper sense of clarity when working with AI agents. Because their focus includes aligning on outcomes, they maintain clear role boundaries, even when multiple agents are in play. They know what each agent is capable of, where each fits, and which use cases unlock the most value, allowing them to optimize the experience rather than improvise. This clarity reflects a more mature agent strategy, where the teams have moved beyond “what tasks should we offload?” to a vision for how agentic AI integrates into the team’s way of working.

This alignment creates the conditions for scale. When teams know why they’re using agents and what success looks like, they can avoid fragmented experimentation and move toward intentional integration. That clarity may become a competitive advantage, enabling teams to adapt faster, deploy agents more effectively, and realize value sooner.

Elevated impact of focusing on outcomes

As we shift toward an outcome-focused approach to integrating agentic AI, we may need to rethink the data we use to measure success, moving beyond individual productivity to include metrics such as collaboration effectiveness and AI agent evaluation². Team processes may look different with the introduction of AI agents, as it’s not just about adding another tool, it’s about reshaping how work gets done. And we found that employees on teams that adopt this outcome-driven mindset are more likely to report seeing AI agents as teammates rather than just tools.

By aligning on outcomes, teams also report stronger teaming behaviors, from richer knowledge sharing to more creative problem-solving. They’re also more likely to feel proud of what their team accomplishes together. This team readiness step of planning for both activities and outcomes can transform how teams collaborate, unlocking value at a collective level.

The takeaway is clear: aligning on outcomes can be the difference between incremental gains and transformative impact. Teams that take the time to define what success looks like with AI agents don’t just adopt the technology; they unlock a system-level shift in how work gets done. They see stronger adoption, clearer roles, and measurable improvements in teaming behaviors.

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Customer spotlight: Wipro’s outcome-driven AI journey

Wipro, a leading AI-powered technology services and consulting company headquartered in India, leverages an Agent Intake and Prioritization Approach as a structured framework for evaluating whether a new agent should be developed. By requiring teams to clearly articulate the agent’s purpose, business value, and intended outcomes in the intake form, this process ensures that every proposal is strategically aligned, technically feasible, and financially justified. Teams are prompted to consider not only what the agent will do, but also how it will impact users, drive measurable improvements, and support broader organizational goals. This clarity at the outset helps avoid duplication, ensures resources are allocated efficiently, and sets a clear benchmark for success.

By investing in this process, teams are better positioned to deliver meaningful solutions, and it fosters a shared understanding across stakeholders, from technical leads to business unit representatives. As a result, agents are developed with a clear value proposition, measurable KPIs, and a direct link to business priorities, which leads to higher adoption, improved productivity, and demonstrable ROI. Ultimately, this approach empowers teams to innovate with confidence, knowing that their efforts are both purposeful and impactful.

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If your team is exploring agentic AI, start by reframing the questions: What outcomes (not just tasks) matter most? How will we measure success beyond productivity? Where do agents fit into our team’s way of working? Consider using these questions in your next team planning session, as these conversations can act as a foundation for clarity, adoption, and impact to truly get the most from this technology.

Stay tuned for our November Research Drop to keep up with what the Microsoft People Science team is learning!  

This month’s Research Drop analyzed 1,800 global employees from the Microsoft People Science Agentic Teaming & Trust Survey from July 2025. Employees were grouped in this analysis based on their favorability for two survey items: “My team is aligned on the outcomes we would want to achieve with the help of an agentic AI (AI Agents)” and “My team understands which tasks are best suited for human judgment versus those that can be supported by agentic AI (AI Agents).”

Special thanks to Wipro for providing their story. Note: all data from this blog is from Microsoft People Science and is not linked to Wipro.

References

¹Mercer. (September 2025). The New Rules of Delivery: An AI-Powered Operating Model for Human-Machine Teaming. HR Tech.

²Capgemini Research Institute. (July 2025). Rise of agentic AI: How trust is the key to human-AI collaboration.

October 2025: Transforming Organizational Data Ingestion in Microsoft 365 & Microsoft Viva

moyoadelakun — Fri, 23 Jan 2026 05:11:16 GMT

Organizational Data in Microsoft 365 is a robust platform that streamlines how admins and data custodians bring HR data into Microsoft 365, Copilot, and Viva. With a single, secure ingestion point, Organizational Data in Microsoft 365 validates data integrity, enables access to shared data services, and governs data usage — empowering organizations to unlock business-critical scenarios with confidence.

This new update to organizational data ingestion in Microsoft 365 marks a significant leap forward for admins seeking to streamline, secure, and maximize the value of their HR data within the Microsoft 365 ecosystem. With new capabilities for attribute access and mapping, expanded connector support, and a unified ingestion experience across Microsoft Viva applications, this update is designed to empower administrators and deliver more personalized, relevant experiences for end users.

What’s New in Organizational Data in Microsoft 365?

Enhanced Attribute Access and Mapping

One of the standout features of this release is the introduction of advanced attribute access and mapping. Administrators now have granular control over which data attributes are ingested and how they are mapped from source systems to Microsoft 365 and Viva destinations. This means organizations can:

Selectively share specific attributes with individual apps, protecting sensitive or custom data.
Expedite the mapping process, reducing manual effort and potential errors.
Ensure that only the right data reaches the right destinations, supporting compliance and privacy needs.

Expanded Connector Availability

Organizational Data in Microsoft 365 now supports direct connectors to leading HRIS systems such as Workday and SAP SuccessFactors, as well as a generic Azure Blob Storage connector. This flexibility allows organizations to:

Ingest data from multiple, distinct sources in parallel, supporting complex environments with diverse data needs.
Automate workflows to align with unique organizational processes.
Upload and map custom attributes, ensuring that even non-standard data can be leveraged across Microsoft 365 and Viva.

Unified Ingestion for Viva Scenarios

A major highlight of this release is the unification of data ingestion across Viva Insights and Viva Glint, in addition to existing support for Viva Profile, Viva Amplify, and Viva Engage. With a single ingestion point, organizations can:

Streamline data flows across all Viva experiences.
Reduce administrative overhead by managing ingestion from one place.
Unlock new scenarios where clean, high-quality data powers advanced analytics and personalized insights.

New Admin Role: Organizational Data Source Admin

To support these new capabilities, a dedicated Organizational Data Source Admin role has been introduced. This role is designed for those responsible for managing data ingestion and ensures that only authorized personnel can configure and oversee these critical processes.

Why Does This Matter?

Organizations are increasingly seeking seamless, automated ways to ingest high-quality HR and organizational data into Microsoft 365, Copilot, and Viva. The demand is for flexibility, control, and security—especially as data privacy regulations tighten and the need for personalized digital experiences grows.

This Organizational Data in Microsoft 365 release directly addresses these needs by:

Allowing flexible, automated ingestion tailored to each organization’s workflow.
Enabling parallel data ingestion from multiple sources, reducing bottlenecks.
Providing fine-grained control over which attributes are shared with which apps, enhancing both security and relevance.
Unifying ingestion for all Viva scenarios, making it easier to deliver consistent, high-impact experiences across the Microsoft ecosystem.

Practical Implications for Organizations

Admins:

Assign the new Organizational Data Source Admin role as needed to ensure proper oversight.
Review the updated documentation to understand all new features and best practices.
Confirm that your organization holds the necessary Viva Insights and Viva Glint licenses to access extended ingestion features.

Compliance:

No new compliance considerations have been identified, but organizations should review their own policies to ensure alignment with internal standards.

Licensing:

No new licenses are required to access the core platform. However, a Viva Insights and Viva Glint license is necessary for extended features.

Key Scenarios Unlocked

With these enhancements, organizations can now:

Seamlessly ingest and map HR data from multiple systems.
Control which apps and services can access imported data, down to the attribute level.
Map external attributes directly to internal reserved fields, ensuring data consistency.
Deliver clean, actionable data to Viva Insights and Viva Glint, powering advanced analytics and employee engagement initiatives.

Supported apps

Microsoft 365 (powering Viva Amplify and Viva Engage)
People Skills
Viva Insights and Copilot Dashboard
Viva Glint

This update delivers unprecedented flexibility, control, and integration capabilities, setting a new standard for organizational data management and paving the way for richer, more personalized digital experiences. If you have questions or need help preparing your organization for these changes, consult the official documentation or reach out to your Microsoft representative for guidance.

Chapter 4: Beyond Tools - 2025 Agentic Teaming & Trust Research Report

Megan_Benzing — Thu, 09 Oct 2025 15:00:00 GMT

Not only are team dynamics changing with the introduction of agentic AI, but our mental models for how we perceive agents are evolving as well. Some AI agents are progressing from simple assistants to direct team collaborators. 82% of leaders believe that AI agents will be viewed as valued contributors and teammates over the next year¹. These teammates can help teams work together more effectively, such as by democratizing expertise and reducing functional boundaries that may have impeded work in the past².

In our research, AI agents were seen as catalysts for team potential. With redesigned team structures and processes on the horizon, the anticipation of their impact is high. 75% of employees believe teams that embrace AI agents will have a competitive advantage and 71% believe AI agents could help their team achieve goals they couldn’t before.

Chapter 4 of our 2025 Agentic Teaming & Trust Research Report dives into human-AI teaming and what it might mean for the future of workplace teams. This chapter explores the concept of agents as teammates and what may be required to prepare for that future state to achieve amplified impact at the team-level.

More than 2 in 3 employees think their team would be better off if they worked with one or more AI agents, so the value proposition is becoming clearer as use cases of agentic AI are more widespread. And it’s not just having agents on your team, but also the idea of them as direct reports. 70% of employees said they were excited to manage a team of agents.

But are teams ready for this change? Only 1 in 6 leaders said they have fully established a new talent strategy that upskills employees to be agent bosses. And readiness for agentic AI integration is evolving beyond individual needs to team-level needs.

Preparing teams to effectively integrate agents into their workflows involves including them in the process of redesigning their ways of working. They need to be open to changing their routines and deepen their understanding of which tasks are not only suited to agentic AI, but also which ones they trust to delegate to agents. And when considering the activities agents might support, it's just as important to push beyond the tasks themselves to focus on the outcomes teams are truly aiming for with agent adoption.

Supporting team readiness helps teams more easily see the impact that agents can have, not only on improved team processes, but also team outcomes. We found that about 45% of the impact that teams get from using agentic AI can be traced back to how ready the team is to work with AI agents and how well they collaborate once they are introduced.

Supporting employees on their journey to human–agent collaboration isn’t just about new tools; it’s about creating the right conditions for success. That means upskilling employees to not just ask questions of agents but to collaborate with them, preparing teams for workflow changes, and fostering a safe space to experiment and learn. Start by assessing your team’s readiness and building a plan that combines skills, structure, and psychological safety, because when people feel equipped and supported, they see greater impact of adopting agents.

Want to explore the full story?

Download the PDF – 2025 Agentic Teaming & Trust Research Report

Catch up on the individual chapters from this report:

Download the PDF - Chapter 1: Ready for Agents

Download the PDF - Chapter 2: The Multiplier Effect

Download the PDF - Chapter 3: Trust 360

Download the PDF - Chapter 4: Beyond Tools

The Agentic Teaming & Trust Study was conducted by the Microsoft People Science team utilizing an Online Panel Vendor, commissioned by Microsoft, with 1,800 full-time employees across nine markets between June 11, 2025 and July 7, 2025. This survey was 12 minutes in length and conducted online. Global results have been aggregated across all responses to provide a total or average. Each sample was representative of business leaders across regions, ages, and industries (i.e., Construction, Financial & Professional Services, Retail, Food, & Beverage, Healthcare, Media & Entertainment, Technology, Transportation, Travel, & Hospitality). Each sample included specific parameters on company size (i.e., organizations with 1,000+ employees) and job level (i.e., business leaders/business decision makers, those in mid- to upper job levels such as C-level executive, VP or director, Manager). The overall sampling error rate is 2.31 percent at the 95 percent level of confidence. Markets surveyed include Brazil, China, France, Germany, India, Japan, Mexico, United Kingdom, and the United States. Findings represent aggregated responses and may not reflect all organizations or industries.

References

¹KPMG. (2025). KPMG AI Quarterly Pulse Survey: From agent experimentation to rapid scale and deployment.

²Dell'Acqua, F., Ayoubi, C., Lifshitz, H., Sadun, R., Mollick, E., Mollick, L., ... & Lakhani, K. (2025). The cybernetic teammate: A field experiment on generative AI reshaping teamwork and expertise (No. w33641). National Bureau of Economic Research.

Chapter 3: Trust 360 – 2025 Agentic Teaming & Trust Research Report

Megan_Benzing — Tue, 07 Oct 2025 15:00:00 GMT

Many AI agents are designed to automate tasks and recommend actions, with employees remaining accountable for decisions. A level of trust with agentic AI is therefore needed to feel confident that the AI agent’s choices will be the right ones. We’re starting to see that exposure to agents is related to trust in agents, as organizations further along in their agentic AI transformation report higher levels of trust in AI agents than those still in the exploration phase¹. The question becomes whether this trust is built off critical thinking or blind trust. And we found that 60% of employees said they are confident enough in AI output that they don’t need to check its accuracy. This brings concerns about overreliance, especially relevant as 56% of leaders report concerns with data quality of AI agents².

Chapter 3 of our 2025 Agentic Teaming & Trust Research Report dives into building trust on a foundation of meta-cognition, where employees reflect on their usage of AI, learning when and how to best leverage the technology. In this chapter, we explore how to combat overreliance of AI through trust, leaning on key organizational factors, such as leadership role modeling, reliable systems, and peer learning/advocacy.

Cultivating enduring trust over blind trust is a dynamic process³, starting from the employee calculating that using AI is worth any risk, then learning to trust the competence and skill of AI, and finally creating a sustained relationship with AI built on trust and history. This process creates a level of trust where employees reflect on when and how to rely on AI systems, including verifying outputs and understanding limitations.

One way to impact trust is through leader involvement. When leaders are deeply engaged in the implementation of agentic AI and are transparent about their experience with adopting agents, it can build employee confidence and critical thinking skills when they go to adopt agentic AI. We see up to a 30-percentage point difference in how much employees not only trust agentic AI, but also how much they reflect on their AI usage behaviors between employees who say their leaders role model effective AI use and those whose leaders don’t.

And once trust is built, it creates impact contagion where a culture of role modeling, openness, and experimentation leads to greater value from integrating agentic AI. Employees with high trust in AI agents are 1.4x as likely to report realized individual and team value of agentic AI, such as improved personal work quality and enhanced team-based knowledge sharing.

As organizations move from exploration to integration, the difference between blind trust and enduring trust becomes critical. By fostering environments where employees reflect on their AI usage and learn from each other, organizations can unlock a reinforcing cycle of trust, impact, and innovation. Agentic AI thrives not just on technical capability, but on the human capacity to think critically, share transparently, and grow together.

Want to explore the full story?

Download the PDF - Chapter 3: Trust 360

Catch up on previous chapters from this report:

Download the PDF - Chapter 1: Ready for Agents

Download the PDF - Chapter 2: The Multiplier Effect

References

¹Capgemini Research Institute. (July 2025). Rise of agentic AI: How trust is the key to human-AI collaboration.

²KPMG. (2025). KPMG AI Quarterly Pulse Survey: From agent experimentation to rapid scale and deployment.

³Yamamoto, M., Xu, S., Kee, K. F., & Li, W. (2025). Testing a dynamic model of trust in AI: How trust develops and affects critical thinking in the American workforce. Journal of Trust Research, 15(1), 12–31.

Chapter 2: The Multiplier Effect - 2025 Agentic Teaming & Trust Research Report

Megan_Benzing — Thu, 02 Oct 2025 15:00:00 GMT

As we strategize for integrating agentic AI into our workflows, it’s important to remember that the goal is to not only drive elevated productivity and performance, but to do so in a way that empowers employees. Deploying AI agents to support employees reach new heights can create positive and sustainable change – enabling a new employee experience. And AI adoption thrives in people-centric environments, as our research shows that high frequency AI users are 1.9x as likely to agree that their organization has people-centric AI practices compared to low frequency AI users.

Chapter 2 of our 2025 Agentic Teaming & Trust Research Report dives into the accelerating value of putting employees at the center of your agentic AI transformation. In this chapter, we explore the positive impact that people-centric transformation has not only on AI adoption, but also AI value and employee experience sentiment.

We found an average 38 percentage-point gap between low and high frequency AI users on the presence of people-centric practices at their organization, such as receiving adequate training in AI, having resources for AI upskilling, and receiving recognition for AI adoption. Rather than treating AI technology as a “flip-the-switch” implementation, these organizations are investing in change management principles and it’s fueling the flywheel of increased adoption and further learning and growth.

We’re also seeing this play out in organizations where there is strong leadership presence in AI transformation. When leaders are explicitly involved in the transformation and openly sharing their experience, it can have a positive impact on the rest of the organization. We found significantly higher agentic AI value perception for employees who reported leadership role modeling effective AI use than employees who reported not having this role modeling.

When leaders actively use AI, it helps employees better understand practical use cases – especially important since 1 in 2 employees report facing moderate to extreme challenges with everyday tasks that shape their work experience. Whether it’s finding the right information, setting goals, sharing feedback, or collaborating with others, these basic activities are unexpectedly difficult. If we are strategic in the way we build and integrate agents, we can ideally reduce this friction for employees, which should free them up to work more efficiently and effectively.

Building the human-AI flywheel can drive sustained change and empowerment. By involving employees in the transformation, they feel involved, considered, and supported, which can increase adoption and impact. Tangible improvements to everyday employee experience can be a game-changer for your employees. As we move forward with AI agent integration, the opportunity is clear: agentic AI can be a catalyst for a more empowered, connected, and resilient workforce.

Want to explore the full story?

Download the PDF - Chapter 2: The Multiplier Effect

Catch up on previous chapters from this report:

Download the PDF - Chapter 1: Ready for Agents

Chapter 1: Ready for Agents - 2025 Agentic Teaming & Trust Research Report

Megan_Benzing — Tue, 30 Sep 2025 15:15:00 GMT

AI agents are becoming increasingly prevalent at work, going beyond generating content to taking action, following processes, and making decisions within set boundaries. Organizations began exploring agentic AI much like they did with generative AI – starting with experimentation. But now, many have moved beyond that early phase, as recent research reported that 90% of organizations have progressed beyond solely experimenting with AI agents¹. And we’re starting to see this impact adoption metrics across industries and job levels – we found that 2 in 3 employees report using agentic AI at least once a week. And this was after being provided with an aided definition and examples to differentiate it from generative AI, showing that this technology is quickly becoming part of everyday workflows.

Chapter 1 of our 2025 Agentic Teaming & Trust Research Report dives into the emergence of agentic AI at work. In this chapter, we explore how momentum and interest is building around AI agents and how we’re starting to see early, multi-dimensional impact. We also focus on what phases organizations are in on their way to having established agentic AI transformation strategies.

Not only are employees beginning to leverage agents more frequently, but momentum around agents is also growing as well. More than 70% of employees see the value of integrating agentic AI at work and are motivated to do so. And for those that are already using agents at work, they are starting to perceive real impact of using them – in greater productivity, decision-making, and work quality. This multi-dimensional impact of agentic AI is starting to show the ways agents can support individual employee work (catch chapter 4 for team-based impact!).

As employees’ knowledge of various AI agents is growing, their preference on what type of agent they want to leverage/manage is getting clearer. 90% of our participants demonstrated a preference for a single type of agent, even though they were able to say they wanted all three types of agents presented. These agent types included a Versatile Agent, a Domain Expert Agent, and an Automated Process Agent, each offering distinct capabilities. But this clarity didn’t drive a “winner” between the agent types – the preferences were split.

As organizations look to transform with agentic AI, understanding employee use cases will be critical. 67% of leaders in our research reported being in the “planning” or “implementing” stage of re-engineering workflows to fully leverage the capabilities of agentic AI and only 16% have fully established talent strategies to help upskill employees to manage, monitor, and collaborate with agentic AI.

When it comes to making agentic AI real, lean into the emerging excitement from your employees to capture the momentum and drive positive change. Make sure every employee – from interns to execs – have the support, resources, and opportunity to integrate agentic AI into their processes. Start by identifying high-impact workflows, investing in upskilling programs, and creating cross-functional agentic AI task forces. Getting everyone on board and making sure transformation is not just top-down will push your organization further into a agentic AI future.

Want to explore the full story?

Download the PDF - Chapter 1: Ready for Agents

References

¹KPMG. (2025). KPMG AI Quarterly Pulse Survey: From agent experimentation to rapid scale and deployment.

Sneak Peek: 2025 Agentic Teaming & Trust Research Report

Megan_Benzing — Thu, 25 Sep 2025 15:22:46 GMT

With the introduction of AI agents to the workplace, we are beginning to blend machine intelligence with human judgment¹, and AI agent teammates are starting to enhance collaboration². AI at work is no longer just about using generative tools for daily tasks, it’s driving a complete rethink of workflows and talent strategies. This summer, the Microsoft People Science team set out to better understand how organizations and employees are adapting to these changes, exploring the dynamics and challenges of integrating AI agents into human teams.

Our latest research, “The 2025 Agentic Teaming & Trust Research Report,” delves into how agentic AI is reshaping how organizations and teams work together. Overall, we found that employees are eager to embrace AI agents, seeing them not just as tools, but as collaborators that can unlock new levels of efficiency and creativity. But most companies are just getting started, and our research shows that building trust and strong role models for AI use is key to success.

Here is a sneak peek at what is to come from this report:

Chapter 1: Ready for agents

Employees are excited about agents – 73% see the value of integrating agentic AI at work. And 90% of employees have a strong preference for different agent capabilities: 34% want a versatile agent, 41% want a domain expert agent, and 38% want an automated process agent.

However, only 1 in 6 leaders report that their organization has fully established transformation strategies, the majority are still in planning or early implementation stages. Leaders are taking a systematic approach to organizational readiness. Across eight readiness activities, 81% of leaders reported they were in the same “stage” for half or more of the activities, such as evaluating which workflows can be automated, augmented, or redesigned with agentic AI. This suggests agentic AI transformation may look more stage-based, rather than activity-based.

Chapter 2: The multiplier effect

AI adoption is higher in companies that adopt people centric practices, such as recognizing employees for adopting AI. Employees who see their leaders role model effective AI use report up to 17-percentage points more agentic AI value than employees who don’t.

Still, 1 in 2 employees report facing moderate to extreme difficulty in navigating daily work activities related to their employee experience, struggling to navigate basic activities, such finding information, setting goals, sharing feedback, and collaborating. Things that should be simple are feeling more challenging and complex. With rising productivity pressures, agentic AI offers a chance to reduce friction and reimagine how employees use their time and define productivity.

Chapter 3: Trust 360

AI trust is growing as 2 in 3 employees reported trusting AI agents. However, 60% of employees reported feeling confident enough in the output from AI that they don’t need to check its accuracy and 1 in 3 employees have experimented with AI tools from outside their company in the past 6 months. This combination of blind trust and shadow AI can create complicated situations where human judgement is more important than ever but challenging to enforce. It becomes critical to prioritize training on AI security and critical thinking when it comes to adopting AI agents.

Enduring trust can grow when leaders role model AI, with up to 30-percentage point differences in trust and critical thinking between employees who have leadership role modeling and those who do not. It also requires trustworthy systems that are reliable, clear, and accountable, indicated by employees as top system drivers of trust.

Chapter 4: Beyond tools

AI agents are seen as team catalysts: 71% of employees report agents will help them better achieve team goals. But only 16% of leaders report having established a talent strategy that upskills employees to be agent bosses.

Agent readiness requires a mindset shift – viewing agents as teammates over tools. High “agent ready” teams that have aligned on what activities, skills, and outcomes they want to achieve with AI agents reported that agentic AI improved knowledge sharing, goal alignment, and problem-solving on their teams – leading to better efficiency and output quality. We found that about 45% of the impact that teams get from using agentic AI can be traced back to how ready the team is to work with AI agents and how well they collaborate once they are introduced. To prepare, help teams identify which tasks are best suited for agents versus humans, align on desired outcomes, and upskill employees to work effectively with agents. Create a safe environment for experimentation with new team structures and processes.

Together, these findings illustrate how agentic AI is already reshaping the employee experience by streamlining daily tasks, fostering new forms of collaboration, and driving measurable improvements in team performance. We’re starting to see employees’ mindsets shift toward managing agents and organizations start to strategize on re-engineering job roles and responsibilities for a world where Frontier Firms aren’t just adopting AI, they are redefining what it means to lead, compete, and thrive in the age of digital labor. This is just the tip of the iceberg in terms of rich insights we’ll share in “The 2025 Agentic Teaming & Trust Research Report,” so keep a lookout for the chapters to be released soon on the Microsoft Viva Community Blog.

References

¹Microsoft Work Trend Index Annual Report. 2025: The Year the Frontier Firm is Born. April 23, 2025.

New Resource: Getting Started with Custom Analysis in Viva Insights and Copilot Analytics

martin_chan — Mon, 22 Sep 2025 15:00:00 GMT

Last month, we introduced an exciting new resource to help organizations unlock deeper insights with Microsoft 365 Copilot: the “Getting Started with Custom Analysis in Copilot Analytics” playbook. This guide is now publicly available as part of the Copilot Success Kit under the User Enablement section.

Why This Playbook Matters

As more organizations adopt and utilize Microsoft 365 Copilot, many are asking:How do we measure success? How can we go beyond the default dashboards and Power BI reports to answer deeper questions? And how do we go about planning and setting up a measurement program?

This playbook was created in response to feedback from customers and partners who wanted practical guidance on the deployment and strategy side of Copilot Analytics. It complements the previously released Advanced Examples playbook, which focuses on interpreting analytics once a program is in place. The new guide takes a step back to help you build a custom analysis strategy from the ground up.

What You’ll Learn

The playbook offers a structured, step-by-step approach for analytics leaders, change management teams, and Copilot champions. Key topics include:

Planning a Copilot Analytics measurement program aligned to your organization's goals
Evaluating out-of-the-box tools
Setting up Viva Insights for custom analysis, including role assignments and data preparation
Running queries and building an analysis plan
Analyzing results using R or Python

Whether you’re just starting your Copilot journey or looking to scale your analytics capabilities, this resource provides the tools and methodology to measure adoption impact, identify barriers, and drive success.

Explore the Playbook

Access the playbook here:
👉 https://adoption.microsoft.com/files/copilot/GettingStartedWithCustomAnalysisInCopilotAnalytics.pptx

What’s Next

To help you get the most out of this resource, we’ll be rolling out:

A series of blog posts and webinars showcasing how to use the playbook effectively
Real-world success stories and advanced analysis techniques
Additional materials such as analysis templates, code examples, and visualization guides to support data scientists and analysts

Start your custom analysis journey today and take your Copilot Analytics strategy to the next level.

Empowerment Unlocked: Key Conditions for Thriving Teams

craigramsay — Wed, 10 Sep 2025 15:00:00 GMT

In the second blog of Microsoft People Science team’s People Success Series, we shared the importance of employee trust in leadership. This 3^rd edition “flips the script” and examines the importance of leadership trust in employees: a cultural foundation to empowerment.

With rapid technological changes, increased pressures to reskill, shifting workplace policies, and staff restructuring, certainty about how and where one does their job can be a challenge for many employees. Recent research found that job insecurity and workplace uncertainty significantly impact workers’ stress levels, emotional exhaustion, and motivation. Workers affected by constant, overlapping changes reported higher levels of disorientation and confusion, which correlated with reduced performance and engagement¹.

But at the same time, today’s fast-changing business landscape requires organizations to respond quickly to challenges and opportunities and employees to be agile and resilient. So how can people be more empowered to take risks and make decisions in a work environment that feels less certain and more stressful?

In 2023, our research on the PS Elements found 9 critical needs associated with Empowerment, such as Decision Transparency, Risk-Taking, and Work Ownership. Our recent research on employee engagement and attrition drivers between 2024-2025² revealed five empowerment items with the strongest associations with engagement and attrition.

Empowerment Top Five Drivers of Engagement and Attrition
Empowerment Item	Item Text	Attrition Multiplier^*	Pearson r (correlation) value^**
Empowerment Item	Item Text	Attrition Multiplier^*	eSat	Recommend
Job Resources	I have the resources I need to do my job well	1.94	.76	.76
Barriers to Execution	At <COMPANY_NAME> we do a good job removing barriers that slow down our work	2.28	.71	.70
Continuous Improvement	<COMPANY_NAME> continually improves the way work gets done	2.12	.74	.76
Job Empowerment	I feel empowered to make decisions regarding my work	2.79	.65	.63
Initiative	I am encouraged to find new and better ways to get things done	2.67	.81	.78

* from the 2025 attrition multiplier analysis: figures represent how many times more likely employees are to stay if these conditions are met².
** from the 2025 engagement key driver analysis: displaying the Pearson r correlation value between the empowerment item and engagement outcomes².

Today, we’ll take a deeper look into each of these empowerment factors and provide data-backed recommendations on how to improve the conditions for empowerment.

Job resources: critical to meeting today’s work demands

We define Job Resources as “resources (time, people, tools, information, knowledge) available when people need them to get their work done.” Our research shows there’s a strong (with Pearson r values = .76) correlation between having the resources to succeed and eSat: (How happy are you working at <COMPANY_NAME>?) and Recommend (I would recommend <COMPANY_NAME> as a great place to work)².

With the many changes happening in today’s work world, employees are under pressure to keep up. Providing sufficient time and resources helps to mitigate feelings of stress. And if not provided adequate resources, our data shows people are twice as likely to leave than those who are².

Calls to action:

Encourage managers to read employee survey comments and have conversations with direct reports about what they need and why. Do they have access to networking opportunities? Do they have access to technology and the training to use it? Do they have sufficient time to adjust to changes?
Take an expansive perspective on overall work demands and resources to make sure that employees feel the full support of the organization. This, in turn, will help employees develop psychological capital and trust in leadership.
Help employees use Copilot prompts and develop job-specific agents that speed up routine tasks.

Barriers to execution: when slow processes get in the way of timely performance

A critical component of empowerment is the ability to “carry out one’s duties and responsibilities without running into roadblocks and with minimal delays.” People expect to see company actions to remove barriers and install efficiencies that make it easier to execute the work. Employees who see their management removing obstacles are more than 2 times as likely to stay with the company than those who don’t². Our data shows a dramatic increase (more than 15% in Pearson r value YoY) in the correlation between removing barriers and engagement outcomes (eSat and Recommend)². This signals an increasing need for task efficiency and predictability in an environment where work processes and roles are quickly evolving.

Calls to action:

While some process roadblocks are systemic and require company support to fix, many barriers to action are entirely addressable by individual workers and small teams (e.g., testing small improvements in their workflow before asking for changes). Give employees the resources and authority to safely mitigate problems at their job level and within their scope without asking for permission.
Open up feedback channels so that employees can easily report obstacles that get in their way. Then involve them in developing solutions and making changes to streamline and improve workflows.

Continuous improvement: a mindset for lasting success

We recognize continuous improvement when companies put effort and resources into ongoing improvements that help people successfully get their work done and produce better quality outcomes. It is an attitude and approach where individuals, teams, and organizations are committed to consistently seeking ways to improve processes, products, skills, and outcomes over time. The continuous improvement mindset is rooted in the belief that there’s always room for growth and optimization, no matter how well things are currently working.

Our stats show that continuous improvement is a strong driver of eSat and Recommend (with Pearson r values of .74 and .76 respectively)². Changes in technology (e.g., AI), office location (e.g., hybrid/remote workplace), and work policies (e.g., asynchronous “always on” work) are swiftly transforming the way work gets done, but without proper process reengineering, these changes can feel disruptive and create frustration among workers. Both barriers to execution and continuous improvement call for streamlining work to reduce friction and are highly associated with people feeling well-supported by the company.

Calls to action:

Organizations can better support their managers to help their teams operate more efficiently and successfully by providing them with more decision-making authority and/or better tools to make improvements upon local team feedback.
Help teams surface improvement ideas through connections to other departments across the organization. Managers can guide their teams on how to grow their informal networks and use them for sharing learnings, innovation, and improvement.
When individual contributors face inefficiency at work, encourage them to bring the issues to their manager so it can be escalated. Leaders may not always have insight into the day-to-day challenges, so it’s up to employees to share their experiences to drive improvement.

Job empowerment: a matter of trust from leadership

Beyond efficiently getting the job done, empowerment also means being trusted to approach one’s work with ownership, flexibility, and autonomy. This is where leadership trust in employees will make or break feelings of empowerment.

Employees want an optimal level of autonomy and involvement in decision making to perform their work without having to check in first – and without a lot of monitoring. In fact, employees are willing to leave if they experience too much micromanagement. Our research found that employees who have job empowerment decision making authority are 2.8 times less likely to leave than those who don’t².

Post-pandemic, hybrid workers have mostly proven they can be productive with less direct supervision, which has fundamentally reshaped their expectations around control and trust. Yet some leaders still don't agree, and continue to enforce strict mandates that harm retention, especially of the most vulnerable and marginalized groups (women, people with disabilities, those who cannot afford the commute and time away from caregiving responsibilities). In fact, employees from underrepresented groups are 22% more likely to consider leaving if flexibility is withdrawn³. And caregiving employees (73% of U.S. workforce) are 30% more likely to quit if their ability to manage care is compromised by return to office directives⁴.

Calls to action:

When managers micromanage (continuously checking up on employee progress), or worse yet, monitoring activities (e.g., tracking keyboard or camera actions), it can feel as though they lack trust which can lead to an environment of fear that hinders innovation and growth. Instead, encourage managers to check in on the status of the work tasks and use those moments as an opportunity to provide feedback and clarity which provide the necessary context for the team to plan and direct their own performance.
Empowerment thrives when employees know they won’t be punished for thoughtful risks. Help managers foster a team environment that normalizes learning from mistakes where people discuss what worked and what didn’t without blame. Use language like “experiments” or “pilots” to frame decisions as opportunities for learning. Back employees publicly when they make decisions in good faith.

Initiative: a path to fearless innovation

People want the self-determination to be creative, take risks, and try new ways of approaching their tasks that bring joy to their work, improve their outcomes… and get the job done. In fact, taking Initiative scores highest among empowerment items as a very strong driver (with Pearson r values greater than .78) of eSat and Recommend². Correlation values have risen 15% YoY indicating an increasing importance of the autonomy, space, and time to experiment with new work practices².

Today, workers welcome new technologies (like AI) that reduce repetitiveness and allow time for more complex strategic activities, leading them to seek greater control over their new, redefined roles⁵. Embracing new tools and processes requires a curious mindset that is open to experimentation and to discovering better ways of working. This mindset is fostered when people can experiment without the fear of negative consequences for failure. Taking risks requires a climate of psychological safety to challenge the status quo.

Calls to action:

Managers can encourage professional growth and risk taking by decreasing the weight assigned to goals based on expected outcomes and increasing the weight assigned to goals that encompass risk taking and learning. Encourage direct reports to challenge themselves by seeking responsibilities and learning opportunities that will take them in new directions. Recognize those who take risks and make independent decisions.
Managers can model risk taking for their teams by sharing examples from their own work that demonstrate the value of creative approaches and innovative ideas. Share successes as well as misses, demonstrating that it is safe to fail.
Managers can create a climate where it's safe to take risks by interacting with employees in ways that encourage alternative perspectives and approaches. Be sure to convey respect and consideration in verbal and nonverbal responses to innovative ideas. Ask questions designed to reveal and develop divergent thinking. Show an openness to new ideas that come from across departmental lines.

Conclusion

With today’s rapid workplace changes, empowering employees is essential for boosting engagement and retention. Empowerment grows with trust from leadership which enables employees to take risks and make decisions more confidently. When organizations provide job support and resources and actively promote self-determination, employees feel valued and motivated to contribute their best, benefiting both themselves and the company.

Is employee empowerment where it needs to be for your organization to thrive?
Think about which of the following survey questions is most critical for your organization's goals and consider including them on your next survey.

References

¹ American Psychological Association. (2025). Work in America™ Survey. Majority of U.S. workers say job insecurity has significant impact on their stress.

² The 2025 engagement and attrition key driver refresh and longitudinal analysis determined the top predictors of engagement and voluntary attrition (across 235 customers) based on LinkedIn Glint survey scores in 2022, 2023, and 2025 representing 6.8 million employee survey responses.

Engagement: As these data variables are continuous, Pearson's r was used to measure the correlation strength of the linear relationship between People Success “critical needs” (25 survey items that measure employee experiences aligned with the six People Success Elements) and the six engagement outcome items (eSat, Recommend, Manager, Belonging, Retention and Intent to Stay). The r values for each item were aggregated and averaged across all customers for each year reported.

To calculate meaningful change from year to year, we examined the delta in r value for each item-outcome pair over the 3-year period. Then, we calculated the correlation percentile splits to determine the top 33 percent (r value YoY deltas from .10 up to .25) and bottom 33% (r value YoY deltas below .05). We comment here only on those items and their outcomes with r value deltas in the top third, noted as “top predictors” or “strong drivers” of the outcome(s) and whether the change is trending up or down.

Attrition: Only survey item responses from at least 15 LinkedIn Glint customers who’ve had at least 80 voluntary terminations between survey administrations were included in this study. “Attrition Multipliers” were formed by comparing favorable vs. unfavorable survey responses scores from those voluntary termed employees for each of the People Success “critical needs” items. Aggregate analysis involved averaging the attrition multipliers per item at a customer level, filtered by n size for each of the three years in this longitudinal study.

³ The Hill. (Aug, 2024). Employers used return-to-office to make workers quit. Then this happened.

⁴ Upwards. (Nov, 2024). The Hidden Key to Successful RTO: Supporting Your Caregiving Employees.

⁵ Stanford Report. (2025, July 7). What workers really want from AI.

Rebuilding Trust in the Workplace Before It’s Too Late

craigramsay — Mon, 21 Jul 2025 15:00:00 GMT

In this second blog in the People Success Elements series, we continue to dive deeper into the modern work experience and let survey responses from over 6.8M employees across more than 235 companies from 2022 – 2025 reveal why worker trust has waned and – importantly – how leaders can earn it back.

In today’s workplace, even amid strong financials, many companies are navigating difficult decisions like layoffs and policy shifts—often without clear communication. The challenges faced by company leadership today are daunting , and stakeholder confidence in executive teams’ ability to handle them is reportedly at an all-time low¹. Only 24% of employees say they trust senior leadership¹.

The Disconnect Between Words and Actions

The most perplexing part of current trust challenges? It’s unfolding during a time of economic growth. Traditionally, trust dips during recessions or scandals. But today, the stock market has hit all-time highs, and there’s a labor shortage, yet workers still face layoffs and abrupt policy shifts². That contradiction can significantly strain employee trust and morale.

Across nearly all industries, the layoffs haven’t spared any sector². When people perceive their employer is not being fully transparent, they are left confused and anxious, which only deepens mistrust.

Return-to-office policy shifts, especially when implemented without employee input, have become a flashpoint for internal tension—highlighting the importance of inclusive decision-making. Nearly 75% of executives admit these mandates have caused internal conflict².

Moments like this break what’s called the “psychological contract,” an implicit understanding that employees have about what they owe the company and what the company owes them. Psychological contracts are tacit, not just unspoken but unconscious, and not clearly articulated in people’s minds until the moment they are broken³. Breaches can be driven by organizational factors such as low initial trust, employer reneging, value misalignment between employee and organization, organizational change, lack of support or fairness, and internal politics. Positive organizational practices can reduce breach perceptions, while negative actions increase them³.

To cultivate a healthy relationship, today’s workforce requires a human-centric approach which fosters a deeper understanding of personal needs such that people care enough about their job and company to put in extra effort, thought, and creativity; to be proactive; to engage in extra-role behaviors⁴.

The Perception of Leadership

Recent research found that people across the board – from boards of directors to frontline employees – doubt that executive teams can juggle today’s disruptions and still put the enterprise’s interests above all else¹. Employees may perceive leadership as disconnected or overwhelmed by today’s challenges. And to be fair, leaders have been dealing with “precedent-setting” disruptions lately, from global crises to rapid tech changes, which means they’re often trekking through uncharted territory¹.

Having confidence is key to trust. People need to believe their leaders have the experience and capability to handle challenges and keep the company on track.

Further, Confidence in Leadership and Company Prospects (I am excited about <COMPANY_NAME>'s future) have a very strong correlation (with Pearson r values > .84)⁵, meaning that when employee confidence in leadership is low, so is their excitement, further eroding their loyalty to the company.

No doubt, the past few years have been challenging with pandemics, social and political upheaval, AI acceleration, and more. Even strong leaders are flying without a playbook. But employees aren’t looking for perfection. They’re looking for humane treatment, honesty, and respect¹.

When decisions appear inconsistent and messaging feels out of touch, employee confidence in leadership’s priorities and alignment begins to erode.

And here’s where leadership’s opportunity begins.

Rebuilding Trust: How Leaders Can Get It Back

So, how do you rebuild something as fragile and essential as trust? Here are four research-backed strategies that start at the top and ripple throughout the organization:

Radical Transparency

People don’t expect you to have all the answers, but they do expect the truth through honest, two-way communication. That means holding regular town halls, offering open Q&A sessions, and addressing hard questions head-on—especially around layoffs and restructuring, big policy changes or tech rollouts.

Our recent data highlights the importance of transparency. Trust in Decision Making (Overall, I am satisfied with how decisions are made at <COMPANY_NAME>) is the strongest correlate (with a Pearson r of .91) of Confidence in Leadership⁵. In other words, lack of clear explanations has a strong, negative effect on trust in leadership.

Think of transparency like internal “forward guidance.” Just like companies signal forecasts to investors, leadership can communicate potential changes early, honestly, and with context. Being upfront with upsetting news is rarely easy or comfortable, but people appreciate being told rather than being surprised.

Visible Accountability and Fairness

Nothing erodes trust like “rules for thee, not for me.” To rebuild trust, leaders must hold themselves accountable just as they do others.

One bold idea emerging is pay transparency at the top, making executive compensation public and clearly linked to performance². Employees aren’t naïve; they notice when a company is belt-tightening for everyone except the C-suite. Disparities in compensation during times of cost-cutting can impact perceptions of fairness and trust².

Our 2025 findings show that the correlation between Compensation Rewards (I am fairly compensated for the work that I do) and eSat has grown substantially (from a Pearson r of .68 in 2022 to .83 in 2025)⁵. Job security concerns may be making fair pay a more crucial factor for engagement.

Fairness is often best demonstrated when leadership strives to hold itself accountable to the same values that are expected from staff.

If “people first” is a core value, maintaining consistency in programs that advance equity and inclusion is essential. Quietly scaling back such efforts can create misalignment between mission and execution, undermining credibility. Recent findings show that Diversity Commitment (Top leaders demonstrate a visible commitment to diversity) has seen an strong uptick (from a Pearson r of .58 in 2022 to .72 in 2025) in strength as a driver of eSat⁵. People expect to be treated fairly, and this becomes increasingly essential in an ever more diverse work world.

Believing there are Equal Opportunities (Regardless of background, everyone at <COMPANY_NAME> has an equal opportunity to succeed) also had a strong increase (from a Pearson r of .58 in 2022 to .72 in 2025) as a predictor of eSat⁵. When employees feel the playing field is level, they’re far more likely to trust leadership’s intentions⁶.

Co-Creation Over Command-and-Control

Here’s one of the most underutilized tools for restoring trust: involvement.

Employees have great ideas, and high motivation to participate in decisions that affect them especially around hot-button issues like remote work, wellness benefits, and tech tools. Our data shows that when people have Job Empowerment (I feel empowered to make decisions regarding my work) they are 2.8x less likely to leave than those who don’t⁵. So why not ask?

The return-to-office transition has shown how top-down decisions, even when well-intentioned, can create unintended challenges when employee perspectives aren’t fully considered. Nearly three-quarters of executives admit strict mandates have created internal conflict². RTO is a prime opportunity to build trust by valuing employee input. Whether it’s surveys, cross-functional advisory panels, or policy pilots with built-in feedback loops, employee involvement fosters buy-in.

Consistency and Unified Messaging

This one’s deceptively simple but mission-critical: say what you mean and do what you say.

When employees hear an executive saying one thing, and another contradicts it, or see decisions being reversed soon after they’re announced, the back-and-forth can chip away at credibility.

The fix? Align messaging across leadership. Push back when mixed signals arise. Advocate for sticking to decisions unless new input justifies a change—and when that happens, explain why. Our data shows that Communication Flow (There is a good flow of communication between leadership, departments, and teams) is a strong driver of Confidence in Leadership (with a Pearson r of .74) and eSat (with a Pearson r of .69)⁵. Key messages should be transparent and reliable through every level of management and across the organization.

Of course, there’s a balance between communicating decisions early even if they might change versus waiting until decisions are final. But there are no trade-offs for aligning communication across the organization. Consistency over time builds trust.

Don’t Underestimate Empathy and Humility

While powerful, these four strategies may fall flat without the right tone. To be successful each of these strategies must be carefully planned and executed with compassion, humility and authenticity.

Preserving the psychological contract comes down to showing that you truly care. Our data show that Care (At work, I feel cared about as a person) has been a steady driver since 2022 of eSat and Recommend (with Pearson r values of .83 and .78 respectively)⁵. Showing you care starts with empathy, and empathy starts with listening. We found that people who have Inclusive Leaders (Leaders at <COMPANY_NAME> value different perspectives) are 2.8x less likely to leave than those who don’t⁵.

When employees raise concerns about workplace changes, like Return-to-Office mandates or AI adoption, don’t just “acknowledge” them, find ways to address concerns. Empathy is saying, “I hear you, and here’s what we’re doing with your feedback.” Feeling well supported by leadership, Company Support (I feel well supported by <COMPANY_NAME> at this time) has a meaningful relationship with Confidence in Leadership (with a Pearson r value of .86) and eSat (with a Pearson r value of .87)⁵. It goes beyond feeling heard – it’s about seeing action.

And humility? That’s admitting when leadership gets it wrong, by owning past missteps and using them as learning moments, e.g., “We tried X and it didn’t work—here’s what we’re doing differently.”

Employees don’t expect flawless leaders in turbulent times, but they do expect honest ones¹.

The Long Game: Why Trust Matters Now More Than Ever

The truth is, once trust is broken, it doesn’t bounce back overnight.

But it can be rebuilt—deliberately, consistently, and humanely. So, whether you’re navigating reorgs and layoffs, launching AI, reworking hybrid policies, or redefining values, remember this: Trust isn’t restored through a memo. It’s rebuilt one conversation, one decision, one act of empathy at a time.

And in a world that’s changing faster than ever, trusted leadership is a necessity.

References

¹Harvard Business Review. (April 28, 2025). Executive Teams Are Losing Stakeholders’ Confidence. Here’s How to Get It Back.

²Forbes. (April 18, 2025). The Trust Crisis: Why Employees No Longer Believe Their Leaders (And How To Rebuild It.

³ Annual Review of Organizational Psychology and Organizational Behavior. (Volume 6, 2019). Psychological Contracts: Past, Present, and Future.

⁴Harvard Business Review. (May 6, 2025). The Workplace Psychological Contract Is Broken. Here’s How to Fix It.

⁵The 2025 engagement and attrition key driver refresh and longitudinal analysis determined the top predictors of engagement (across 235 customers) and voluntary attrition based on LinkedIn Glint survey scores in 2022, 2023, and 2025 representing 6.8 million employee survey responses.

⁶ Harvard Business Review. (March 21, 2025). Employees Won’t Trust AI If They Don’t Trust Their Leaders.

It’s a Brave New Work World: Reimagining People Success in the Age of AI

craigramsay — Wed, 02 Jul 2025 03:03:39 GMT

Welcome to the first blog in the Microsoft People Science team’s People Success Summer Series where we explore how workplace experiences that drive employee engagement have changed. For this series, we analyzed survey responses from over 6.8M employees across more than 235 companies from 2022 – 2025 to examine how workers’ expectations have evolved over the past three years and identified the key drivers of engagement and the top predictors of retention today.

While what employees want from work remains stable, and our engagement benchmarks have been consistent since 2021, how people work—where, when, why, and with whom—is undergoing a profound transformation. AI tools like Microsoft Copilot are reshaping the employee experience, and organizations must adapt to amplify human potential through people-centric strategies.

What is “People Success”?

Since the beginning of the decade, People Science has studied this concept we call “People Success,” an employee-centric environment composed of psychological, emotional and experiential conditions where employees can bring their best selves to work to do their best work. Since we first blogged about this in early 2023, a lot has changed, and it’s time to revisit People Success in light of significant advancements in workplace practices.

The Six People Success Elements

Based on over 350M employee survey responses from 2020 – 2022, our six-factor People Success Elements model (below) provides a lens through which organizations can assess, understand, and act upon employee feedback to improve the experience of their people in a more humanistic, modern-day work world.

How People Success drives critical engagement outcomes

In order to be motivated to do their best and be fully engaged, people expect a workplace culture and management practices that provide Purpose, Growth, Clarity, Empowerment, Connection and Wellbeing. By addressing the workplace conditions and employee needs in the six People Success Elements, our research¹ shows a clear impact on a retention. This chart shows some of the top reasons that people choose to stay with their employer:

Enabling People Success in an AI-driven Workplace

From a business standpoint, being customer-centric and building solutions people need, all while creating business impact, will remain important for decades to come². With this constancy of business purpose, we forecast that fundamental human workplace needs will stay fairly consistent as well. However, how people get their work done will continue to evolve over the next decade, bearing little resemblance to work as it stands today³.

As AI tools like Microsoft Copilot become embedded in the modern workplace, organizations will experience a transformation that will fundamentally reshape how employees experience their work and how organizations fundamentally operate. Given the momentous advancements for getting work done with higher quality and efficiency, a transformation of this magnitude elicits mixed emotions for workers. This underscores the need for organizations to be proactive and understand how to optimize the impact of AI on their business and the employee experience. Our research shows that a people-centric change management approach that brings employees along the journey is key to successful AI implementation.

Through the lens of the People Success Elements, let’s look at major trends drawing from a People Science longitudinal analysis examining millions of employee survey responses and voluntary attrition patterns over a three-year period¹, and dig into how AI transformation is shaping a brave new work world.

Finding Purpose: The Impact of Meaningful Work

People strive to live a life of meaning and self-actualization.

Since 2022, Confidence in Leadership (I have confidence in the leadership team) is a consistent top-ten driver of employee engagement as measured by eSat: (How happy are you working at <COMPANY_NAME>?) and Recommend (I would recommend <COMPANY_NAME> as a great place to work)¹. People want to believe their employer is managed well.

Our research also shows that Meaningful Work (The work that I do at <COMPANY_NAME> is meaningful to me.) is the #1 predictor of voluntary attrition since 2022¹. When people feel they are doing meaningful work, they are 3.8x less likely to leave than those who don’t¹.

With time freed up by AI, people can be more focused on work that is high-impact, forward-looking, and aligned with long-term goals. This type of meaningful work can help them feel they’re contributing more directly to business outcomes, reinforcing their sense of value and purpose.

People Science Tip
“Encourage people to use the time freed up by AI to focus on higher value work that provides a greater sense of meaning and fulfillment, instead of additional menial tasks that can (and should) be outsourced to AI.”

Driving Growth: Skills Development in the AI Era

Today, skills matter more than tenure², making continuous learning essential for the worker and company. Most employees don’t have the time or capacity for traditional, broad-based training programs and need more targeted, just-in-time learning⁵. People expect to learn new skills on the job that are pertinent to their current duties while also helping them prepare for the future.

Asked on surveys by 75% of Glint customers, Opportunities to Learn and Grow (I have good opportunities to learn and grow at <COMPANY_NAME>) is a very strong (with Pearson r values greater than 0.78) driver of Retention (I rarely think about looking for a job at a different company) and Recommend (I would recommend <COMPANY_NAME> as a great place to work)¹. Those who say they have good opportunities to learn and grow are 2.8x less likely to voluntarily leave than those who don’t¹.

People Science research shows that a people-centric approach to AI adoption is key to success - prioritizing resources (available and easy to use learning), time to experiment and empathy (the safety to try and fail), and support (access to SMEs who can coach them and managers they can seek guidance from). The cool thing is, using AI can function as a real-time mentor, offering support, feedback and suggestions that assist in continuous learning⁵.

Skills growth helps enhance career possibilities. Perceiving Good Career Opportunities (I have good career opportunities at <COMPANY_NAME>) is consistently a top driver (with Pearson r values greater than 0.78) of eSat and Recommend for the past three years¹. With AI technology advancing faster than many organizations can manage, employees who are open to learning AI and have the proper support may develop these new skills, enhance their value to the organization, and help to build future employment prospects.

People Science Tip
“AI will transform work, so it’ll require fundamentally new ways of thinking, and continuous learning of unfamiliar skills. Create adequate time and space for people to experiment, try, fail, and learn without fear of negative consequences.”

Clarity and Feedback: Essential for Employee Retention

People naturally want to perform well. They seek to understand the vision of success for the organization, what is expected of them individually, and how they should spend their time, talent, and energy to reach their goals. Clarifying priorities, such as focusing on the 20% of work that delivers 80% of the outcomes, assists with task completion in an environment with frequent interruptions⁶.

Clarity is created by helping employees understand role expectations, knowing their priorities, and getting meaningful performance feedback from their manager - all three factors are historically solid predictors of voluntary attrition (with multipliers greater than 2.5)¹. When any one of these factors is missing, it may become so frustrating for employees that it could cause them to leave. Clarity is also a critical factor to help people navigate organizational changes. This Support (I feel supported in my efforts to adapt to organizational changes.) is a strong (with Pearson r values greater than 0.70) driver of eSat and Recommend.

Performance Feedback (<MY_MANAGER> provides me with feedback that helps me improve my performance) was included on surveys by 70% of Glint's customers in 2024¹, and for the past three years, it’s been the #1 driver of satisfaction with one’s manager¹, underscoring the importance of direct manager coaching to ensure clarity of role and priorities. While AI can streamline work and even provide a certain level of coaching, it cannot (yet) replace the very human manager-direct report experience required for high quality performance feedback.

With workers interrupted every 2–3 minutes and 80% reporting a lack of time or energy to do their jobs effectively⁴, clarity is more critical than ever. This all suggests that while the volume of information has increased, the ability to manage it efficiently hasn't kept pace.

More than ever, to be successful and not burn out, workers need clarity about what is most important and what to pay attention to. And those who say they know what they should be focused on are 2.7x less likely to leave than those who don’t know¹.

People Science Tip
“While AI can help assist with identifying priorities (for example, scanning communications to identify goals, important decisions, and task assignments), the human interaction of performance feedback remains a critical responsibility for managers and a necessarily skill to develop.”

Empowerment in Decision-Making: Key to Employee Engagement

People want to be trusted to get their job done with as much freedom, efficiency, and autonomy as possible. They want the flexibility, resources, and decision-making authority to perform their work independently.

Key empowerment factors that have historically been strong drivers of eSat and Recommend (with Pearson r values greater than .70) are Continuous Process Improvements (<COMPANY_NAME> continually improves the way work gets done) and Resource Availability (I have the resources I need to do my job well)¹. Since last year, Encouraged to Try New Ways (I am encouraged to find new and better ways to get things done.) has become a very strong (with Pearson r values greater than 0.78) driver of eSat, Recommend and Belonging outcomes.

With an AI ability to learn and improve, employees will see process improvements and big productivity gains as this “personal assistant” takes on administrative tasks and frees them to focus more on work that involves creativity, problem-solving and decision-making⁷.

Using AI agents democratizes expertise that was once siloed within specific individuals⁸. With direct access to specialized knowledge whenever it’s needed, regardless of hierarchy or the boundaries of job functions, employees are empowered to independently solve problems, handle more complex cognitive work, and increase their expertise.

When employees can more independently solve problems and deliver better work outcomes they are 2.8x less likely to leave than those who don’t¹.

People Science Tip
“One key to reaching full AI empowerment is to enable users with job-specific use cases, scenarios and prompts that can be applied to tasks that are known to most benefit from AI assistance.”

Strengthening Connection: AI's Impact on Collaboration

People want a sense of worth and identity within their groups. They want the comfort and freedom to express themselves as individuals, entirely and fully, and to be accepted for who they truly are. They want to be part of a collaborative team of people who genuinely like and support each other.

Connection at work is fostered by a sense of inclusion, strong collaboration, feeling valued and having a sense of belonging. As hybrid and remote work have become normalized, those working remotely or on geographically dispersed teams can feel less connected to their peers⁹.

For three years, Belonging (I feel a sense of belonging at <COMPANY_NAME>) has consistently ranked as one of the top five drivers of Retention (I rarely think about looking for a job at a different company) and voluntary attrition making this single factor a cornerstone to people’s desire to stay¹. No wonder it is asked in surveys by over 70% of Glint customers¹.

AI can enhance inclusion by ensuring that all team members can participate effectively, regardless of their working style or communication preferences by streamlining communications and surfacing diverse voices in meetings (e.g., via transcription and summarization tools)¹⁰ and by supporting neurodiverse employees with adaptive technologies¹¹.

People Science Tip
“Copilot can be a powerful ally in fostering workplace inclusion by supporting employees with communication challenges. From summarizing meetings for those with hearing loss to easing email drafting for individuals who are non-native speakers, Copilot helps reduce stress and boost confidence—making work more accessible and empowering.”

AI Enhances Wellbeing: Predicting burnout early

People need to feel safe, and treated with respect, fairness, and equity in all manners of their work life. They desire the flexibility to be where they are needed most to successfully take care of work obligations and home life, and to provide security and peace of mind for themselves and their loved ones.

Critical to Wellbeing is to Feel Cared About at Work (At work, I feel cared about as a person), Feel Supported (I feel well supported by <COMPANY_NAME> at this time.) and the ability to Balance Work and Life (I am able to successfully balance my work and personal life). These key factors have increased in power as drivers of engagement and predictors of voluntary attrition since 2023¹. With ever more chaotic changes impacting work life, job security and mental health, people are looking for an environment of economic security, psychological safety, and a human-centric work experience.

Since 2024, Work Life Balance has seen the largest uptick (14%) across all factors as a predictor of voluntary attrition¹. This is likely the effect of increased burnout signals over the past few years driven by the shift from employee-centric policies instated during the pandemic to the recent focus on staff-cutting and increased productivity.

AI’s analytics capabilities can process vast amounts of sentiment and behavioral data to predict when an employee might be approaching burnout. Early detection enables organizations to intervene proactively, offering essential support and accommodations¹². AI-powered chatbots, and employee assistant agents can provide immediate support and resources through chatbots and assistants, helping employees manage stress and anxiety effectively¹².

Even with these positive effects on work life, the rapid pace of AI integration may cause anxiety and uncertainty among workers, especially in industries where automation is more visible. However, recent research shows a shift in perception—AI is now more often viewed as a tool to augment rather than replace workers³. 77% of managers said the primary reason they’re adopting AI in the workplace is to either enhance worker productivity or improve efficiency, an 11% increase from last year³.

75% of employees said they’d feel more excited about AI if their organization openly communicated its plans¹³. When employees understand why it’s being implemented, how employee data will be protected, and the goals and benefits of AI adoption for both the company and its employees, they’re more likely to view it as a tool that empowers them rather than one that replaces them¹³. This is a significant mindset shift from one of fear to one of feeling cared for, which is a sentiment highly correlated with engagement¹.

People Science Tip
“Focus on building psychological safety in your teams to ensure people are willing to speak up and share their concerns about AI. This level of transparency helps build trust, reduces fear of the unknown, and can build excitement around upskilling.”

Conclusion: Thriving through change means putting people first in the age of AI

Since the pandemic, organizations have been grappling with challenges like returning to office, raising productivity, and adopting AI. In a rapidly transforming workplace, understanding and addressing the fundamental needs of employees is essential for survival. After three years, the People Success Elements continue to provide guidance for leaders on what matters most for people to be happiest and most productive at work.

In this era of unprecedented change, people strive to stay engaged in their work. AI—especially tools like Copilot—is emerging as a powerful ally in the workplace. It can help employees feel more purposeful, capable, empowered, and balanced. As organizations continue to adopt AI, the approach should be people-centric focused on using it to amplify human potential—not just automate tasks. The Viva measurement apps (Glint, Pulse, and Insights) are ideal for assessing Copilot adoption and its impact on the employee experience.

Deeper Insights in the People Success Summer Series

Stay tuned for the rest of The People Success Summer Series, which will dive deeper into the evolving role of each of the People Success Elements in the current world of work.

References

¹The 2025 engagement and attrition key driver refresh and longitudinal analysis determined the top predictors of engagement (across 230 customers) and voluntary attrition based on LinkedIn Glint survey scores in 2022, 2023, and 2025 representing 6.8 million employee survey responses.

² Forbes. (May 12, 2025). AI And Knowledge Workers: What Could Change In The Next 10 Years?

³ Gartner. (July 6, 2022). 6 Ways the Workplace Will Change in the Next 10 Years.

⁴ Microsoft. (2025). The 2025 Work Trend Index Annual Report: 2025: The Frontier Firm is born.

⁵ Gartner. (September 24, 2020). Stop Training Employees in Skills They’ll Never Use

⁶ Microsoft. (June 17, 2025). Breaking down the infinite workday.

⁷Microsoft Education Team. (2025, April). Empowering everyone with agents in Copilot Chat.

⁸Microsoft. (2025). AI at Work: “Intelligence on Tap” Will Reshape Knowledge Work

⁹Forbes. (May 15, 2025). Return To Office Is Back. Why Is Remote Work Still Controversial?

¹⁰ Microsoft. (2024, September 17). Learn how Copilot agents enhance collaboration within your team on Microsoft Teams.

¹¹ Microsoft. (Dec 3, 2024). Empowering potential: AI’s role in disability inclusion.

¹² Forbes. (September 27, 2023). Combating Employee Burnout With AI And Future Of Work Policies.

¹³ Insentra. (December 17, 2024). AI Anxiety in the Workplace: How to Overcome Employee Concerns.

Insights from our Tech Industry Cohort Kickoff

AdrianaDuqueHughesPhD — Tue, 13 May 2025 15:00:00 GMT

The tech industry is at a crossroads, facing challenges that demand bold leadership and strategic action. Our Tech Industry Cohort, launched last month, created a space for innovators to tackle these issues head-on and explore the opportunities hidden within the turbulence. Here's what we uncovered in our time together:

🔹 Facing the Pressures Head-On
The ripple effects of layoffs continue to test employee trust and morale, leaving organizations striving to rebuild confidence. Geopolitical tensions and trade issues compound this uncertainty, forcing leaders to adapt while navigating rising costs.

🔹 The AI Balancing Act
AI adoption represents the industry's greatest opportunity—and challenge. While skill shortages and job disruptions demand immediate attention, the concerns around data ethics underscore the need for thoughtful, transparent implementation. AI is not just a tool; it’s a partnership. Getting it right is how organizations unlock its immense potential while empowering employees to embrace change.

🔹 Seizing Opportunities Through Training
Employees are eager to upskill, especially in AI, and organizations that embrace enterprise-wide training programs reap the rewards of better innovation, stronger retention, and increased revenue. Addressing barriers like lack of time, leadership buy-in, and employee confidence is key to creating a culture of growth and resilience.

🔹 Fueling Transformation Through Collaboration
HR and IT must work hand-in-hand to drive successful AI integration—open communication, tailored learning programs, and a focus on data protection are critical. Strategies such as AI collaboration groups and appointing AI ambassadors enable employees to lead the charge in adoption.

🔹 Boosting Engagement with AI Agents
Recognition matters, and Copilot AI Agents are already helping organizations make it easier for employees to feel valued. Automated recognition systems ensure appreciation is timely and frequent, boosting morale and driving motivation across teams.

🔹 Proving AI’s Worth
Demonstrating ROI to leadership isn’t always straightforward, but trusted advisors and clear evidence of outcomes are helping organizations make a compelling case for AI investment.

The insights shared in this cohort were a powerful reminder: the tech industry’s challenges are deeply human. Prioritizing transparency, engagement, and empowerment allows organizations to not only adapt but thrive in the face of change.

Now Is Your Time
Leadership in the tech industry isn’t just about navigating the present—it’s about shaping the future. The Tech Industry Cohort is an invitation to join forces with other trailblazers, dive into actionable strategies, and drive innovation that sets the standard for our field.

Don’t miss the chance to be part of this journey, and register for our next session on August 12^th. Let’s innovate, collaborate, and lead the way forward—together!

Research Drop: Unlocking AI Potential for Frontline Workers

Megan_Benzing — Tue, 13 May 2025 11:05:02 GMT

Research Drop in Brief:

Information workers (IW) and frontline workers (FLW) experience AI transformation at work differently. Though in our sample access to tools is the same, adoption metrics reveal a gap.
FLWs face unique challenges in AI adoption, such as ineffective training and uncertainty about how to properly integrate AI into their workflow. Addressing these barriers can unlock the potential of AI to improve working conditions, efficiency, and communication for FLWs.
On average, IWs workers are more likely to report optimistic AI sentiment, see the value proposition for organizational investment, and realize the personal benefit of using AI at work. In some industries and departments, this sentiment gap between IWs and FLWs reaches 22%.

As we continue to see more and more AI integration in our daily workflows, our ability to recognize its impact on individuals grows. AI use cases are emerging in all job types, and industries and change leaders are focused on optimizing AI investments across the board. Much of the research we see about AI adoption and transformation centers around the Information Worker (IW), typically a salaried employee whose primary responsibilities are conducted sitting at a desk. But what about Frontline Workers (FLW), who are more likely to be deskless and paid hourly? Are FLWs having the same experiences with AI as their IW counterpart? How is their AI journey similar or unique to their deskbound peers?

This month’s Research Drop explores the differences between full-time employees who self-report as paid an “annual salary” or an “hourly wage.” For the sake of this blog, we use these classifications as a proxy for IWs and FLWs. We acknowledge this proxy may not be perfect but aim to provide some directional insight into how these groups experience AI at work and what we may have overlooked as stewards of AI transformation.

Usage differences exist despite AI access parity

One of the first things we look for when comparing two employee groups is their access to AI – are there disparities in what tools and technologies certain employees are provided? Interestingly, we found a less than 1% difference between salaried employees and hourly employees regarding AI access – 61% of salaried employees and 60% of hourly employees report high access to organization-sponsored AI tools. This suggests that from a rollout perspective, both IWs and our FLWs are included as user groups for pilot programs, license rotations, and full-scale implementations.

However, we start to see some differences when we look at usage metrics. 61% of salaried employees are high or moderate frequency AI users (use AI at work at least once a week), while only 51% of hourly employees fall into high/moderate use categories. This is our first indicator that there may be some underlying differences in these groups’ AI experiences.

A multi-level pattern of falling behind emerges for FLWs

At multiple levels of AI transformation (for example, greater workforce impact, organizational impact, and individual impact), we uncovered a pattern. FLWs’ AI sentiments are lower than their IW counterparts. This raises the alarm that current AI transformation strategies are not as effective within the FLW population.

Uncertainty at the macro level

We found a 10-percentage point difference between hourly and salaried employees on our AI Optimism scale, which includes items such as believing that AI transforms work for the better and that AI is built to benefit employees. When employees are not excited and hopeful for an AI-future, these worries will negatively impact their experience with AI and reduce the likelihood of successful AI integration.

Where everyone is using AI at work and is inspired by success stories and innovative use cases, IWs are more likely to agree with positive, future-forward sentiments about AI. While still optimistic (the favorability score for the AI Optimism scale for hourly employees was 59%), these FLWs may be slightly more apprehensive when it comes to AI at work. In some industries where AI transformation is more aligned with automation than augmentation, FLWs might have concerns about the long-term vision for their roles. From a macroeconomic perspective, however, while some people may think that AI automation is replacing FLW jobs, we actually see that FLW jobs are growing in number. The World Economic Forum reported that frontline jobs are set to increase significantly in the coming years. Roles in industries like farming, delivery, and construction are expected to grow the most, along with positions in healthcare and education. This trend highlights the ongoing demand for these essential roles, even as technology evolves¹. Whereas IWs may be more inclined to see AI as a tool that enhances productivity and opens doors to innovation, FLWs may need more direct support in mitigating concerns around job replacement to feel optimistic.

When connecting the dots gets blurry

We found an 8-percentage point difference between hourly and salaried employees on our Organizational Value of AI scale. This includes items such as believing AI is critical for organizational success and is worth the investment. Again, FLWs are behind IWs in seeing the full vision for enterprise-wide AI and the impact it could have. The gap is slightly smaller in this instance, suggesting that FLWs are slightly more likely to see the benefit for their organization than they are to see AI being beneficial for themselves.

Employees’ understanding of the organizational benefits of AI may be impacted by how well their organization translates the enterprise-wide transformational strategy. Internal communication for FLWs regarding strategy and vision is challenging to ensure, as their work is often shift-based or dispersed; consistent access to technology is limited. Organizations need to be intentional in how and when they share company-wide communications, making sure that the goals and benefits of technology rollouts meet their people where they are in their workday and workplace. Additionally, empowering FLWs to co-build the vision of AI transformation may help them translate their experience being on the frontline directly to the goals and desired benefits of AI for both their organization and themselves.

Challenges in capturing personal impact

We found a 10-percentage point difference between hourly and salaried employees on our Realized Individual Value of AI (RIVA) scale. This includes items such as reducing work-related stress and speeding up task completion. Even beyond seeing the vision for company strategy, seeing the vision for personal productivity and impact is critical to adoption. Change leaders seek to justify the ROI of AI investments, and enthusiastic employee voices are vital to achieving this goal – but FLWs in our sample are not yet reaping the benefits that IWs are.

The more benefits one sees of making a behavioral change at work (for instance, using AI), the more likely they are to continue that behavior. Not seeing as many benefits as IWs puts FLWs at risk to have lower adoption levels. Microsoft internal research shows that AI mastery requires time and practice, where employees see 20-30% higher sentiment related to learning, thriving, and productivity when Copilot usage is 6 months, compared to 3 months². FLWs may need extra support to ramp up to the sustained use necessary to build AI proficiency that results in strong value. It can be helpful to surface high value scenarios for this employee group to capitalize on AI value immediately, as their time is limited and dedicated time for upskilling is scarce.

The moment to capitalize on frontline AI-human collaboration is now

Research shows that when AI for FLWs is done right, it acts as a critical resource for these employees³. AI has the potential to impact frontline work by improving working conditions, accelerating efficiency, and supporting on-the-move tasks that require high levels of operational coordination. AI can improve internal content generation and distribution for company-wide communication by getting to “the right people at the right time via the right channel”³. Consider how to be more intentional with the expectations for FLWs’ AI experience. It is unlikely that the goal for FLWs is to become “super users” – but rather to support their workload and augment their experience.

Knowing when to integrate AI tools and how to optimize what’s available is a challenge for FLWs. The top barriers for their AI adoption are lack of time to understand the tools, ineffective training, and uncertainty around when to use the tools⁴. This results in scattered usage and value add. More than half of FLWs have had to quickly adapt to using digital tools without any prior training or preparation⁵. To truly unlock the potential of AI for frontline workers, it's crucial to tackle challenges like training gaps and tool accessibility with focus and care.

Practical steps to engage and equip your frontline workers for AI transformation

As we think about how to shore up the gaps that exist between information workers and frontline workers in terms of their AI experience, it’s important to remember that we consistently see that AI adoption is not one-size-fits-all. Various external and internal factors influence each employee's likelihood of adopting and recognizing value from integrating AI into their workflow. At a high level, based on varied tasks and goals, each industry and department will likely differ in how it approaches AI transformation. Industries and departments with larger FLW populations need to be strategic in how they invest and roll out their AI technologies to both employee groups.

Bring FLWs into the process early and seek their feedback to better understand the needs of FLWs. What pain points are they having? Where can AI be best deployed to alleviate barriers and expedite processes? Bringing them into the transformation process can also help crystallize the impact AI can have on the business, as FLWs are likely a direct channel to customer needs and opportunities. Create opportunities where FLWs can provide feedback, such as deploying surveys via mobile devices, ensuring all shift schedules are represented, setting up kiosks and QR codes in common areas, or setting aside time during weekly huddles to seek feedback. Empower FLWs to be a critical part of the evolution of their roles/teams. By soliciting their input, changes can be made that scale to similar roles across the organization. Encourage FLWs to upskill on the components of AI that excite them and which directly improves their day-to-day, whether through AI taking a larger share of the logistic load or through an AI-powered communication system.

To make the most of AI for FLWs, companies need to tackle issues like training gaps and tool accessibility, while figuring out tailored ways to roll out AI solutions. By getting employees involved early on and focusing on their specific needs, businesses can create smarter workflows and an improved work experience for everyone.

Stay tuned for our June Research Drop to keep up with what the Microsoft People Science team is learning!  

This month’s Research Drop analyzed 1,800 global employees (1,022 salaried employees, 778 hourly employees) from the Microsoft People Science AI Readiness Survey from April 2024. Note: participants were asked to respond to questions around “generative artificial intelligence” which has been shortened to “AI” for the sake of this blog.

¹ World Economic Forum. (January 2025). Future of Jobs Report 2025.

²Analysis conducted by Microsoft HR Business Insights team on internal Microsoft Copilot users.

³ Forbes. (April 25, 2025). AI: The Frontline Jobs Revolution You Didn't See Coming.

⁴ BCG. (June 26, 2024). AI at Work 2024: Friend and Foe.

⁵ Microsoft WorkLab. (January 12, 2022). Technology Can Help Unlock a New Future for Frontline Workers.

⁶ Accenture. (March 10, 2025). Gen AI amplified: Scaling productivity for healthcare providers.

⁷ McKinsey and Company. (January 28, 2025). Superagency in the workplace: Empowering people to unlock AI's full potential.

⁸ Gartner. (March 5, 2025). Gartner Predicts Agentic AI Will Autonomously Resolve 80% of Common Customer Service Issues Without Human Intervention by 2029.

⁹ Salesforce. (January 7, 2025). AI Education: How to Reskill Your Team for the Future of Customer Experience.