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.
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