Insights from our Tech Industry Cohort Kickoff
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: đš 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. đš The AI Balancing Act AI adoption represents the industry's greatest opportunityâand 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; itâs a partnership. Getting it right is how organizations unlock its immense potential while empowering employees to embrace change. đš 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. đš Fueling Transformation Through Collaboration HR and IT must work hand-in-hand to drive successful AI integrationâopen 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. đš 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. đš Proving AIâs Worth Demonstrating ROI to leadership isnât 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 industryâs 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 isnât just about navigating the presentâitâs 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. Donât miss the chance to be part of this journey, and register for our next session on August 12 th . Letâs innovate, collaborate, and lead the way forwardâtogether!Think like a People Scientist: Leading AI Change with Employee Sentiment
In our latest âThink like a People Scientistâ webinar, I was joined by Ryan Lebow, Principal People Scientist, Microsoft, to talk about the topic of measuring employee sentiment with Viva Glint and Viva Pulse during AI transformation. This webinar shared valuable strategies and tools to help organizations navigate the complexities of AI adoption through employee sentiment. Key Takeaways Understanding Change and Sentiment: Ryan kicked off the session with a historical perspective on technological advancements, highlighting the fear and uncertainty that often accompany significant changes. From the invention of the telegraph to the rise of personal computers, these examples underscore the importance of addressing employee sentiment during times of transformation. AI in the Workplace: The discussion then shifted to the present, focusing on AI's impact on the workplace. Ryan shared statistics showing that 75% of employees are already using AI at work, with many bringing their own AI tools. This highlights the urgency for organizations to develop clear strategies for AI implementation and measurement. Measuring AI Sentiment: Ryan emphasized the importance of measuring both the business impact and employee experience of AI. Tools like Viva Glint and Viva Pulse can help organizations gather valuable feedback and insights. These tools enable leaders to understand how AI is affecting productivity, engagement, and overall sentiment. Practical Steps for HR and IT Leaders: The session also provided practical steps for HR leaders to collaborate with IT teams, including scheduling regular meetings, identifying key metrics, and creating collaborative working groups. These strategies are essential for ensuring successful AI implementation and addressing any challenges that arise. To further support your AI measurement journey, we recommend exploring the following resources: Read our âTop Ten Tips for Measuring AI Transformationâ Leverage the Playbook for Measuring Copilot Implementation and Impact with Viva Access the slides from the webinar below and share with your colleagues and AI leaders Visit our âThink like a People Scientistâ Adoption page for all past and upcoming webinars in this series Thank you for being part of our âThink like a People Scientistâ series. We look forward to seeing you at our next event!Research Drop: Unlocking AI Potential for Frontline Workers
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 monthâs Research Drop explores the differences between full-time employees who self-report as paid an âannual salaryâ or an âhourly wage.â 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 â 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 â 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 groupsâ 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. FLWsâ 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 evolves 1 . 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. Employeesâ 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 â 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 months 2 . 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 employees 3 . 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 âthe right people at the right time via the right channelâ 3 . Consider how to be more intentional with the expectations for FLWsâ AI experience. It is unlikely that the goal for FLWs is to become âsuper usersâ â but rather to support their workload and augment their experience. Knowing when to integrate AI tools and how to optimize whatâs 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 tools 4 . 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 preparation 5 . 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, itâs 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!âŻâŻ This monthâs 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 âgenerative artificial intelligenceâ which has been shortened to âAIâ for the sake of this blog. 1 World Economic Forum. (January 2025). Future of Jobs Report 2025. 2 Analysis 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.How important is belonging to your organization?
Microsoft People Science provides customers with the latest research on the modern workplace, helping them navigate changes that may impact the workforce such as technological advancements, economic instability, and societal shifts. For many organizations, DEI (diversity, equity, and inclusion) is a key focus area. Research shows that fostering a sense of belonging and good treatment at work significantly impacts employee engagement and financial success. Evidence-based decision-making is a good way to guide any programmatic adjustments to DEI practices that affect employees. Start by collecting and acting on employee feedback and let facts be your guide. Staying up with the changes With technological advancements like AI transforming work, economic instability reshaping markets and impacting labor and growth, demographic shifts challenging workforce stability, and work models and expectations constantly evolving, organizations often struggle to figure out how to adapt while keeping employee engagement and productivity at the forefront. The Microsoft People Science team strives to address the big questions and concerns our customers face by providing straightforward, researched-backed information to help them navigate turbulence and make informed decisions. With 85% of companies reporting having dedicated DEI budgets 1 , deciding how to move forward with DEI is a business decision many companies face. The Microsoft People Science team has been researching modern workplace practices (like DEI) for over a decade and has data that can help guide customers. Based on over 350 million data points (collected between 2020 and 2023) and extensive research on psychology and workplace behavior, we examined how employees stay motivated at work, despite the tumultuous changes during that time period. This study resulted in the People Success Elements 5 , a model for understanding what contributes most significantly to employee happiness and success. This framework helps Microsoft Viva Glint offer out-of-the-box employee surveys focused on what matters most to people to be their best and do their best at work. Three of these elements in particular â Empowerment, Connection, and Wellbeing â are based on trust, and call out for a positive work environment that is open and welcoming, where workers feel valued, respected, appreciated, and treated with fairness and care. This kind of environment is more prevalent in companies that place an emphasis on people-centric policies, management practices, and employee programs. People Science Insights In 2023, we noticed a market shift among our customers towards a focus on achieving high performance, resulting in our High Performance Organization (HPO) eBook 6 . Our customers were certainly still concerned with creating an âengagedâ workforce, but many of them wanted to establish a culture that would drive individual productivity and team performance as well. To help determine the workplace conditions that predicted financial performance (as measured by 35 financial success indicators), we studied various workplace factors across 220 customers that best differentiated company performance 7 . This study resulted in the following Top 15 list: Significantly, employeesâ sense of belonging is the top predictor of financial success. It predicts five out of six top financial indicators 7 . And our latest engagement driver analysis showed that across all 400+ engagement survey topics, belonging has the strongest correlation with happiness at work. No wonder the belonging question is so popular â it is used by over 70% of Glint customers 8 . Fostering a culture where people feel a strong sense of belonging helps organizations achieve higher levels of employee engagement, commitment, and greater financial success. A more committed and dedicated workforce increases retention, thus paving the way to greater efficiency as measured by net income over workforce size. Belonging is a huge win-win for the employee and the business. A sense of belonging is only possible if people feel well-treated at work. In the Top 15 table we see several other related top drivers of business success that describe important treatment factors like feeling free to speak oneâs mind, being a valued member of the team, and being able to be oneself at work. In a recent study on survey question utilization trends across Glint customers 10 , the Speak My Mind item (currently used by 23% of Glint customers) was identified as a key opportunity to increase engagement and performance because it gets at a component of psychological safety, which has a positive impact on turnover, stress, and productivity (For more, view our recent webinar on Building Psychological Safety 11 ). In sum, this data shows how someone is treated at work in terms of feeling they belong, feeling valued and respected for who they are, and being able to speak freely about what concerns them is critically important to employee happiness and success and company performance. Let the evidence decide your next steps Here are some Microsoft People Science best practice methods you can use to examine your own workforce attitudes and opinions about DEI: Align: As with all changes that impact your workforce, reconnecting to your organizational strategy and checking in with employees can help teams chart the best course forward. A great way to do that is through seeking and acting on survey feedback. Collect: Leverage existing surveys and listening channels to involve employees during times of change. The goal is to understand how your employees feel about their work environment and treatment. Listen: Consider hosting live focus groups and dialogue sessions to allow people to share their ideas and concerns. Acknowledge what youâve heard and any plans to follow up. Assess: Based on the feedback youâve gathered, assess how similar or divergent attitudes are across your workforce and how they align with your company stance or position on the changeâs topic(s). This insight may help you think through your next steps. The Microsoft Viva measurement apps (Glint, Pulse, and Insights) are an ideal way to assess the impact of large-scale changes to your organizational strategy and what helps employees perform at their best. Glint and Pulse provide out-of-the-box surveys and questions that assess how people feel they are being treated and the extent to which that treatment is impacting their sense of belonging, happiness, productivity, and success at work. Collecting and acting on employee feedback is crucial for guiding programmatic adjustments that affect your workplace. Our research shows that fostering a sense of belonging and good treatment at work significantly impacts employee engagement and commitment. Companies that prioritize people-centric policies and create a positive work environment see higher levels of productivity, retention, and financial success. Microsoft People Science Microsoft People Science is a research-backed and people-centric approach to the study and practice of happiness and success at work. People Science prides itself on providing customers with the latest research and findings about the modern workplace. We serve customers across a wide spectrum of industries, geographies, and company cultures, and remain open to all customer orientations on any given topic, current event, or public discourse that may be impacting their strategies and choices. References 1 Workday. (February 6, 2024). Workday DEI Landscape Report: Business Leaders Remain Committed in 2024. 2 MIT Sloan. (November 7, 2024). How Integrating DEI Into Strategy Lifts Performance. 3 Harvard Business Review. (December 13, 2024). Continuing the Work of DEI, No Matter What Your Company Calls It. 4 Pew Research Center. (November 19, 2024). Views of DEI have become slightly more negative among U.S. workers. 5 Viva Glint Blog. (April 20, 2023). The Elements of People Success. Six Microsoft People Science. (2023). Redefining High Performance in the New Era of Work. 7 This research draws from employee survey responses from 220+ customers, collected from the year prior to October 1, 2023. 132 survey questions covering the entire ranges of People Success factors were utilized in various tests (e.g., multiple regression) to validate their impact on talent outcomes (engagement, attrition, performance) and business outcomes (35 financial metrics, e.g., stock return, market cap per employee, etc.) 8 This analysis examined LinkedIn Glint customer item utilization between September 2023 to August 2024 (n = 728 customers). 9 American Psychological Association. (2023). 2023 Work in America Survey: Workplaces as engines of psychological health and well-being. 10 Viva Glint Blog. (December 18, 2024). Research Drop: What Organizations Ask in Employee Surveys (Plus Three Untapped Opportunities for 2025). 11 Microsoft Viva Blog. (November 15, 2024). 3 Steps to Build Psychological Safety on Your Team.Research Drop: Investing in Training Opportunities to Close the AI Skills Gap
Research Drop in Brief: 39% of workers' existing skillsets will become transformed or outdated over the next five years, emphasizing the need for organizations to keep up with skill demands. Despite the rise in AI use, 70% of organizations report struggling to equip their workforces with the necessary AI skills, and 62% of leaders believe their organization has an AI literacy skill gap. When employees feel adequately trained in AI, they are 1.9x as likely to report realizing the value of AI, such as improved decision making. To close the gap, organizations need to focus on providing equitable access to training opportunities and helping their employees dedicate time for upskilling and continuous learning. With rapid workplace changes, 39% of workersâ existing skillsets will become transformed or outdated over the next five years 1 , making it imperative for organizations to keep up with the demands of this revolutionary workforce change. One of the significant macrotrends impacting work is the introduction of generative AI to the workplace, such as Microsoft Copilot. The integration of AI tools and technologies continues to grow across industries and roles, directly altering the way employees across the planet execute their daily tasks. Three in four organizations (77%) are planning to reskill/upskill their workforce in the next five years in order to better work alongside AI 1 . Leaders acknowledge that equipping employees with the skills they need to fully optimize AI at work can be tricky â 31% of leaders report a main barrier to reskilling/upskilling their workforce is not knowing where to start with data and AI training 2 , which can cause lag in scaled organizational development. And we see consistently that employeesâ excitement for AI at work outpaces the ability for their organizations to roll out organization-sponsored AI tools 3 , which can lead to inconsistent adoption and misaligned AI usage. For this reason, now is the time for organizations to invest in enterprise-wide AI training and learning opportunities. This monthâs research drop explores the current AI skill gap, the impact of AI-specific training, and strategies for organizations to better equip their employees to succeed in the era of AI. The reality of the AI skill gap The AI skills gap is increasingly prevalent â 70% of organizations report struggling to equip their workforces with the skills they need for future success 4 , 62% of leaders report their organization has an AI literacy skill gap 2 , and 39% of CEOs report low levels of confidence about their employees having the right skills to fully maximize AI benefits 5 . The demands of leading through AI transformation can feel overwhelming, involving high-effort experimentation and comprehensive organizational strategies for deployment. The pace of change in the current workplace continues to be faster than ever, with employees simultaneously feeling the challenge of keeping up and excitedly leaning in to experiment with AI before their organization officially adopts. Leaders are in a difficult situation of needing to support employees encompassing a wide variety of stages in their AI journey. And employees are craving more opportunities to learn. Nearly half of employees feel they are receiving moderate or less support for AI capacity building at their organization 6 , which can directly impact their ability to dedicate time for learning and upskilling. A strong organizational investment in training and learning opportunities is needed to help bridge the gap. Differentiating value of AI-specific training 48% of employees rank training as the most important factor for AI adoption 6 . And for good reason â at organizations with enterprise-wide data and AI literacy programs, the results are striking. Within these organizations, 90% report faster decision-making, 81% report increased revenue, and 81% report better employee retention 2 . Training is a key input to the adoption and usage of AI. We found that while 43% of employees who feel adequately trained are high frequency users of AI (at least once per day), less than 1% of employees who donât feel adequately trained are high frequency users of AI. This means nearly no one fully adopts what they donât feel equipped to use, and employees missing that foundational training may not be trying or leveraging AI at all. Weâve seen in previous research that upskilling can improve confidence, encourages play and experimentation, and drives behavior changes, further underscoring that adequate training is a key ingredient in driving AI use, adoption, and ultimately, transformation. Training also impacts employeesâ conviction in their future in an AI-first organization. Employees who feel adequately trained in AI are more likely to agree that they have irreplaceable skills (79%), compared to employees who donât feel adequately trained (68%). When employees are confident and comfortable with AI technology, they better see the vision for how they can collaborate with AI, rather than compete with it. And once employees are trained and leveraging AI, they also become more likely to realize the benefits of using AI at work. In our data, we see that employees who feel adequately trained in AI are 1.9x as likely to report realized individual value of AI (RIVA) outcomes. Our RIVA scale includes positive AI outcomes such as employees reporting reduced stress, better decision making, and higher quality work output. These outcomes are critical to not only fully maximizing and seeing the ROI of AI at work, but also to reinforce the utilization of AI to complete daily work tasks. The lagging state of organization-wide AI-specific training programs Unfortunately, although we see the benefits training can bring, training opportunities can be hard to come by. One report shows that only 49% of employees have received training in AI and that 57% of employees report feeling âbehindâ in keeping up with AI 7 . Employees worry that this might negatively impact their organizationâs future â 1 in 3 employees report that a lack of AI-specific training is the biggest barrier to their workforce being prepared to leverage AI 4 . So, whatâs going on? In our dataset, two main obstacles to training confidence emerged: accessibility of training and capacity to engage in training. Accessibility of training As many organizations are still in the planning and early implementation stages of AI, their training programs are as well. Many employees report that training feels exclusive to certain organizational groups. Only 16% of employees report that their organization offers enterprise-wide AI training, while 77% report that their organization either offers no training or only offers it to a selective group of employees 4 . Some of this may be aligned with pilot programs in the early stages of AI transformation, where training might be limited to those with early access licenses. These trainings may also be limited to groups the organization have selected due to their outspoken excitement, hand raising, or opt-in processes. When organizations take a proactive approach to providing AI tools to their employees, employees are far more likely to feel equipped to use them. We found 80% of employees with high access to company-provided AI tools agree that they are adequately trained in AI, compared to just 60% of those with low access to company-provided AI tools who are left to navigate AI on their own â a 20-percentage point difference. The groups most likely to be given access are those teams that are more likely to be closely involved in the AI transformation initiatives. In our dataset, the departments with the highest access were Product Development and IT. While it is important that those directly involved in the transformation strategy have tool and training access, the rest of the organization may feel left out and fall behind, resulting in a lack of cross-functional use cases and deepened skills gaps throughout your organization. Capacity to engage in training Itâs not only the accessibility of training that impacts whether an employee feels skilled in AI, however. There are individual restrictions that may hinder upskilling opportunities. A key blocker can be that training takes time. For an employee to engage in training, they need to have the capacity to dedicate work hours to participate, to expend focus time on learning, and to commit to experimentation to practice the learnings. When an employeeâs workload is too high, or their leader doesnât support dedicated learning time, they may not benefit from available training. We also find that employee burnout had a direct impact on training confidence. When burnout is high, training confidence plummets. While 83% of employees with no burnout indicators feel adequately trained, that number drops to 55% for employees that have three or more burnout indicators â an almost 30-percentage point difference. Burnout indicators include overwhelming workload, little or no support from managers/peers, and unclear job responsibilities. Employee burnout is a sign that something needs to be addressed in the workplace to best set employees up to be happy and successful. Employees who feel burnt out have a hard time feeling prepared and trained to integrate AI into their work. If you know your organization already struggles with burnout or dedicating time for learning, consider how you can work AI training into role or function-specific time. When leveraged properly, AI can reduce burnout by reducing time spent on tasks or by helping employees make informed decisions. If an employee is already burned out, they donât have the capacity to attend training or to learn and that generates a negative feedback loop. Therefore, itâs important for organizations to understand that itâs not only about providing training opportunities that lead to upskilling/reskilling but also supporting employees who want to invest their time and capacity to engage in the training. Strategies for organization-driven AI upskilling/reskilling Employees are craving training opportunities to help them close the AI skills gap, so how can organizations be better equipped to meet this need? Our research shows that organizations can help employees âfuture-proofâ themselves by ensuring equitable access to training and by helping employees invest in training opportunities. If the goal of your organization is enterprise-wide AI adoption, then training also needs to be scaled throughout the organization, whether through network-based sharing or democratized access to training resources. Consider where you may have gaps in developmental opportunities or accessibility of learning. Are there departments who havenât been provided with training yet? Some employees may even be unaware of what opportunities are open to them. Focus on clear company communications that share the location and availability of training, learning paths, and skill-building sessions. While some employees may proactively look for these opportunities, others may need more direct instructions on how to upskill themselves. It's also helpful to brainstorm scalable strategies for training. For example, can you leverage AI champs to conduct training? Perhaps they can help create a âtraining-in-a boxâ kit which can be adapted by function or region to leverage the learning communities that already exist in your organizational network. AI-specific training should slot into your continuous learning strategies, where an emphasis on cross-team sharing, learning, and experimentation helps your workforce keep up with the pace of change. Employees also need leadership and managerial support to engage in training. Workloads can be high and bandwidths tight, so employees may not feel as though they have the capacity to focus on learning. Consider a monthly calendar hold for your team to dedicate the time to engage in learning. Include training or structured L&D programming as a team-wide goal to measure for the quarter, raising its importance to that of other team outputs. Employees need to feel safe and supported to prioritize their development and are likely to look to their leaders to model this behavior. This can also be a key moment where leaders can help connect the dots between individual roles and organizational strategy, further helping employees to invest in the vision and future of AI at your organization. To conclude, organizations should deeply invest in AI training opportunities and support employees in dedicating time to upskill. By promoting continuous learning and addressing factors such as burnout, companies can prepare their workforce for the future. A well-trained and supported team is essential for successful AI transformation. Stay tuned for our May Research Drop to keep up with what the Microsoft People Science team is learning!âŻâŻ This monthâs research drop analyzed 1,800 global employees from the Microsoft People Science AI Readiness Survey from April 2024. Note: participants were asked to respond to questions around âgenerative artificial intelligenceâ which has been shortened to âAIâ for the sake of this blog. 1 World Economic Forum. (January 2025). Future of Jobs Report 2025. 2 Datacamp. (2024). The State of Data & AI Literary Report 2024. 3 Microsoft People Science. (April 2024). The State of AI Change Readiness. 4 i4cp. (2025). Workforce Readiness in the Era of AI. 5 ADP. (2025). Unveiling the Next Anything: Navigating new frontiers in talent, compliance and technology. 6 McKinsey & Company. (January 28, 2025). Superagency in the workplace: Empowering people to unlick AI's full potential. 7 American Management Association. (March 19, 2025). Organizations Make Progress Adopting AI, but Many Employees Feel Left Behind.Think like a People Scientist: Bridging HR & IT for AI Success
âIT brings the tech, HR brings the people.â This was a memorable takeaway from last monthâs Research Drop on HR & IT collaboration in driving successful AI transformation. In our most recent âThink like a People Scientistâ webinar, we dove deeper into this idea and explored how, and why, HR and IT teams should be working closely together when it comes to driving AI transformation. In this webinar, Dr. Julie Morris, Program Manager in the People Science R&D team was joined by Dr. Megan Benzing, Senior People Scientist at Microsoft, and Craig Foster, Director of HR Digital Transformation at Microsoft. To kick of the session, we asked the audience which team(s) are currently leading the AI strategy in their organizations. Not surprisingly, over 50% reported that it was their IT team but 35% also felt that HR was one team that should be involved from across the organization. Some of the reasons given for this more collaborative approach included people-centricity, enhancing adoption and promotion of AI, and change management integration. Despite the general awareness that HR has a key role to play in AI transformation, our research uncovered some of the challenges that are facing HR when it comes to AI. During the webinar Dr. Megan talked about the lack of access to AI for HR teams and the impact that this can have on realized value, not only from HR employees but leaders too. You can read more about this research here. To bring a real-world example to the audience, Craig Foster shared insights from Microsoft's own journey in building the Employee Self-Service agent and digital transformation. He discussed how HR and IT teams at Microsoft have worked together to create a seamless experience for employees, leveraging AI to enhance productivity and engagement. Here are some key learnings from Craigâs experience: Integration of HR and IT: Successful implementation of the Employee Self Service (ESS) tool requires close collaboration between HR and IT teams to ensure both technical and human-centric aspects are addressed. Employee Experience: Focusing on enhancing the employee experience is crucial. This involves creating seamless and intuitive self-service tools that empower employees and improve productivity. Data Security: Ensuring the security and privacy of employee data is a top priority. This involves implementing robust security measures and addressing employee concerns about data protection. Change Management: Effective change management strategies are essential to facilitate the adoption of new technologies and processes. This includes providing training and support to employees to help them adapt to the changes. Continuous Improvement: The ESS journey is ongoing, with continuous improvements and iterations based on employee feedback and evolving needs. To further explore insights from this webinar and learn how to navigate AI transformation in your organization, we invite you to: Read our Research Drop: Investing in HR & IT Collaboration to Drive Successful AI Transformation Access the slides from the webinar below and share with your colleagues and AI leaders Join our next âThink like a People Scientistâ webinar on May 6 where we will discuss how you can lead AI change with employee sentiment Thank you for being part of our âThink like a People Scientistâ series. We look forward to seeing you at our next event!
People Science Researchers Reflect on SIOP 2025 Learnings
Last week several of our team members attended the 2025 Society for Industrial and Organizational Psychology (SIOP) annual conference in Denver, Colorado. This marquee event in our field brings together industry leaders, practitioners, and academic professionals to discuss, learn, and share on the most important topics. This year we heard a lot about building AI with âhumans in the loop,â agents disrupting employee experience workflows such as hiring and skills-based learning, and the future of employee listening. The Microsoft People Science led several sessions and participated in panels on topics related to agents at work, the future of nudge interventions, and manager action taking. Our sessions were packed with attendees, and we also hosted a customer networking event focused on how to measure AIâs impact on the employee experience. We had an energizing and productive time at the conference, and we look forward to applying many of the ideas into our work. Our team is buzzing about all that they learned: Building AI with a human in the loop AI will reinvent the workplace, and we should welcome it rather than run from it so that we can develop it with proper guardrails and ensure it enhances engagement and productivity, fosters innovation, and maintains ethical standards. By embracing AI and establishing ourselves as the âhuman in the loopâ, we can leverage AIâs potential to streamline processes, improve decision-making, and create a more dynamic and efficient work environment. Being the "human in the loop" means taking an active role in leveraging AI to enhance the workplace. It involves thoughtfully considering how AI can assist in hiring the right candidates, fostering career development, and helping individuals find meaningful and fulfilling work. Additionally, it includes identifying areas where AI can be applied to tasks that are rule-based, routine, and repeatable, while pushing the boundaries for what the future may entail, ensuring that these processes are optimized to reduce bias and improve reliability. This plays to our strengths as People Scientists. Jaime Gonzales, Principal People Science Manager AI coaching will help scale support at work, but empathy remains uniquely human Using AI for employee coaching is likely to significantly scale (and in many cases enhance) coaching experiences globally, particularly for employees who do not normally have access to great coaching on-the-job (e.g., front-line employees, deployed soldiers). Aspects of coaching like career plan development, performance appraisal, feedback, and collaborative ideation are all likely to benefit from the development of AI solutions in the coaching space. Further, AI can help managers identify skill gaps on their teams, suggest relevant training programs, and offer tailored career advice, better fostering employee growth and development and actually enriching the leadership of a manager. One aspect of coaching that the consensus suggested would remain uniquely human is the support that managers provide employees in moments of great need, vulnerability, and struggle. In these moments, the perception is that AI is likely to lack the nuanced understanding required in situations that demand empathy and direct human connection (those moments when an employee needs to know that their manager is standing beside them). The thinking goes that even if AI developed an improved capacity to understand and act with empathy, that the current workforce would not feel as supported and satisfied if the desired support were not coming from another person. Thus, while AI will likely augment several managerial tasks, it cannot (yet) substitute the profound importance of human empathy in some high-need and emotional work situations. Eric Knudsen, Principal Manager, People Science Analytics The data sources are expanding, but action continues to be the âwhyâ While there were more sessions than ever about different types of data collection methods and the use of passive data + AI analysis, the problem we are trying to solve has largely stayed the same: driving action. The growing interest in passive data collection methods and non-sentiment data are driven by better access than in years past and a desire from leaders to lighten the load on employees needing to provide their input via traditional surveys. These expanded approaches offer valuable insights, but it is crucial to remember that they are most valuable when they help leaders and managers make better, more informed decisions about how to best serve their people. While passive data may be held closer to HR and other groups who may own the data, those most insightful data points need to be shared back to the people within the organization (leaders, managers, and individual contributors!), ensuring that the insights lead to tangible improvements in their experiences and performance. Carolyn Kalafut, Principal People Scientist In uncertain times, we lean on psychological safety to help us adapt and be resilient When everything around us is changing rapidly, psychological safety becomes increasingly critical. Among all the varied session topics at SIOP (e.g., AI, skilling, listening), psychological safety was present throughout. It truly underscored that speaking up, asking questions, and being authentic are key to navigating the spectrum of workplace changes we are currently navigating. When our leaders and cultures create safe environments, it demonstrates a commitment to people-centric change and acts as a lighthouse to guide us through. Presenters discussed creating spaces where training can thrive through vulnerability, where employees are comfortable with providing honest feedback, and where experimenting with AI and agents is celebrated. By leading with psychological safety, we ensure that change is not just about adapting but also about thriving, resilience, and meaning. Megan Benzing, Senior People Scientist At a Turning Point: AI, Work, and the Role We Must Play At this yearâs SIOP conference, the conversation around AI took a meaningful step forward, and the momentum is only growing. Rather than chasing trends, discussions focused on how AI is reshaping work and the role we must play in that transformation. The message was clear: our responsibility is not just to analyze this transformation, but to help build it. Three key themes emerged for me across the sessions I attended. First, grounding efforts in familiar frameworks and well-established paradigms such as the Technology Acceptance Model or principles of social learning can help bring clarity and structure to adoption strategies. It doesn't have to feel like you are starting from scratch. Second, start small, and start now. Use AI in everyday workflows, identify a couple of use cases, test, and iterate. We heard great examples from job analysis to item creation and automated dashboards, by starting with breadth rather than depth you can help identify where deeper investments will have the most impact. Third, we need to evolve our skillsets. That means understanding how AI systems work, collaborating more closely with IT, product and design, and developing solutions that scale. We have a critical role to play in shaping AI at work, so it is ethical, effective, and truly human-centered. Caribay Garcia, Principal People Science ManagerBring stakeholders together with the Communications Track at the Microsoft 365 Community Conference
The Microsoft 365 Community Conference is the ultimate Microsoft 365 community eventâand the perfect place to expand your skills and knowledge in Microsoft 365 and AI. Youâll learn directly from over 160 Microsoft product leaders, and gain access to more than 200 sessions, including keynotes, workshops, breakouts, and hands-on demos. This year weâre bringing you all-new content and sessions to help you transform the way you workâand maximize your impact with the tools you use every day. Communications sessions Discover new ways to modernize communications, engage employees, and foster a strong organizational culture with Microsoft tools. Gain practical guidance, inspiration from experts, and real-world examples from peers across industries. Speakers John Cirone | Senior Director, Global Employee and Executive Communications Murali Sitaram | Corporate Vice President, Viva Engage Dan Holme | Principal Group Product Manager, Viva Engage Amy Morris | Director, Global Employee and Executive Communications Sessions What's New and Whatâs Next for Microsoft Viva Communications and Communities with Murali Sitaram, Kripal Kavi, and Jason Mayans GEEC Out Part 1: Insights and Strategies for Global Executive and Employee Communications at Microsoft with John Cirone and Amy Morris GEEC Out Part 2: An Insiderâs View of Communications, Change, and Culture at Microsoft with Amy Morris and John Cirone Reimagining Communications and Employee Engagement in the Age of AI with Michael Holste, Venkat Ayyadevara, and Jason Mayans Closing the Loop: Acting Effectively on Employee Feedback with Mark Straetmans and Alisa Liddle Viva Communications and Communities Administration, Moderation, Security, and Compliance with Sandra Martinez Vargas Viva Connections: Building an Employee App for Your Organization with SharePoint and Viva with Tejas Mehta and DC Padur Unlocking Enterprise Knowledge with AI and Copilot with Christina Ferancik Learn more See all Communications Track sessions.6 Insights from the Microsoft & Josh Bersin Webinar: Employee Experience in the AI Era
Our recent webinar hosted by Microsoft and Josh Bersin, âThe Employee Experience Platform Evolves in the Era of AI,â brought together industry experts including Josh Bersin, CEO and Global Industry Analyst from The Josh Bersin Company; Kathi Enderes, SVP and Global Industry Analyst from The Josh Bersin Company; and Prerna Ajmera, GM of HR Digital Strategy and Innovation from Microsoft. With business leaders seeking to enhance productivity, engagement, and well-being, AI-powered solutions are reshaping how organizations support their workforce. Here are six key insights from the event that highlight the evolving landscape of employee experience in the AI era. Watch the Microsoft & Josh Bersin Webinar: The Employee Experience Platform Evolves in the Era of AI 1. The Super Worker Effect One of the most compelling insights from the event that the Bersin team shared was the concept of the "Super Worker Effect. This idea emphasizes that the most significant factor in AI adoption is not the technology itself but workforce readiness. AI has the potential to transform employees into "super workers" by amplifying their capabilities, enhancing productivity, and enabling more meaningful work. Organizations must focus on preparing their workforce to leverage AI effectively, ensuring that employees can harness the full potential of these advanced tools. 2. The Role of the Employee Experience Platform The employee experience platform (EXP) plays a crucial role in the AI transformation journey. With the advent of generative AI, the role of EXPs like Microsoft Viva is evolving. The Bersin team shared that these platforms are becoming more personalized, streamlined, and impactful, interfacing with employees, contractors, and managers in natural language. This personalization enhances communication, skill-building, and overall employee engagement, making the EXP an indispensable tool in the modern workplace. 3. Change Agility in AI Transformation Traditional change management approaches are no longer sufficient in the rapidly evolving AI landscape. As Kathi Enderes from the Josh Bersin Company emphasized, thereâs a need for "change agility," a dynamic approach to managing change that focuses on continuous adaptation and growth. Unlike static change management, change agility involves iterative processes, bottom-up transformation, and a growth mindset. The EXP can support this by facilitating communication, tracking goals, and providing ongoing training and support. 4. HR's Leadership Role in AI Transformation HR departments have a pivotal role in leading AI transformation within organizations. Drawing on real-world insights from Prerna Ajmera and Microsoftâs own journey, as AI reshapes the workforce, HR must take on a leadership role in ensuring workforce readiness, driving change agility, and fostering a culture of continuous learning. HR professionals need to work collaboratively across the enterprise, partnering with IT, legal, and operations to implement AI tools and redesign job practices. This leadership is essential for creating a supportive environment where employees can thrive in the AI era. 5. Personalized Learning and Development (L&D) Effective communication and personalization are key to successful AI transformation. Prerna Ajmera suggests that using EXPs to tailor communication and create personalized employee journeys is crucial. By leveraging AI, organizations can deliver the right information to the right people at the right time, enhancing engagement and satisfaction. Additionally, fostering a culture of experimentation and sharing success stories can build positive momentum for AI adoption. Ajmera also highlighted how AI is revolutionizing learning and development strategies by analyzing skills, recommending tailored courses, and creating individualized development paths, aligning employee growth with organizational objectives. 6. Microsoftâs Approach to Building AI-Enabled HR Experiences Microsoft's approach to building AI-enabled HR experiences is comprehensive and multifaceted. The company focuses on responsible AI principles, connecting solutions with M365 and Viva, and leveraging AI-enabled tools like Employee Self-Service Agent to enhance employee experience and improve HR processes. Microsoft's strategy involves breaking down silos within HR, fostering a culture of continuous learning, and ensuring that AI tools are accessible and beneficial to all employees. Conclusion Microsoft & Josh Bersin provided valuable insights into how AI is not just a technological update-it is transforming how organizations engage, develop, and support their people. By leveraging AI-driven EX platforms, companies can foster a more agile, skilled, and satisfied workforce, positioning themselves for success in the evolving world of work. For more insights: Access the full report by HR technology leader Josh Bersin, in collaboration with Microsoft: âThe Employee Experience Platform Evolves in the Era of AI.â Read our blog on how weâre using the Employee Self-Service Agent Join us for our April webinar in the Viva Community: Think Like a People Scientist: Bridge HR and IT for AI Success
