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