We spent much time measuring AI’s impact on work.  This email is slightly long, but I hope you enjoy reading it. 

• Real-world productivity gains from AI are emerging but remain difficult to quantify at scale – While studies show AI can increase individual efficiency (e.g., +14% in customer support, +25% in creative work), its broader impact on total economic output and job creation is still unfolding.
• The Productivity J-Curve” suggests AI’s full impact won’t be immediate. Historically, transformative technologies (e.g., electricity, the internet) required decades to boost productivity significantly, as companies had to restructure workflows and develop new use cases.
• AI’s effects on displacement remain highly variable. Some workers (e.g., freelancers in writing-intensive fields) have already seen income declines, while others (e.g., AI-savvy professionals) may benefit from higher demand and wage premiums. The overall redistribution of labor and income is still taking shape.
• Accurate AI measurement is essential, but the data reported is very subjective and difficult to measure. Many employees perceive time savings from AI to be greater than measured results, suggesting that reduced cognitive effort and task satisfaction are important but harder-to-quantify factors in AI’s workplace impact.  Many knowledge workers report the use of Gen AI for self-satisfaction, making it hard to measure

The paper that I most liked is this study (though we referred to many papers for this analysis)

Butler, J., Vorvoreanu, M., Janßen, R., Sellen, A., Immorlica, N., Hecht, B., & Teevan, J. (Eds.). (2024). Microsoft New Future of Work Report 2024. Microsoft Research Tech Report MSR-TR-2024-56. 

Draup’s view is that innovation is one element that will protect and grow human labor. (From the paper cited above) Innovation, not just automation, drives long-term job creation. Simply automating existing tasks can lead to labor displacement without significant economic growth. Instead, AI must enable HR leaders to consider expanding employment opportunities rather than just replacing human labor.

Using this concept, we have built an interesting prototype. (we will make this available for your evaluation soon)

Advances in AI have made it easier to uncover significant skill overlaps between seemingly unrelated job families. Draup can map out which competencies frequently appear together by analyzing millions of job postings and employee profiles, revealing “skill adjacencies” humans might overlook.

A striking example is the overlap between data analytics and finance roles. At first glance, a Data Analyst and a Finance professional might appear to have little in common—one might work on web user metrics while the other deals with balance sheets. However, a closer look at their skill requirements tells a different story. Both roles rely heavily on quantitative analysis, proficiency in handling and interpreting data, and the ability to communicate insights to inform business decisions.

Figure 1: Overlap of key skills between a Data Analyst and a Finance role. The image highlights shared competencies – such as strong quantitative analysis, experience working with large datasets, and the ability to communicate insights

The takeaway is that skill adjacencies are becoming more pronounced across the economy and AI can help unpack nonobvious career moves

Non-Obvious Career Transitions via Skill Adjacency

By focusing on skills rather than job titles, HR can facilitate career transitions that once seemed non-obvious. Below are a few examples of how skill adjacency can enable employees to jump between very different roles:

• Coffee Barista → Entry-Level Robotic Programmer: Modern baristas often work with IoT-enabled coffee machines and digital point-of-sale systems, gaining exposure to basic tech troubleshooting and data readouts. Those technical aptitudes – comfort with machines, data tracking, and routine automation – can serve as a springboard into entry-level Robotic roles.
• Retail Associate → Supply Chain/Logistics Coordinator: Front-line retail workers frequently use inventory management software and learn to balance stock levels with customer demand. These skills are highly transferable to logistics roles, which rely on technology to manage warehouses, shipments, and inventory.
• Truck Driver → Logistics/Fleet Operations Manager: Professional drivers plan routes, adhere to strict schedules, and comply with detailed safety regulations. These skills align with logistics coordination and fleet management roles, which require route planning, compliance expertise, and time management.

Summary:  Workforce planners and Talent Acquisition professionals must prioritize innovation. A key innovation will be understanding how AI reshapes labor dynamics and drives the convergence of previously distinct job families in the AI era.

Full References

• Draup Skills Advisor Model Beta
• Brynjolfsson, E., Mitchell, T., & Rock, D. (2022). Skill Adjacency and AI-Driven Workforce Transitioning. Stanford Digital Economy Lab.
• OECD. (2023). The Future of Work: Skill Overlaps and AI-Powered Reskilling. Organisation for Economic Co-operation and Development.
• World Economic Forum. (2023). The Future of Jobs Report 2023: Key Skills for Workforce Mobility. World Economic Forum.
• Butler, J., Vorvoreanu, M., Janßen, R., Sellen, A., Immorlica, N., Hecht, B., & Teevan, J. (Eds.). (2024). Microsoft New Future of Work Report 2024. Microsoft Research Tech Report MSR-TR-2024-56.