AI is reshaping the workforce by both creating and displacing jobs, so employees naturally focus on job security, dignity, and career growth. According to the 2025 WEF Future of Jobs report, by 2030 there is expected to be a net increase in jobs, with about 170 million jobs created and 92 million jobs displaced. Much of the growth is forecast in logistics, software technology, and healthcare, while many routine, function-based roles are at risk.

Despite heavy AI spending, less than 40% of companies that invest in AI have seen profits. One major reason is that workers often don’t find the tools useful or feel they were imposed on them. Without trust and buy-in, adoption stalls and productivity gains don’t materialize.

For AI to truly reimagine how work gets done, leaders need to:

• Address workers’ concerns about job loss and career paths.
• Ensure AI augments people’s work rather than being perceived only as a cost-cutting tool.
• Embed AI into core operations in ways that make day-to-day work easier and more meaningful.

In short, AI returns depend as much on human capital strategy as on the technology stack. Prioritizing workers’ trust is not a “soft” issue; it is central to realizing value from AI investments.

The typical AI investment pattern focuses on two ends of the AI stack:

• Infrastructure (bottom of the stack): compute, storage, networking, and related cloud services.
• Applications (top of the stack): user-facing tools and interfaces that wrap AI models into products and workflows.

Between these layers sit the data layer (where data is processed) and the model development layer (where models move from experimentation to practical use). While these are important, the current stack-based approach often overlooks:

• How employees actually adopt AI tools in their daily work.
• How workflows and roles need to adapt over time.
• How to secure worker buy-in and trust from the outset.

The result: many organizations remain stuck at the pilot stage of AI implementation, and less than 40% report profits from their AI investments.

To change this, leaders need to move from a narrow, stack-only view to a holistic AI strategy that:

• Plans for long-term organizational change, not just short-term pilots.
• Builds AI into core operations and cross-functional workflows.
• Integrates worker experience, training, and feedback into every phase of AI deployment.

In other words, the missing layer is not just technical—it’s organizational design and workforce engagement.

Many companies are not seeing expected productivity gains from AI because workers either don’t find the tools useful or fear they will lead to job losses. To reimagine AI as a shared opportunity rather than a threat, leaders can focus on three practical moves:

1. Develop frameworks for shared productivity gains
   With most AI-driven productivity gains expected in the next three to five years, it helps to clarify how those gains will be shared. Today, many organizations are reinvesting AI benefits into:
   • Innovation
   • Data infrastructure
   • Workforce upskilling
   Spelling this out gives employees confidence that their contributions to AI-enabled productivity will be recognized and that they will have a place in the future organization.
2. Communicate transparently about job impact
   If AI is likely to change headcount or skill requirements, leaders should be upfront about:
   • Which roles may be reduced, reshaped, or created.
   • What new skills will be needed and how employees can acquire them.
   • Where AI is intended to augment rather than replace human work.
   Clear, consistent messaging reduces uncertainty and builds trust.
3. Include workers early in the AI design process
   Currently, almost 50% of C-suite leaders say they would not involve nontechnical employees in early AI development stages such as requirement gathering and ideation. This is a missed opportunity.
   • Bring domain experts and everyday users into the “lab” to test and shape tools.
   • Use their feedback to make AI systems more inclusive and practical.
   • Signal that AI is being built with them, not just for them.

By combining shared-gain frameworks, transparent communication, and early workforce involvement, organizations can accelerate AI adoption while maintaining trust, safety, and transparency. Over time, this human-centered approach will shape which businesses successfully navigate rapid technological change.