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. Many of the new roles will be in logistics, software technology, and healthcare, while routine, function-based jobs are more at risk.

For companies, this means AI is not just a technology decision; it’s a people and sustainability decision. Organizations that treat AI purely as an infrastructure or application investment often struggle to see returns. In fact, less than 40% of companies that invest in AI have seen profits so far, and many remain stuck at the pilot stage.

Prioritizing workers—by addressing their concerns, involving them in the process, and supporting their development—helps:

• Build trust and reduce resistance to AI tools
• Increase adoption and effective use of AI in daily workflows
• Align AI projects with real operational needs and domain expertise
• Support long-term organizational change, not just short-term pilots

In short, to realize meaningful returns on AI investments, leaders need to reimagine AI as a workforce transformation initiative, not just a technology rollout.

Many organizations have invested heavily in AI, but most are still at the pilot stage, and fewer than 40% report profits from their AI initiatives. A key reason is how they structure their investments across the AI stack.

The AI stack typically includes:

• Infrastructure: compute, storage, networking
• Data layer: data processing and cloud services
• Model development: moving models from experimentation to practical use
• Application layer: interfaces and tools that make AI usable for end users

Historically, companies have focused their spending at the bottom (infrastructure) and the top (applications) of this stack. What’s often missing is a holistic view that includes:

• How employees will actually use AI tools in their workflows
• How roles and processes will change over time
• How to secure worker buy-in and address concerns about job loss

Another major factor is that workers often don’t find the tools useful or feel they were designed without their input. When employees are not involved in shaping and testing AI solutions, adoption suffers and productivity gains fail to materialize.

To improve returns, leaders need to:

• Embed AI into core operations, not just isolated pilots
• Plan for long-term organizational change, not quick wins
• Design AI with domain experts and end users involved from the start

By rethinking AI as an integrated business and workforce strategy, companies can move beyond pilots and start to see more consistent value.

Given the scale of AI’s impact on jobs and workflows, earning employee trust is essential. Workers need to feel that they have a voice in the transition and that AI is being used to support, not simply replace, them. Leaders can take several practical steps:

1. Develop frameworks for shared productivity gains

• Most AI-driven productivity gains are expected to arrive within the next three to five years.
• Creating clear frameworks that show how these gains will be shared—for example, through reinvestment in upskilling, better tools, or career development—helps reassure employees that they benefit from AI, not just the company.
• Many organizations are already reinvesting AI gains into innovation, data infrastructure, and workforce upskilling, which can spread AI’s impact across multiple workflows rather than focusing on a single efficiency play.

2. Communicate transparently about job impacts

• If job losses or role changes are expected, be upfront about new skill requirements and potential headcount changes.
• Where AI is meant to augment rather than replace people, say so clearly and back it up with training and role design.
• Transparent communication reduces uncertainty and helps employees plan their careers.

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.
• Bringing domain experts and everyday users into the lab early—during testing and iteration—makes AI systems more inclusive and more relevant to real work.
• This involvement also signals respect for employees’ expertise and builds trust in the tools being deployed.

By reimagining AI implementation as a partnership with employees—rather than a top-down technology rollout—leaders can increase the pace of adoption while maintaining trust, safety, and transparency. In the long run, companies that prioritize human capital strategies will be better positioned to navigate rapid technological change.