Despite massive investment in AI and system transformation, change is slow to come for many.

Decades of digital transformation efforts prove that technology is not the sole differentiator. Whether it’s enterprise resource planning (ERP), cloud, or now AI, success depends less on the technology itself and more on how the workforce is connected to the change process.

According to EY, 34% of companies expect to spend over $10 million on generative AI next year. Meanwhile, 54% of senior business leaders report feeling that they are failing as leaders amid AI’s rapid growth, with half indicating a decline in companywide enthusiasm for AI integration and adoption.

And what are executives getting for the investment and stress? Not much. McKinsey cites just 19% of executives reporting revenue increases of more than 5%.

Companies need better ideas on how to effect change. And employees have a closer sense at the grass roots of the organization on where the leverage is. Give them a voice in innovation, build shared ownership and place investments where they will be most helpful, and least resisted.

Why employee buy-in matters more than ever

This is not a call for executive leaders to step aside in AI deployment. Academic research on digital transformation and change management highlights the trade-offs between bottom-up and top-down approaches. What’s clear is collaboration involving all levels of the organization is crucial for lasting, successful transformation.

Top-down change is decisive, with clearly articulated goals – but it can backfire. A little more than 50% of workers are worried about the impact of AI in the workplace, and 32% think it will lead to fewer job opportunities according to the Pew Research Center. With this sentiment, there’s a distinct risk that employees will avoid or abandon tools, resist change, and even sabotage the process. Given this, short-term gains from top-down changes can be short lived.

Having said this, many executives hear the phrase “bottom-up” and may envision an aimless, meandering maze. That doesn’t have to be the case.

Effective change requires a partnership between leaders and employees. Leaders set the vision and connect with employees on the front line to drive innovation in practice.

When executives engage their teams, it drives shared ownership, it becomes “ours” not us vs. them. Organizations with robust two-way communication and involvement achieve significantly better results. For example, employees feel three-times more well prepared to work with AI, and 2.6-times more comfortable using it in their role when leadership clearly communicates the AI integration plan and involves staff.

Renault Group, for instance, opened up its innovation process to all employees and received 570 employee-driven project submissions in one year across categories from AI and process automation to customer experience. The top 10 teams’ ideas were implemented and recognized with innovation trophies, reinforcing to the whole workforce that everyone’s contributions matter in the transformation journey.

What bottom-up innovation looks like in practice

Not every AI use case is feasible. True for both top-down and bottom-up. The Association for Information Systems examined an organization that employed both bottom-up and top-down to generate ideas for AI adoption. It found that top-down approaches were often closely aligned with business goals and that they tended to be more feasible. But the most transformative, breakthrough ideas came from the bottom up – from employees who understood day-to-day challenges and saw new ways to solve them.

There is good evidence that generative AI, in particular, is well-suited for bottom-up process change. Generative AI is flexible, decentralized, and can be used by almost anyone. It can adapt to thousands of small use cases across a business, which is exactly why a bottom-up approach works.

Finding the right mix of vision and execution

To close the gap between AI spending and impact, here’s some practical steps:

• Start with your internal power users. Identify the employees already using AI tools in their daily work and learn what they’re doing right.
• Include employees in decision-making. Invite them to pilot tools, test workflows, and surface pain points. Use that feedback to shape and scale broader deployment.
• Balance quick wins with long-term learning. Deliver early results through pilots, but keep investing in training and cultural readiness so adoption doesn’t stall.
• Make change a shared outcome. When leaders set the vision and employees drive the experimentation, transformation stops being a mandate and builds momentum.

It’s time for leaders to stop treating AI like a technology rollout and start treating it like a people initiative. If you want AI to pay off, make engagement your next investment. The organizations that empower their teams to shape how AI is used will be the ones that will see a return on all that investment.