The story is familiar. Pilots are everywhere, and most large enterprises now have teams testing AI tools across functions. Yet results remain uneven. Many organizations are still trying to turn experimentation into performance.
What separates the firms that have found value with AI from those that are still chasing it? It’s not access to technology—after all, the tools are available to everyone. The difference lies in how people and systems adapt together. Value depends on how executives sponsor the shift to AI and how leaders and teams choose to work.
AI transformation is a technical challenge, clearly, but it’s also a human one. The companies that are pulling ahead treat it as a shared effort—embedding new capabilities into the ways people collaborate, learn, and lead.
From Doing More to Doing What Matters
Companies that are finding value have learned a key lesson: less is more. They’ve stopped chasing every automation use case and instead selected a few complex, high-value workflows—in some cases ones that have been problematic in the past—and focused on improving those processes with AI.
That kind of focus builds momentum. Working to solve a hard process forces teams to get serious about data quality, decision logic, and accountability. Modularity is important, too, to build flexibility into the approach. This commitment requires people to align on what better actually means and to measure results in terms of real-world business effects. And focusing on doing fewer things but doing them well creates the foundation for scale.
The Human Factor
Although models, infrastructure, and data are vital, success still comes down to people. Every company has to adapt to emerging technology, and achieving optimal results always comes down to understanding the nuances of applying the technology to the business’s needs.
This is a critical area. The divide between what’s possible with AI and what people in an organization are ready—or willing or comfortable—to do with the technology determines whether AI drives value or dies in pilot mode.
That’s why the best leaders treat people as their first focus. They make it clear that everyone has a role to play—and they mean it. Executive sponsorship goes a long way toward reassuring workers. For example, the share of employees who feel positive about GenAI rises from 15% to 55% when leadership shows strong support for it.
Many leading companies have adopted executive guidance along these lines and are thriving on collaboration between humans and AI. Four human roles have emerged that are key to making this collaboration pay off.
Shapers: Setting the Direction
Shapers decide where AI makes a difference. They are the big thinkers who connect business goals to technology opportunities and define what success looks like in measurable terms.
They set priorities, decide what gets scaled, and ensure that resources line up in furtherance of desired outcomes. Without clear direction, AI efforts tend to drift. But when shapers frame what’s at stake for the workforce, teams know why AI matters and why they are building new workflows and services.
Builders: Turning Vision into Reality
Builders make ideas real. They are the product experts, engineers, and analysts who design and test solutions that fit into the workflow.
They focus on effective functionality: data that refreshes correctly, models that identify and address substantive targets in explicable ways, and outputs that users trust. In many organizations, they are responsible for proving that the new ways of working can outperform the old.
The best builders understand both the business and the technology. They solve problems quickly and push teams to learn by doing.
Consumers: Making AI Part of Everyday Work
Consumers bring AI into daily operations. They are the managers, analysts, and members of frontline teams who use AI tools to make smarter calls, not just faster ones.
Adoption takes time, but proof spreads through results. A team that uses a copilot to close its books faster or forecast more accurately doesn’t need convincing—it needs more of what works. Trust builds through outcomes that people can see.
Tech + Us: Harness the power of technology and AI
Stewards: Keeping Innovation Responsible
Stewards make sure that progress doesn’t come at the expense of integrity and ethics. They manage risk, compliance, and governance so that innovation scales safely.
Their role is to give structure to experimentation—model reviews, data lineage, bias testing, and ongoing monitoring—without slowing things down. The best governance is built into the design, not bolted on later. When accountability and creativity sit side by side, trust follows naturally.
A Shared Effort
Few people in an organization play a single role at all times. A leader might act as both shaper and steward; a builder might also be a consumer of another team’s tool. The point isn’t to assign titles. It’s to create ownership.
When people see how they are contributing to the work, AI stops being a project and starts being part of how the company runs. That’s when transformation becomes real.
Companies nurture a sense of ownership by building fluency across the enterprise. People test ideas, learn what works, and share what they find. Early adopters tinker, and then teams start to standardize. Over time, good practices become normal practices. AI becomes permanent, not a novelty.
Leaders accelerate this process by giving people room to explore in sandboxes or shared forums where learning spreads laterally, not top-down. As fluency grows, confidence follows.
Looking Ahead
Success depends on how fully each participant commits to the transition. Everyone has a role to play. Some will shape strategy and set the direction. Others will build new tools, integrate them into the workflow, or identify fresh ways to use AI responsibly.
Companies that empower their people to shape, build, use, and steward AI will move faster and with more conviction than the competition. Together, they will determine how deeply AI takes root in the organization.
