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Who actually owns AI transformation?
Business & Technology
Fast CompanyEN
September 23, 2026

Who actually owns AI transformation?

If you ask almost any professional who owns their organization’s AI transformation, they’ll likely point to a role at the top of the org chart or a team charged with the work. While those answers aren’t wrong, they’re incomplete. Companies are spending millions on centralized structures to drive AI transformation and are still struggling to show meaningful results. Leaders can set direction, but no executive can see the thousands of small workflows that make up company operations. The people who understand a process well enough to redesign it are usually the ones executing it every day. That’s why we stopped asking how many people are using AI and started focusing on how people are using it to rethink how work gets done. There’s a big difference. A user might leverage AI to summarize a document or draft an email. A builder looks at a workflow and asks, “Why are we still doing it this way?” and then changes it. EMPLOYEE-LED INNOVATION Builders are not always engineers and likely don’t report to the chief AI officer or another executive. It is a mindset, not a role. Builders generally share three characteristics: They are close to the work, naturally curious, and proactively improve workflows without waiting for permission. A builder might be a salesperson redesigning follow-up to increase win rates, an accountant building automation to flag anomalies before month-end close, or an analyst eliminating transactional work so they can spend more time bringing insights to the business. Leadership’s role is to chart the AI strategy and provide training, tools, guardrails, and agency to allow the people who know the work best to transform the work. Having builders across teams and levels empowers all employees to scale innovation faster and more effectively than any top-down mandate ever could. While teaching everyone to be a builder can sound a bit daunting, we’ve done this before. Think of the internet. We didn’t teach a small group of employees to use the internet. We provided access, training, and guardrails. Eventually, it’s how work got done. LEAD WITH GUARDRAILS This is where leaders can inadvertently get in the way. In an effort to manage the very real risks that come with AI, we can create so many approval processes that employees stop experimenting altogether. Instead, set the non-negotiables around security, privacy, approved tools, and human oversight. Then give people room to build. But permission alone isn’t enough. People need to see what good looks like. Here, peers can be far more powerful than a leadership mandate. Give builders places to show what they’ve tried, what worked, and what didn’t. A colleague showing how they eliminated three hours of work from their week will do more to make AI approachable than another leadership presentation about its potential. Historically, our job as leaders was to identify and decide what needed to change. With AI, the model is shifting. Our job as leaders is to create an environment where employees who know the work best redesign work themselves. Our job now is to set direction, establish boundaries, remove barriers, and then recognize what is working so it can scale. MAKE AI GOOD FOR BUSINESS AND EMPLOYEES If we’re asking employees to use AI to make the company more productive, we should also invest in making these skills beneficial to employees’ careers. Companies should absolutely expect improved productivity, better financials, and better client outcomes from these investments. But employees should also come out of this transformation with new skills that help them continue thriving in their careers. We have a responsibility to teach people skills they can carry forward, creating new career opportunities as roles evolve. This approach becomes more about giving employees the opportunity to help identify how AI can help shape their jobs. Employees need to feel and see the personal and professional benefits AI can provide their careers. TRACK METRICS THAT MATTER As organizations implement AI, many are measuring activity instead of outcomes. While log-ins, licenses, and usage can tell you whether people are accessing the tools, they can’t tell you the impact. If a redesigned workflow gets a product out the door five days faster, that’s meaningful. If a project requires 40% fewer hours and improves margin, that’s meaningful. If teams are creating capacity that can be redirected to clients or higher-value work, that’s meaningful. The most meaningful metrics are already on your company or board scorecard. They are things like revenue growth, margin expansion, client NPS, staffing efficiency, and productivity. SO WHO OWNS AI TRANSFORMATION? Leaders own the strategy. Technology teams own platforms and guardrails. But no one person or team can redesign every workflow across an organization. That’s where builders come in. They’re the ones thinking about how to leverage AI to make work better. And ultimately, that’s the transformation that matters. Tanya Moore is chief people officer of West Monroe.

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Curated from Fast Company. Classified as Business & Technology by AI, which sometimes makes mistakes.

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