From AI Hype to AI ROI: Part I

Part I: Leading AI Adoption at Scale

This is the first in a three-part series on how enterprise leaders can accelerate AI adoption with practical, IT-aligned approaches that connect real use cases to measurable business value.

Over the past two decades, I have led teams, organizations, and business units in complex enterprise and customer environments. Transformation at scale is rewarding work, but it is never passive. To capture real ROI from AI, leaders need to engage directly: use the tools themselves, learn enough to guide smart experimentation, and build the confidence their teams need to adopt new ways of working. The leaders who do this well will not only reshape how work gets done, but improve the cost, speed, and quality of delivering it.

Challenges:

• The AI ROI debate: Companies need measurable ROI from AI. That pressure shows up in boardrooms, on earnings calls, and in conversations with investors and analysts. The harder part is delivering broad, cross-functional value in a way that changes how knowledge workers operate.
• Connecting back to the Business: The barrier to AI adoption is no longer just the technology. The tools are here, they are increasingly accessible, and the learning curve for non-technical knowledge workers is lower than it used to be. In many organizations, employees are already experimenting faster than the company is operationalizing AI at scale. When that activity stays disconnected from IT, it creates sprawl, security concerns, and fragmented adoption.
• Operator Culture gap: Organizations are struggling not because the tools are missing, but because they have not built an operator culture at scale—one where curiosity is rewarded, experimentation is safe, and learning is treated as part of the job rather than a distraction from it.
• Tech vendors move faster, but speed alone is not the point: Tech vendors often move faster on AI than large enterprises because their products, talent mix, and market expectations push them to. But speed alone is not the goal. Across both tech vendors and enterprises, what matters is setting realistic business goals, improving operational fluency, and working across functions well enough to move priorities forward (governance & guardrails required) without creating unnecessary politics or operational overhead. Keeping it simple is critical to ensuring all employees are on the same page.

Where the Enterprise Is Now

Many large companies are somewhere in the messy middle of AI adoption: trying to figure out how to deliver AI across functions at scale while protecting the core business, keeping employees productive, and maintaining market confidence. This is difficult work. It is also disruptive, and for many employees, unsettling. Fear around job loss and changing expectations can slow productivity and fuel resistance if leaders do not address it directly.

The good news is that transformation can be a powerful forcing function for productivity, learning, and renewed energy across the organization. If you lead a business unit or large organization, this is the moment to engage your teams around technology that can genuinely improve how work gets done.

Leaders who visibly learn, experiment, and give their teams permission to do the same move through resistance faster, build stronger internal capacity, and create better conditions for innovation.

The challenge is not simply defining what AI-first means. It is making it real in your organization by bringing people along, aligning with critical partners such as IT, ops & finance, and applying what you learn in ways that fit and will scale effectively inside your company, operating model, and industry.

How to Execute on ROI in the AI Era

The next two posts focus on execution in large business functions and enterprise environments. They are not just for technical teams. They are about how leaders help knowledge-worker teams adopt AI in practical, IT-aligned ways that improve execution at scale and connect back to business outcomes.

The series continues with two practical follow-on posts.

• Part 2: Enabling Employees: How to help teams in large business functions build AI literacy, applied skills, and responsible tool adoption.
• Part 3: Developing Leaders and Spotting Talent: How to help leadership teams build operating muscle, create alignment, and identify the leaders most likely to scale AI effectively.

Keep in mind:

(1) Applied learning matters: People learn by using these tools regularly in the flow of work. Training helps, but reinforcement matters just as much: manager check-ins, working sessions, action items, mentoring, brown bags, and recognition all help turn early experiments into repeatable habits.

(2) You do not need to build everything from scratch: Most leaders do not need to invent a new center of excellence to get started. Use existing company resources, proven frameworks, and approved tools, then adapt them to your business context. The goal is to make practical progress, not create a massive internal spectacle around AI. I created a guide here that maps top AI resources to roles in corporate organizations, you and you teams may find it helpful.

Takeaways:
Leaders need to go first. AI adoption becomes real when senior leaders use the tools, work with IT rather than around it, and make experimentation safe and relevant to the business. The next two posts focus on the practical side of that work: enabling teams and building the leaders who can scale it.

I hope readers find this helpful, and as always, feedback & insight welcome! /LC