From AI Hype to AI ROI: Part III

Part III: Developing Leaders and Identifying Top Talent

Building the mindset, muscle, and operating model to scale AI

Leadership teams need a different level of engagement. AI is reshaping organizational design, team structure, service delivery, and the way business functions work together. For this work, I prefer smaller groups and targeted sessions. Because the downstream impact can be significant, I check in with peers across finance, legal, IT, and HR early enough to create alignment without slowing everything down. The best leaders are part-time coaches, and that mindset matters here.

How to structure it: I typically do this work with direct reports, pulling in high-potential junior employees, and specialists, as it diversifies and improves output. Ensure you are standardizing useful learnings so other orgs cross-company can map to and absorb them. Smaller groups move faster. You can test ideas, learn what works, and have leaders carry the right practices back into their teams.

Tooling: The tools here often focus on planning, forecasting, customer onboarding paths, operational decision-making, and ROI reporting across functions such as finance, supply chain, operations, product, and sales. Sometimes the first step is simply asking IT or engineering for a practical demo applied to a real use case your business is solving for; at the end of the day tooling is only successful if it can deliver a consistent, high-quality output.

Use cases: This work becomes especially valuable when the business model is shifting, new technologies are being introduced, or leaders need to streamline how their organization runs.

• Move faster at scale while maintaining quality: Review where barriers exist, where automation can help, and where workflows can be simplified or redesigned.
• Improve experiences for internal and external customers: Walk through the handoffs across functions and look for places where AI can improve speed, consistency, and quality.
• Reduce OPEX and streamline functions: Run focused reviews periodically to identify what is not working, where costs are rising, and which improvements are worth scaling.

When leaders across functions model curiosity, practical learning, and disciplined use of AI, adoption improves, teams gain confidence, and ROI becomes easier to track and defend.

Develop the Leaders who Model the Shift

Learning & Development, High Potential Programs & Boot-Camps: When I lead large organizations, I see it as my responsibility to close capability gaps when my teams cannot get the learning they need internally. This often shows up when business leaders need more technical depth or when technical leaders need stronger business and operational fluency. At the VP level, the need usually expands to communication, prioritization, and consensus-building. If your top leaders need new skills, help them get the right training and create enough space for them to use it. That investment creates leverage at scale, and their teams notice it.

The leaders worth investing in are the ones who grok the business and tech aspects, while staying curious about how transformations happen, and devising scalable methods to help their teams, company, and customers succeed.

Engaging & Educating Top Leadership

High-potential programs: These programs can be very effective, but only if leaders have the time and space to participate. For managers through directors, the focus is usually on concepts, application, and solving real business problems in teams. For senior directors and VPs, the focus shifts to why transformation is needed, how to lead it well, and how to navigate the organizational friction that comes with it. The content has to be relevant to the leader’s role and the technology landscape, or it will not stick.

Intensive boot camps: If you want to move toward AI-first in a practical way, consider a short leadership boot camp focused on planning, leading change, and applying the right tools to the right business problems. One useful outcome is having leaders design and run practical learning efforts for their own teams.

Key Takeaways:

Enterprise AI adoption will stall if leaders do not build the muscle to guide it. Start with small groups, relevant use cases, and practical learning. Invest in the leaders who show curiosity, judgment, and the ability to carry new practices back into the business.