Insights

What’s the winning AI strategy: build, buy, or borrow? That’s the wrong question. The real question is: when should you build, when should you buy, and when should you borrow? In real AI strategy, there is rarely a single “right” option. The organizations that get this right don’t choose one path — they combine them deliberately. With traditional software, this is usually a cost and speed decision. With AI, it becomes a control, risk, and data decision — and those trade-offs have long-term consequences. ...

Some of this will feel familiar. The parts that don’t are the parts that cost you. Every organization running an AI program has someone in the room saying this is just the next digital transformation. They are partly right, and the part they are right about is the surface. The part they are wrong about is the expensive part. The management challenges do repeat. If you have led a transformation before, you have already handled them — they are covered at the end. ...

“You’re not choosing whether AI becomes part of your organization. You’re choosing how intentionally and how well.” Let’s skip the debate about whether AI is coming. It’s already here. Every organization I speak to is doing AI work in some shape or form. Maybe it’s a pilot. Maybe it’s a vendor tool already deployed. Maybe it’s a team experimenting quietly without a formal program. Maybe it’s all three simultaneously. The significant shifts we’re seeing now aren’t the beginning of AI. They’re the maturation of it. The question was never whether. It’s always been how. ...

“AI tools don’t create value. Organizations that are ready to use them do.” The biggest AI fear today? Justifying the investment. Yes — fear. Whether you acknowledge it or not. Every organization is investing in AI. Tools are bought. Pilots are running. Vendors are contracted. And somewhere in every boardroom, every leadership team, every program office, there’s a quiet anxiety nobody is saying out loud. Are we going to be able to justify this? ...