AI Strategy

Most organizations are asking the wrong first question about AI. They ask “what tools should we buy?” when they should be asking “what problems are we trying to solve, and where does AI genuinely help?”

Effective AI adoption starts with governance, not as a blocker, but as a framework that gives teams confidence to experiment. When people understand the boundaries, they move faster, not slower. The organizations getting AI right are the ones that treat it as a leadership challenge, not just a technology one.

At YMCA Canada, I led one of the first enterprise-scale AI pilots using Microsoft Copilot and ChatGPT across a federation of 37 associations. The lesson was clear: the technology is the easy part. The hard part is change management, building AI literacy among leadership, and creating governance structures that work across distributed organizations.

I now advise organizations navigating the same journey, helping them move past the hype cycle and into practical, ethical AI adoption that delivers measurable results.

What is an enterprise AI strategy?

An enterprise AI strategy is a structured plan for where and how an organization uses AI to create measurable value. It goes beyond tool selection to include governance frameworks, change management, leadership literacy, and a clear understanding of which problems AI genuinely helps solve versus where it’s just hype. The organizations getting AI right treat it as a leadership challenge, not just a technology procurement decision.

What does enterprise AI adoption look like for mid-size organizations?

For organizations with 100-500 employees that aren’t tech companies, enterprise AI adoption looks very different from what you read in the press. It starts with governance — clear policies on what data AI can access, which tools are sanctioned, and who’s accountable. Then it moves to identifying high-value use cases, running controlled pilots, and measuring actual outcomes. The technology is the easy part. The hard part is change management and building AI literacy among leadership.

What is leadership AI literacy and why does it matter?

Leadership AI literacy is the ability of executives and board members to understand AI well enough to make informed decisions — not just approve budgets, but ask the right questions and set the right direction. It’s the difference between a board that rubber-stamps an AI vendor proposal and one that asks about data handling, governance posture, and what happens when the model gets it wrong. Organizations where leadership lacks AI literacy make poor AI decisions, regardless of how good their technical teams are.

How do you measure ROI on AI adoption?

Start by defining what you’re actually measuring before you deploy. Track time saved on specific workflows, reduction in manual errors, improvement in response times, or increases in output quality. The mistake most organizations make is deploying AI broadly and then trying to prove value retroactively. Pick 2-3 high-value use cases, establish baselines, run a controlled pilot, and measure the delta. If you can’t measure it, you can’t justify scaling it.

What are the biggest mistakes organizations make with AI adoption?

Three common failures: adopting AI without governance (staff paste confidential data into public tools within days), blocking AI entirely (staff use personal accounts with zero oversight anyway), and chasing the latest model instead of solving specific problems. The organizations that succeed start with the question “what problems are we trying to solve?” rather than “what AI tools should we buy?” They treat AI as a leadership challenge, not a technology one.