A decision every CEO faces when looking at AI adoption is whether to develop custom systems or deploy commercial off-the-shelf software for speed and efficiency.
Here's what I learned from watching companies waste millions chasing AI for AI's sake, and the simple framework that separates winners from wishful thinkers. I've watched too many boardrooms where the conversation goes like this: "What's our AI strategy?" followed by "Let's put AI everywhere and see what sticks.
" That's not a strategy. That's expensive experimentation. The real opportunity lies in treating AI like you would treat any other major investment: ruthlessly focused on ROI, and honest about when to develop a solution for customers versus when to deploy a commercially available capability.If you can't draw a straight line from your AI project to your bottom line, you're not investing, you're gambling. Too many organizations blindly go chasing the "AI goldrush," simply because their competitors are doing the same, without considering whether it belongs there.of organizations now use AI in at least one business function, with many organizations planning to increase AI investment. Yet ambition often outpaces strategy.of businesses say they have a visible, defined AI strategy, despite widespread adoption. This gap between enthusiasm and execution leaves many initiatives without a clear business case. Anchor investments in outcomes that matter and focus on the areas where value can be proven and scaled.A decision every CEO faces when looking at AI adoption is whether to develop custom systems or deploy commercial off-the-shelf software for speed and efficiency. The answer really is simple: If it’s an AI system that will define your value proposition and competitive edge, it should be developed and owned in-house. These systems enhance core products or services and can become true differentiators. If the goal is efficiency or automation, then leave it to the specialists. Proven solutions can deliver results faster, with less risk and more cost-efficiency than building from scratch., for example. The movie and series recommendations presented to you on Netflix’s homepage are based on your viewing habits and the behavior of similar users. That's Netflix’s competitive moat. They built it, own it and continuously refine it because it directly drives subscriber retention and revenue. Now consider finance automation and reporting. Unless you're in the accounting business, automating reporting isn't your competitive advantage; it's table stakes. One automotive manufacturer I know spent 18 months building a custom AI for expense reporting. A specialist-deployed solution would have solved the same problem in 12 weeks at one-tenth the cost and with more effectiveness. This distinction ensures resources are directed where they matter most and avoids unnecessary reinvention.Instead of asking "where do we use AI?" CEOs should focus on three fundamental questions:2. Where can we get quick wins from specialist solutions? Deploy here, as this is where you can prove value quickly, build momentum and enable the AI change efforts.This becomes the real differentiator—not the model, but the data that trains it.For AI to move from experimentation to enterprise impact, CEOs must take the lead in setting direction.1. Drawing The Map Before You Start DiggingWhen strategy is defined at the top, AI adoption becomes part of the wider business vision rather than a series of disconnected projects.Equally, challenge the organization to see what’s possible with AI, and reward "entrepreneurial" behavior that encourages innovation.Leaders must also ground every initiative in ROI from the outset. By linking AI projects to financial returns, efficiency gains or risk reduction, CEOs can hold teams accountable and maintain credibility with boards and investors.Finally, make data governance a C-suite responsibility. It's the foundation that allows AI to scale. High-quality, well-managed data is critical for reliable outcomes, and only executive-level ownership can guarantee the standards required across the enterprise. The CEOs winning the AI game aren't the ones with the biggest AI budgets; they're the ones who know exactly when to develop, and when to deploy.
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