According to Forbes, boards are stuck in a rut with AI discussions focused on pilots, employee training, and use case exploration without actually changing business trajectories. The article identifies ten critical questions that move AI from “interesting” to “material,” including where companies intend to lead versus follow with AI, how AI accelerates existing strategy versus forcing business model rethinks, and whether freed capacity from AI tools is actually being redeployed to higher-value work. The piece emphasizes that outcome metrics like margin improvement, reduced opex, and shortened cycle times matter more than activity metrics like number of pilots or LLM accuracy scores. Boards also need to assess their own AI literacy gaps and ensure AI strategy isn’t just understood by technology leaders but appears in the language of talent, productivity, and customer value.
From Demos to Business Impact
Here’s the thing about most boardroom AI discussions – they’re happening in entirely the wrong section of the agenda. When AI lives in the “IT section” instead of the strategy section, you’re basically treating it as fancy tooling rather than a potential driver of competitive advantage. The real question isn’t whether you’re doing AI, but whether AI is doing anything meaningful for your business. Are you actually improving margins? Reducing headcount growth? Shortening cycle times? Or are you just collecting impressive-sounding pilot numbers that don’t move the needle?
The Human Barriers Nobody Talks About
This is where it gets uncomfortable. You can have the best AI strategy in the world, but if employees think “this tool will make me redundant,” adoption will stall no matter how compelling the business case. Fear is a massive blocker that most companies underestimate. Then there’s what I’d call organizational blindness – teams insisting there’s “no waste” in their workflows while treating every AI error as unacceptable. The reality is that human bias against machines can sink your entire AI initiative before it even gets started.
Governance in an AI World
From a risk perspective, AI is just another major transformation – only it’s happening about ten times faster than anything we’ve seen before. If AI risks and controls aren’t showing up in your risk register and audit agenda, they’re probably not being managed systematically. Think about it: How are you managing data quality and access control for AI use cases? Testing for bias and model drift? Protecting IP when using external models? These aren’t technical questions – they’re fundamental governance questions that every board should be asking.
The Capability Gap
You don’t need a board full of data scientists, but you do need enough literacy to ask the right questions. When only your technology leaders can describe the AI strategy, that’s a red flag. AI should show up in the language of talent, productivity, and customer value – not just models and platforms. The most AI-ready teams aren’t just good at prompting; they’re good at describing their own work and thinking strategically about where value is created. That’s the real transformation that separates companies that get AI from those that just talk about it.
