AI Strategy & Business Leadership

You have probably read one of these stories in the last few weeks. An AI agent did something catastrophic, the headline blames the AI, and you file it away as another reason to be cautious. But if you read past the headlines on enough of these stories, something starts to bother you. The AI did the thing. That part is true. But the conditions that made the thing possible were almost always there before the AI showed up. Most AI failures are not AI failures. They are governance failures that an AI happened to expose, and the framing you bring to these stories shapes the work you do in response to them.

Every AI deployment claims to have a human in the loop. Most of those deployments still fail. The phrase has become a compliance incantation: say the magic words, check the box, proceed. It covers so many different arrangements that it tells you almost nothing about whether oversight is actually happening. The replacement is a concept every other piece of critical infrastructure in your business already uses: name a decision owner. Someone accountable for outcomes, with the authority to override and the capability to evaluate. You wouldn’t accept “humans are in the loop” as an answer to who owns your cloud strategy. Why accept it for AI?

Most enterprises are tracking whether AI is being used, not whether it’s working. With 42% of companies abandoning AI initiatives before production and only 29% of executives able to measure ROI confidently, the problem isn’t the technology. It’s the measurement. Here’s what organizations get wrong and a practical framework for connecting AI spending to actual business outcomes.

AI is following the same adoption pattern as every major enterprise technology wave before it: innovation, sprawl, standardization, control. The only difference? What took twenty years is happening in less than two. Most organizations are already deep in the sprawl phase. Here’s how to recognize the pattern and get ahead of it.

AI deployment is accelerating faster than the rules meant to keep it in check. With hard regulatory deadlines arriving, corporate customers demanding governance ahead of legislation, and agentic AI outgrowing existing frameworks, 2026 is shaping up as the year that determines whether oversight catches up to innovation or falls permanently behind.

Most businesses don’t need custom AI. Off-the-shelf tools will serve them just fine. But for organizations whose competitive advantage depends on unique workflows and proprietary data, custom AI may be the only path to results that competitors can’t replicate.

Most LLM advice focuses on what to type. But the real issue isn’t your wording; it’s understanding how these models decide what to say. This article introduces 22 patterns for getting past polite nonsense and toward genuinely useful AI interactions.

Not every AI project succeeds… and that’s okay. What’s not okay is refusing to recognize when a project should be killed. With 95% of AI pilots failing to deliver measurable impact and 42% of companies abandoning most of their AI initiatives in 2025, knowing when to pull the plug is one of the most important ROI skills an organization can develop. This article provides a practical framework for distinguishing between projects that need more time and those that should be terminated.

With 42% of companies abandoning AI initiatives in 2025 (up from 17% the year before), the difference between success and failure often comes down to how comprehensively you measure value. This article provides a practical framework for combining multiple ROI methods into a balanced scorecard that captures the complete picture of AI value across financial, customer, process, and learning dimensions.