For years, I watched boardrooms stall on decisions they didn’t fully understand. AI was one of them. The same executives who championed “digital transformation” were suddenly mute when the conversation shifted from cloud migration to cognitive automation. And you know what? I get it. AI doesn’t just promise productivity! it threatens exposure. It’s a mirror, not a hammer. It reflects every inefficiency, every unresolved decision, every contradiction in your workflow.
You see, the moment AI enters the room, it starts asking uncomfortable questions. Questions like: “Why do you have four versions of the same customer record in different systems?” “Why does this approval process involve six humans and a PDF form?” “Why do you store more data than you analyze?” And that’s just the surface. The deeper truth? Most companies aren’t scared of AI because it might not work. They’re scared because it will.
Let me start with the basics: data. Everyone says they’re data-driven. Until AI shows up. Then suddenly everyone remembers their warehouse is full of half-populated records, legacy fields nobody uses, and Excel files with filenames like “final_FINAL-v3.xlsx”. When the SEC dropped $600M in fines in 2024 for recordkeeping failures. THAT actually happened… it wasn’t just a regulatory crackdown. It was a signal that data integrity isn’t optional anymore. It’s existential. AI doesn’t tolerate garbage inputs. It amplifies them.
But cleaning your data is just the beginning. The real obstacle is people. You can have the best LLM on the planet… it won’t save you from the politics of middle management. Because that’s where innovation goes to die. Not in strategy decks. In fear. In fear of being blamed if the AI screws up. In fear of looking obsolete next to a chatbot that can summarize a 40-page report in 3 seconds.
I’ve watched VPs avoid decisions for quarters waiting for a standard, a competitor case study, something. And when you dig beneath the surface, it’s always the same issue: career risk outweighs company opportunity. It’s a perverse equation. And it’s costing businesses millions.
IBM Watson Health was supposed to change medicine. It had billions in funding, partnerships with hospitals, and the power of machine learning behind it. And it collapsed. Why? Because they overpromised and underdelivered. Because the stakeholders weren’t aligned. Because the product couldn’t deliver value in the complex, real-world context of clinical decision-making. Buried in that failure were lessons nobody wants to confront: AI requires humility, scope discipline, and relentless execution. You need to crawl before you automate.
Then there’s the legal fog. Everyone heard about The New York Times suing OpenAI. Allegations of unauthorized data use. Suddenly, every company started asking, “If we use this model, do we own the output?” “Can our data leak back into the model?” “Can we even audit how this thing makes decisions?” The lack of clarity here isn’t just a legal issue. It’s a trust issue. And trust is the currency of enterprise adoption.
But here’s the punchline. Most of those questions? They should already be asking them of their existing systems. Legacy ERPs don’t explain how pricing logic works. CRMs don’t always disclose where a lead came from. Yet nobody panics. Because legacy feels safe. Familiar. It doesn’t challenge identity.
That’s where AI hits hardest. Identity. It forces every department, every role, every process to ask: what’s the actual value we’re providing here? If a model can write your outreach emails, what’s marketing doing? If it can summarize call notes, what’s the role of your BDR? If it can classify support tickets, does tier 1 support even need to exist?
The existential crisis isn’t in the technology. It’s in the mirror AI holds up.
So what do you do with that?
You build a data foundation. Like it’s a product. Because it is. You create a multi-disciplinary AI council with legal, operations, product, security at the table. Not a steering committee. A strike team. You ditch proof-of-concepts, they’re just theater. You run proofs of capability. You don’t bet the business. You find the repetitive, high-volume, low-variance decisions and let AI eat those first.
And maybe most importantly you start building organizational courage.
Because fear is natural; But inaction is a choice.
AI isn’t here to replace leaders, It’s here to demand better leadership.
And the longer you wait… the louder that mirror gets.