AI fluency won’t save you from bad tech leadership
Every failed AI rollout has one thing in common: a C-suite that thinks "GPT" is a strategy.
You can’t automate your way out of incompetence.
That’s the part nobody wants to say out loud at the offsite. Especially not in front of the VP who just greenlit another “AI Center of Excellence” staffed with interns, vendors, and vibes.
But here’s the uncomfortable truth: most AI failures aren’t technical. They’re executive.
We were promised a revolution
Smarter workflows. Automated insights. Decision-making without the drama. AI was supposed to declutter complexity, not layer more on top.
Instead, most companies bought themselves a new kind of chaos—wrapped in APIs, managed by consultants, and sold as progress.
Yes, the tech is impressive. But the implementations? They’re museum exhibits. Expensive. Beautiful. Non-functional.
Why?
Because enterprise AI isn’t failing due to lack of fluency. It’s drowning in bad leadership.
When fear runs the roadmap
Executives are terrified—of looking out of touch, of missing “the next cloud moment,” of being out-innovated by competitors who just announced a GPT-powered Slack bot.
So they overcorrect. They issue vague mandates. They greenlight every vendor pitch that includes “transform,” “accelerate,” or “enterprise-ready LLMs.”
And they stop asking hard questions. Because hard questions slow things down—and nobody wants to be the leader who “missed the moment.”
The result? AI-as-theater.
Real talk on unicorn vendors
Writer: Raised over $100M to “govern enterprise AI.”
What it actually governs: your ability to overpay for polished nothing.
It promises brand-safe content at scale—what it delivers is the corporate equivalent of Clippy in a suit.
Jasper: Claimed it would redefine enterprise marketing.
What it redefined was how fast a generative AI darling could pivot from “game-changing” to “who’s still using that?” once OpenAI made their moat irrelevant.
[Insert-Logo-Here AI]: You’ve seen the type.
A shiny LLM interface with “enterprise-grade” slapped on like SPF 15 on a sunburn. They pitch security, compliance, and “workflow integration”—and hope you don’t ask what their product actually does beyond chat.
This whole class of vendors isn’t building for durability.
They’re building for demo day.
And they thrive in orgs where innovation is measured by how many AI logos you can fit into one slide—not whether anyone’s job got meaningfully better.
They solve for buzzword alignment, not business alignment.
And until someone in the room says, “Do we really need this?”—they’ll keep eating your budget while your actual problems fester.
The middle manager multiplier effect
Let’s not just blame the C-suite. Middle managers are often the accelerant.
Desperate to be seen as forward-thinking, they treat AI procurement like a status game. The more logos on your internal vendor list, the more “innovation points” you score.
Pushback? Seen as obstruction. Realistic scope? That’s a “lack of vision.”
So you get Frankenstein stacks—Writer on top of Google Workspace, Jasper feeding into Salesforce, some RAG setup no one knows how to maintain. None of it connects. None of it scales. But all of it demos well.
By the time the CTO realizes the pilot is a money pit, it’s already in the annual report.
The real problem?
Nobody owns the outcome.
AI teams don’t talk to product. Product doesn’t talk to ops. Legal and compliance are looped in two weeks before launch. Everyone wants the headline. No one wants the operational debt.
And through it all, the execs keep asking the wrong question:
“How are we using AI?”
Not: “What problem are we solving—and is AI the right lever?”
So you want to “do AI”? Try passing this audit first
Before you throw another million at a unicorn vendor or spin up a “CoE” with a catchy acronym and no clear purpose, ask yourself:
1. Do we have clean, centralized data?
If your data lives in seventeen SaaS platforms, three rogue Notion workspaces, and a spreadsheet called final_v27_reallyfinal.csv, stop. You’re not ready for AI. You’re ready for data therapy.
Fail condition: Your LLM is hallucinating because you fed it garbage, and now your VP of Sales thinks the chatbot is gaslighting them.
2. Are domain experts actually involved?
AI should augment humans, not ambush them. If the teams whose workflows are being “reimagined” weren’t even consulted, you’re not building transformation. You’re building resentment.
Fail condition: A marketing AI tool that no marketer uses—because it doesn’t solve a single problem they actually have.
3. Is success defined by business outcomes—or demo flair?
“Look what it can do!” is not a KPI. If your metrics are based on usage instead of value, congrats: you’ve gamified vendor lock-in, not created leverage.
Fail condition: AI-generated content that floods internal docs, creates version confusion, and adds rework—while usage graphs climb and productivity stalls.
4. Can we support the post-deployment mess?
LLMs break. Integrations decay. Prompts rot. If you don’t have people ready to maintain, govern, and evolve the system, you’re not automating. You’re time-bombing.
Fail condition: Your chatbot starts answering HR queries with training data from Reddit—and nobody notices for a month.
5. Do we have a kill switch?
Not just a literal one, but a cultural one. Can someone say, “This isn’t working,” without being labeled “anti-innovation”?
Fail condition: A doomed AI pilot that survives quarterly reviews purely because “it’s too late to turn back now.”
If you failed two or more of these?
You don’t need an AI roadmap. You need a reckoning.
AI doesn’t reward speed. It rewards clarity. And until your leadership is willing to slow down, challenge vendor narratives, and face the operational truth—you’ll keep mistaking motion for progress.
Because the future of AI in the enterprise isn’t defined by who bought the most tools.
It’s defined by who had the guts to say:
“We’re not ready. Let’s fix that first.”