The Model Obsession Is Killing Your AI Projects
I’ve watched dozens of companies burn through millions on AI initiatives that went nowhere.
The pattern is always the same. Executive reads about GPT. Calls emergency meeting. Declares company must “implement AI” immediately. Team scrambles to evaluate models. Picks one. Builds something. Nobody uses it.
Sound familiar?
Here’s the uncomfortable truth: your AI strategy is backwards. You’re starting with the solution and hunting for problems. That’s not strategy. That’s expensive theater.
Why Model Selection Is a Distraction
Let me be blunt. The difference between GPT, Claude, Gemini, or whatever launches next week? For most business applications, it’s marginal.
Yes, I said it.
These models are commoditizing faster than anyone predicted. The technical gaps shrink monthly. Yet companies spend six months in “evaluation phases” comparing benchmark scores that have zero correlation with actual business outcomes.
While you’re debating which model scores 0.3% higher on some academic test, your competitors are shipping products that solve real problems.
The model is not your competitive advantage. Your workflow is.
Workflow First: The Counterintuitive Approach
Here’s what actually works.
Forget AI exists for a moment. Walk into your operations. Watch people work. Ask annoying questions.
Where do things get stuck? Where do humans do repetitive cognitive labor that makes them want to quit? Where does information die in someone’s inbox? Where do decisions wait days for data that should take seconds to compile?
Those friction points? That’s where AI creates value.
Not because the technology is impressive. Because it removes real pain from real processes.
The Workflow Audit Nobody Wants to Do
I get it. This is boring work.
No executive gets excited about mapping approval chains or documenting how the sales team qualifies leads. There’s no press release in “we spent three months understanding our own business.”
But here’s what happens when you skip this step:
You build an AI chatbot nobody asked for. You automate a process that shouldn’t exist. You create new problems while solving imaginary ones. You generate impressive demos that collapse under real world conditions.
The companies winning at AI? They’re doing the unglamorous work first.
They map every step of critical workflows. They quantify the time and cost at each stage. They identify where human judgment adds value versus where it just adds delay. They find the seams where AI can slip in without disrupting what already works.
What This Looks Like in Practice
A logistics company I worked with wanted to “use AI for customer service.”
Instead of spinning up a chatbot, we spent three weeks studying support tickets. Turns out 60% of inquiries were people asking where their package was. Not complex questions. Just tracking requests.
The AI solution? Not a conversational agent. A simple automated response system that pulled tracking data and sent proactive updates before customers even asked.
Implementation took two weeks. Support volume dropped 40%. Customer satisfaction went up.
No fancy model required. Just workflow understanding applied correctly.
The Three Questions That Matter
Before you evaluate a single AI vendor, answer these:
First: What specific workflow creates the most friction for your highest value activities?
Second: What would change if that friction disappeared overnight?
Third: Is the bottleneck actually a technology problem or a process problem wearing a technology disguise?
That last one catches people. Sometimes the answer is “we need better training” or “we need to fire someone” or “we need to stop doing this entirely.”
AI can’t fix broken processes. It just breaks them faster.
The Model Becomes Obvious
Here’s the beautiful part of working backwards from workflow.
Once you know exactly what you need AI to do, model selection becomes trivially simple. You need specific capabilities for specific tasks. You test against those tasks. You pick what works.
No philosophical debates. No analysis paralysis. No expensive proof of concepts proving concepts nobody cares about.
You ship something that matters because you started with something that matters.
Stop Chasing Shiny Objects
The AI vendors want you focused on models. That’s where their differentiation lives.
Your differentiation? It lives in your workflows. In how you actually create value. In the specific ways your business operates that no competitor can copy because they don’t even understand them.
Start there. The right model will follow.
Or keep doing what everyone else is doing. Keep starting with technology and searching for applications. Keep wondering why your AI initiatives produce impressive demos and zero business results.
Your call.