The tech industry keeps calling this an "adoption problem." It's not. It's a design problem.
The "Add a Chatbot" Strategy
Here's what happened over the past two years: every software company panicked about AI. They had working products. They had happy customers. But if they didn't have "AI" in their pitch, they were obsolete.
So they did what made sense on a tight timeline: they added chatbots to existing products. Put a text box in the interface, wire it to GPT-4, and ship it. Call it "AI-powered."
Microsoft did this with Office. Salesforce did it with their CRM. ServiceNow did it. Everyone did it.
And now we're seeing the results.
Why It Doesn't Work
The problem isn't the AI. GPT-4 is remarkably capable. The problem is that just having a chatbot interface doesn't make the AI useful.
Think about what happens when a user asks a question. They have expectations based on their business. They're asking because they need something specific. But the AI doesn't know:
- What context matters for this question
- What data is relevant versus noise
- What normal looks like in their business
- What answer would actually be useful
- What they're really trying to accomplish
So it gives an answer. And the answer might be technically correct but operationally useless. Or it pulls the wrong data. Or it misses what actually matters.
The user tries a few more times. Gets inconsistent results. And stops using it.
This is what "struggled to reliably pull data from other applications" means. It's not that the data access failed technically. It's that even when it worked, it didn't give them what they needed.
The Missing Piece: Direction
AI is a tool. An incredibly powerful tool. But like any tool, it needs to be directed properly.
You can't just point a chatbot at your data and expect it to know what matters. You have to tell it:
- What patterns are important in your business
- What thresholds indicate problems
- What context it needs to give useful answers
- What actions are possible when it finds something
- How your metrics relate to each other
This direction can't come from the user typing better prompts. Most users don't even know what context the AI is missing until it gives them the wrong answer.
The direction has to be built into the system. You have to feed the AI the information that really matters—not just access to all your data, but understanding of what that data means in your business.
Why This Is Hard to Bolt On
You can't add this after the fact. You can't take a generic chatbot and make it understand your business by configuring a few settings.
The whole architecture has to be designed for it. The AI needs to be:
- Fed business context from the start, not just raw data
- Directed toward what matters in your operations
- Connected to the knowledge that exists in your operators' heads
- Built to learn your specific patterns, not generic ones
This is why the "add a chatbot" strategy fails. You're taking AI that was designed to be generic and trying to make it specific. You're hoping it'll figure out what matters. It won't.
The Numbers Don't Lie
Microsoft 365 has 440 million users. Copilot converted 1.8% of them.
Gartner found 60% of enterprises started pilots. Only 6% finished them and planned to expand.
These aren't "early adoption challenges." This is widespread rejection after trying the product.
When 94% of companies that pilot your AI decide not to deploy it, that's not a marketing problem. That's a product problem.
The product problem is this: the AI doesn't know what users expect because it has no context for what they're asking.
What Actually Works
The companies succeeding with AI aren't the ones with access to the best language models. They're the ones who solved the direction problem.
They built systems where the AI is fed the right context from the beginning. Where it's directed toward what matters. Where business knowledge is encoded into how it operates, not left for users to provide in prompts.
This takes more time upfront. You can't just wire up a chatbot API and ship it. You have to understand the business problems, encode the relevant context, build the direction into the system.
But it works. And "works" means users trust it, use it daily, and get value from it.
The Real Lesson
Microsoft lowering sales targets is proof of something important: you can't just slap a chatbot on existing products and call it AI.
The chatbot is not the AI strategy. It's just an interface.
The AI strategy is about direction—feeding the AI what it needs to know, building in the context that makes answers useful, designing systems where the AI understands what users expect.
Without that, you just have a very impressive tool that doesn't know what job it's supposed to do.
And tools that don't know their job don't get used. No matter how technically impressive they are.
The first wave of "AI-powered" products is failing because companies focused on adding chatbots instead of building proper AI systems. The second wave will be built by people who understand the difference.
