Generative AI has ignited a firestorm of investment and experimentation across industries. From chatbots and copilots to marketing automation and internal knowledge assistants, the technology is everywhere. And, it’s sold as a cure-all.
But let’s be honest: most enterprise AI projects fail. Not quietly, either. We’re talking about failed rollouts, wasted budgets, and initiatives that never move beyond pilot mode. Accenture reportedly billed $1.2 billion last quarter in generative AI projects and many of those are still stuck in experimentation, with little to show for it.
So, what’s going wrong?
It’s not the technology. It’s how we approach it.
The AI Mirage: A Field of Proofs, Few Results
There’s a widespread assumption that plugging a large language model (LLM) into your systems will deliver immediate business value. The model knows everything, right?
Wrong.
What LLMs provide is efficiency, not differentiation. If your chatbot answers the same customer questions as everyone else’s, you’re not gaining an edge, you’re just keeping pace. Real value comes from your proprietary content, internal knowledge, processes, and people. And the LLM doesn’t know any of that — unless you teach it.
This is where most companies hit a wall. They try to build AI tools on top of unstructured, inconsistent, poorly tagged content. They start with unclear objectives and no metrics. They can’t even define what “good” looks like.
That’s not an AI problem. It’s a leadership problem.
The Three Deadly Sins of AI Projects
After years of working with global enterprises, here are the three patterns we see again and again:
1. No Use Cases, No Metrics
Leaders approve AI projects with vague goals like “improve productivity” or “enable automation.” But there’s no clarity on the business process being improved, what success looks like, or how it will be measured. One project we rescued had never even defined a baseline. How would they know if it worked?
2. Garbage In, Garbage Out
The content and data used to “train” or feed the AI is often a mess: outdated manuals, inconsistent PDFs, duplicated files, and no metadata. If your knowledge base is 10,000 pages of poorly structured documentation, even the best AI can’t find what matters.
3. Tech-First Thinking
Everyone’s asking: Should we use Copilot? Gemini? Claude? That’s like picking the paint before you’ve drawn the blueprints. Without understanding your users, workflows, and bottlenecks, the tool doesn’t matter. And yet, this is often where the conversation starts.
Forget Proof of Concept. Start with Proof of Value.
Proof of Concept (PoC) has become a crutch in the enterprise world. It lets you “test” ideas with no accountability. If it fails, no problem, it was just a PoC. But if you’re not measuring impact, using real data, or designing for production, what are you proving?
We recommend a Proof of Value (PoV) approach instead.
A PoV forces you to:
- Use real, messy, uncurated content, not the “demo-ready” version
- Define success metrics in advance
- Focus on the business process you’re trying to improve
- Plan for scale, not just experimentation
The time for tinkering is over. If your AI pilot doesn’t reflect the complexity of the real environment, it won’t survive deployment.
Bad Questions, Worse Answers: The Human Problem in the Loop
Here’s something no one likes to admit: most people don’t know how to search well.
We worked with field technicians using a chatbot assistant. They’d type “Vector 7700,” the name of a machine, and expect answers. But what about it? Troubleshooting? Setup? Specs? It’s like walking into Home Depot and saying “tools.”
This isn’t the user’s fault. It’s a design flaw. We can’t expect users to become AI prompt engineers. Instead, systems must interpret ambiguous queries, use faceted search, and respond with real understanding.
That’s where Retrieval Augmented Generation (RAG) comes in. But even RAG fails without:
- Well-tagged, structured content
- Rich metadata
- An understanding of the user’s context and intent
Want Better Results? Listen for Better Words
If you’re evaluating AI vendors, here’s a simple test: What words are they using?
Real partners will talk about:
- Information architecture
- Use cases and user journeys
- Metadata and content tagging
- Governance and content workflows
- Knowledge models and process analysis
If they skip these and start with “GPT-4” or “our amazing UI,” they’re selling smoke.
Leadership Lessons: Practical Steps for AI Transformation
Here’s how business leaders can break the cycle and turn GenAI into value:
1. Define “Good”
If you can’t define a good answer or a good outcome, you can’t measure improvement. Start with KPIs tied to real business impact.
2. Start with the Strategy
What’s your business trying to achieve? Don’t ask, “What can AI do for us?” Ask, “What processes matter most — and where do we lack insight or efficiency?”
3. Fix the Content First
You can’t layer AI over broken content. Tag it, structure it, and componentize it. This isn’t optional, it’s foundational.
4. Respect Context
The same answer won’t work for everyone. AI must adapt based on user role, experience level, task, and timing. That’s metadata — and it’s everything.
5. Build Feedback Loops
Use rating systems, user comments, and search logs to refine the system over time. Measure not just accuracy, but usefulness.
Final Thought: The Work Hasn’t Changed—Only the Tools
There’s a tendency to treat AI like magic. But at its core, this is still information management. It’s just faster and smarter than before.
We’ve been solving the same problems, accessing knowledge, improving decisions, and helping people do their jobs better, for decades. The difference now is that we have better tools and better tools deserve better strategies.
Generative AI isn’t the destination. It’s an amplifier. If your processes are strong, your content is clean, and your teams are aligned, it will take you further, faster.
But if your foundation is weak, it’ll just get you lost more efficiently.
👉 This article appeared on CustomerThink.com