1 December 2024
Why AI Projects Fail (And It's Not the AI)
Most AI projects stall not because of model limitations, but because of data access problems. Here's what we've learned from four years of shipping AI systems.
The demos always work. You’ve seen it: a proof of concept that impresses everyone in the room. The model answers questions about your data. It feels like magic.
Then comes the hard part.
The real bottleneck
Getting that demo into production means connecting AI to your actual business systems. The ERP that’s been running for fifteen years. The databases with quirky schemas. The legacy applications that nobody quite understands anymore.
This is where most AI projects die — not with a bang, but with a slow fade of “we’re still working on the data pipeline” updates.
What we’ve learned
After four years of building AI systems that run in production, we’ve identified the patterns that separate projects that ship from projects that stall:
Start with data access, not model selection. The model is the easy part. Most frontier models will handle most tasks well enough. What matters is whether you can get your data to the model quickly, securely, and in a form it can understand.
Security can’t be an afterthought. AI agents that access business data need fine-grained access control. Row-level, column-level, context-aware. Bolting this on later is painful.
Semantic layers matter. LLMs work much better when data is structured with meaning — business terms, relationships, calculated fields — rather than raw tables and joins.
Real-time is non-negotiable. For conversational AI to feel responsive, data access needs to be sub-second. If your agent waits three seconds to query, the experience falls apart.
The path forward
We built our platform specifically to solve these problems. Not because we wanted to be a platform company, but because we got tired of watching good AI ideas die on data integration.
When we engage with a client now, we can get their data AI-ready in weeks rather than months. That’s not magic — it’s just having the right infrastructure in place.
Thinking about an AI project? We’re happy to talk through whether your data is ready and what it would take to get there.