Every enterprise AI conversation I’ve had this year follows the same arc. Someone builds a proof of concept. The demo goes well. Leadership gets excited. Then, three months later, the project is stuck in limbo: security reviews, data access requests, and nobody quite sure who actually owns it.
I see this pattern across telecom and fintech organizations. The demo-to-production gap isn’t a technology problem. It’s an organizational one.
The demo was the easy part
A POC can skip everything that makes enterprise software hard. It runs on a developer’s laptop with test data. It doesn’t need to handle real user volumes. During a demo, nobody asks about audit trails or data retention policies.
Then the project moves toward production and reality hits. Security wants a threat model. Legal wants to know where the data goes. The platform team wants to know who pays for compute. The data science team discovers the training data is messier than expected. None of this is surprising. These are the same problems every enterprise system faces, plus a few new AI-specific ones: model drift, prompt management, and probabilistic outputs.
The teams that get stuck are the ones that treated the POC as the starting line instead of a feasibility check.
Start boring, stay boring
The single best predictor of success I’ve seen is picking a first use case that’s low-risk and internal. Something where a human reviews the output before anything happens. Document summarization for internal teams. Draft generation for support responses that get edited before sending. Classification of inbound requests to route them to the right queue.
These aren’t exciting. That’s the point. You want a use case where a bad output is an inconvenience, not a liability. One where you can iterate on prompts and evaluate quality without a customer ever seeing an unpolished result.
I keep telling teams the same thing: your first AI feature should be invisible to customers. Ship it internally, prove it works, build the muscle memory for operating AI in production, then expand.
Build the platform before the pilots multiply
Here’s what happens when you don’t have a shared platform: every team builds its own integration. They pick different models, prompt patterns, and logging approaches. Six months later, you have eight AI features and no way to compare quality, manage costs, or enforce policies across them.
The fix is unglamorous. Build a thin shared layer early. It needs three things:
- Centralized model access with authentication, rate limiting, and cost tracking.
- A prompt registry so prompts are versioned, reviewable, and not buried in application code.
- Evaluation tooling that every team can use to measure output quality against a golden set.
This doesn’t need to be perfect or fully featured. It needs to exist before the third team starts building their own AI integration. I’ve watched organizations try to consolidate after the fact. It’s painful and expensive.
Governance that enables instead of blocks
The worst governance models I see are designed by committee without input from the engineering teams that have to live with them. They produce a 40-page policy document, a six-week review cycle, and a strong incentive for teams to quietly build things without telling anyone.
Good governance is lightweight and fast. A one-page use case template. A clear risk-tier system: low risk gets self-service approval, high risk gets review. A standing meeting where legal, security, and engineering are in the same room instead of a months-long email chain.
One organization I worked with reduced its AI approval cycle from eight weeks to five days by switching from a document-based review to a 30-minute live walkthrough with all stakeholders. Same rigor. Fraction of the time.
The uncomfortable truth
Most enterprise AI projects don’t fail because the technology isn’t ready. They fail because the organization isn’t ready. The AI works fine in the demo. The procurement process takes four months. The data team can’t provide clean training data. The legal review has no precedent to follow, so it defaults to “no” until someone escalates.
If you want to ship AI in an enterprise, spend less time evaluating models and more time clearing organizational roadblocks. Get a budget owner. Get a security sponsor. Get data access sorted before you write the first prompt.
Process beats talent. Every time.