What Makes a Generative AI Product Actually Ship to Production
Most GenAI pilots never make it to production. The gap isn't the model — it's everything around it. Here's what separates the demos from the products.
The dirty secret of the GenAI wave is the conversion rate from pilot to production. Industry surveys suggest that fewer than 30% of enterprise GenAI pilots make it to production deployment. The failure isn't the model — frontier models are genuinely capable. The failure is everything that has to exist around the model: prompt architecture, evaluation framework, safety layer, integrations, cost model, user experience, and operational infrastructure.
The Evaluation Problem (That Nobody Talks About)
How do you know if your AI is working? This question kills more GenAI projects than any other. A chatbot demo looks good — but 'looks good' isn't a metric. Production requires a formal evaluation framework: a golden dataset of inputs with expected outputs, an automated eval pipeline that scores every model response, and thresholds that determine when quality is good enough to ship. Building this eval infrastructure is less exciting than the AI feature, which is why it gets skipped. Don't skip it.
Prompt Engineering Is Software Engineering
Production prompts are version-controlled, tested against the eval suite on every change, parameterized to accept dynamic inputs safely, and designed to handle edge cases gracefully. Many organizations treat prompt management as an afterthought — prompts buried in code strings, changed without tracking, with no way to roll back a regression. Treat your system prompts like application code.
The Cost Model Has to Work
A GenAI feature costing $0.20 per query might work fine in a pilot with 100 users. At 100,000 users, that's $20,000 per day. The cost model needs to be designed from the start: prompt compression, query caching, model selection (smaller models for lower-stakes tasks), and clear unit economics that justify the spend.
Safety and Guardrails Are Not Optional
A production GenAI system needs input validation, output validation, rate limiting, audit logging, and human-in-the-loop escalation paths. Building all of this after the feature is already in production is significantly more expensive than designing it in from the start. At Edwyz, we specialize in taking GenAI features from pilot to production. If you have an initiative that's stuck, let's talk.