Edition #15 - AI That Ships The difference between demos and production
Most AI projects do not fail because the model is “bad.” They fail because leaders confuse possibility with production.
By Mykel Salomon ·
Opening Reflection
Most AI projects do not fail because the model is “bad.” They fail because leaders confuse possibility with production.
A demo can feel like magic. Production is accountability.
Production is when a customer gets the wrong answer at scale. Production is when a bot confidently says something incorrect, and nobody catches it. Production is when “it worked in the pilot” meets the real world.
That gap is what this edition is about.
Signal of the Week
If it feels like your organization has 20 pilots and 0 outcomes, you are not imagining it.
IDC research (via Lenovo partnership) found 88% of AI proofs of concept do not reach wide scale deployment, and that only 4 out of 33 POCs “graduate” to production. Click Here
Gartner has also reported that on average, 54% of AI projects make it from pilot to production, which sounds better until you remember that means almost half never make it. Click Here
Gartner recently warned that at least 50% of gen AI projects were abandoned after proof of concept due to issues like poor data quality, inadequate risk controls, rising costs, or unclear business value. Click Here
Here’s the takeaway: The demo is not the hard part anymore.
The hard part is everything that comes after the demo.
The Core Lesson
A demo is “look what it can do.” Production is “can we trust it, every day, with real users, under real pressure?”
In this week’s conversation, one line kept coming back:
Code is not the bottleneck anymore. Fundamentals are. Requirements. Documentation. Evaluation. Ownership.
If the problem is vague, the AI will be vague. If “good” is undefined, the rollout becomes political. If “done” is unclear, the pilot never ends.
🌍 In the Wild
Here is what “real production” looks like when it works.
1) Narrow problem, perfect fit
Production AI succeeds when the scope is clear and tightly defined.
A strong example pattern is AI used to extract and summarize high-volume documents for teams drowning in text. In this week’s episode, insurance underwriting came up as a classic case: the work is repetitive, document-heavy, and measurable. When the problem is clear, AI can compress time dramatically.
Pattern: narrow scope, measurable quality, expand only after stability.
2) Evaluation is not optional
If you cannot test it, you cannot trust it.
Production teams build an “answer key” first: Real examples with correct outputs. Then they test repeatedly and improve.
A demo can impress you once. Production needs to be correct on a random Tuesday.
3) Bottom-up beats top-down
AI works best when it is pulled by real workflow pain, not pushed by executive FOMO.
The worst approach: “Go buy AI and report adoption.” The best approach: “Show me the friction. We’ll target one small thing and ship.”
Hallucinations: The Production Reality
You cannot “wish away” hallucinations. You design systems to reduce them and catch them.
Production-grade patterns include:
Strict constraints: answer only from approved sources
Retrieval first: ground responses in knowledge base and policy content
Human review: especially for high-risk outputs
Testing harnesses: measure error rates, not vibes
Escalation rules: when unsure, handoff immediately
Simple rule: If the system cannot explain where it got the answer, it should not be customer-facing.
The Demo to Production Checklist
If you are leading AI initiatives, use this as your map:
Problem clarity One sentence. Specific. Measurable.
Define “good” and “done” What does correct look like, and how often?
Workflow ownership Who is accountable for outcomes, not just the tool?
Evaluation plan before build Examples with correct answers. Repeatable tests.
Guardrails and escalation paths What does the AI never do? When does it handoff?
Small scope, fast iteration Keep the vision big. Keep the build narrow.
Culture that admits failure If teams can’t report what broke, the AI program becomes theater.
The Big Question
Are you measuring success by how impressive the demo is, or by whether the system can be trusted when nobody is watching?
A 10-minute Exercise
Pick one AI initiative currently in “pilot.”
Write this down:
The user problem in one sentence
The expected user behavior in 3 steps
5 example inputs with correct outputs
One sentence for “done”
If you can’t write those today, your project is not blocked by the model. It is blocked by missing fundamentals.
🎙️ This Week’s Episode
Episode: AI That Ships: From Proof of Concept to Production
Guest: Ian P. Cook, PhD | SVP of AI Qloo
This conversation explores why so many AI initiatives get trapped in demos and pilots, and what it actually takes to make AI work in real business.
We talk about:
Why AI breaks when problems are vague and goals are not tied to business outcomes
Why documentation and requirements are now the real bottleneck
How to define “good” and “done” so pilots can graduate to production
Why hallucinations are a production issue, not a PR issue
Why the best AI adoption is bottom-up, not top-down
This episode is a reminder that real innovation is not in the hype. It’s in the execution.
🎧 Available on:
Spotify -> Watch Here
Apple Podcasts -> Listen Here
YouTube -> Watch Here
Thought to End the Week
AI is fantastic at how. Humans own the why.
If you want AI in production, stop shopping for magic. Start building clarity, evaluation, and trust.
That is how AI ships.
Have a great weekend, Humans!
Mykel