AI EXPERIMENTATION GUIDE
Everyone's building AI.
Almost no one's validating it.
88% of AI pilots never reach production. The problem isn't the technology — it's that no one tested whether customers wanted it. Rapid experimentation fixes this.
88%
of AI pilots fail to reach production
$4.6T
projected AI market by 2030
Only 11%
of AI projects reach production (Accenture)
THE AI VALIDATION GAP
The Problem With AI Projects
Every enterprise is rushing to build AI products. Boards are demanding AI strategies. Teams are spinning up pilots. Budgets are being allocated at unprecedented speed. The pressure to ship something — anything — with AI in it has never been higher.
But the Law of Market Failure applies to AI just as much as any other product. The question isn't "can we build it with AI?" — it's "should we build it at all?"
Most AI projects fail not because the models don't work, but because nobody validated whether customers would actually change their behavior for the AI-powered solution. The technology is impressive in a demo. It just doesn't solve a problem customers will pay to have solved.
The hardest part of AI isn't the model — it's knowing whether anyone will use what you build. Rapid experimentation answers that question in days, not quarters.
THREE TRAPS
Why AI Ideas Fail Without Experimentation
The Solution Trap
"We have AI, so let's find a problem for it." Teams build AI features nobody asked for because they're excited about the technology, not because customers have a job they need done. The result: technically impressive products that nobody uses.
The Demo Trap
"It works in the demo!" But demos aren't demand. A working model doesn't mean customers will pay, change their workflow, or even notice. The gap between "impressive demo" and "product people use daily" is where most AI investment goes to die.
The Pilot Trap
"Let's run a 6-month pilot." By the time you learn it doesn't work, you've spent $500K, the market has moved, and the team is too invested to kill it. Pilots are slow, expensive, and optimized for sunk cost fallacy, not learning.
Rapid experimentation breaks all three traps. You test demand in days, with real customers, before writing a single line of model code. The cost of learning whether an AI idea will work drops from hundreds of thousands to hundreds.
THE FRAMEWORK
How to Validate AI Ideas
The AI experimentation framework adapts pretotyping specifically for AI product validation. Four steps. No model required.
Define the Job, Not the Tech
"What is the customer hiring this AI to do?"
Ignore the model architecture. Structure the idea: what problem, who feels it, what changes if AI solves it. Write a hypothesis: "At least X% of Y will Z when offered AI-powered [solution]." If you can't articulate the customer job, the AI is a solution looking for a problem.
Prioritise Ruthlessly
"Does this AI idea deserve an experiment?"
Not every AI idea warrants testing. Impact vs Effort. AI features that are high-effort and low-impact should be killed before the experiment stage, not after 6 months of model development. Save experiments for ideas with genuine strategic potential.
Test Demand, Not Capability
"Will customers actually use this?"
Don't build the model. Simulate the AI experience using pretotyping methods: Mechanical Turk (human behind the curtain), Fake Door (landing pages, ads), Pinocchio (non-functional mockups). Define success criteria before you start. Run 2–4 week proof-of-value sprints. Measure what customers actually do.
Stop, Pivot, or Scale
"What does the evidence tell us?"
Evidence drives one of three paths:
Stop — You just avoided months of model development and potentially millions in infrastructure. Celebrate the save.
Pivot — The use case needs refining. Design follow-up experiments with adjusted assumptions.
Scale — Build with evidence. Validated AI ideas enter your delivery pipeline with a data-driven business case.
Enterprise AI governance: Rapid experimentation for AI operates within your governance framework. Experiments test customer demand, not model capability — so you can validate AI ideas before triggering full data privacy reviews, model risk assessments, or infrastructure investment. Built-in approval workflows handle compliance review at the experiment category level. When an idea passes validation, it enters your AI delivery pipeline with evidence.
IN PRACTICE
AI Experimentation in Practice
Exponentially's AI Idea Validator
We built our own AI Idea Validator using this exact framework. Before writing any code, we tested whether innovation teams would actually use an AI tool to validate their ideas. The pretotype was a simple form that emailed results manually. When hundreds of teams used it in the first month, we knew the demand was real — and built the AI version with confidence.
Try it free →PEXA: 50 Pretotypes in 9 Months
PEXA used pretotyping to test 50 ideas in 9 months — including AI-powered features for their property exchange platform. By testing demand first, they avoided building AI products nobody wanted and focused investment on the ones customers validated through real behavioral data.
Read the case study →Tabcorp: $12M Saved
Tabcorp's innovation team used rapid experimentation to run 12 experiments in 3 weeks, including testing customer appetite for AI-assisted features. The data saved $12M by killing ideas that looked promising in presentations but had no real customer demand when tested.
Read the case study →TOOLS
Tools for AI Experimentation
AI Idea Validator
Test any AI idea in 2 minutes. Get a Lean Canvas, hypotheses, and your first experiment design. Free, no signup.
Try it free →Exponentially Platform
Capture AI ideas from across your company. Run pretotyping experiments. Track what you've validated and what you've killed.
Learn more →Free Innovation Guide
A practical guide to embedding rapid experimentation — including AI idea validation — in your organisation.
Download free →FURTHER READING
AI + Experimentation on Our Blog
Is Experimentation Dead in the Age of AI?
AI accelerates the build, but only experimentation tells you what customers actually want.
AI as Facilitator: How Voice-mode ChatGPT Ran a Case Study Interview
Using AI to run customer interviews — a real-world experiment.
Predictions, AI and the 10X Experiment Challenge
How AI is changing the speed and scale of experimentation.
Is AI a Feature or a Business?
The question every team should ask before building an AI product.
AI + Experimentation Resources
10 essential tools for innovation teams running AI experiments.
FREQUENTLY ASKED QUESTIONS
AI Experimentation FAQ
How do you validate an AI product idea?
Use rapid experimentation to test customer demand before building the model. Simulate the AI experience using Mechanical Turk pretotypes (human behind the curtain), test willingness to pay with Fake Door tests, and measure real engagement before investing in development.
Why do most AI projects fail?
88% of AI pilots fail to reach production, primarily because teams focus on model capability rather than customer demand. The technology works, but nobody validated that customers would actually change their behavior for the AI-powered solution. This is the Law of Market Failure applied to AI.
What is AI experimentation?
AI experimentation applies rapid experimentation principles specifically to AI product ideas. Instead of spending months building models, teams test whether customers actually want the AI-powered solution in days using pretotyping methods.
How long does it take to validate an AI idea?
A single AI idea can be validated in 1–2 weeks using pretotyping. An initial AI experimentation sprint tests multiple ideas in 2–4 weeks, giving teams clear data on which AI investments are worth making. Most organisations then extend into longer engagements to scale validated AI ideas.
Can you test AI ideas without building the AI?
Yes — that's the entire point. Mechanical Turk pretotypes simulate AI with humans behind the curtain. Fake Door tests measure demand without any backend. Pinocchio pretotypes show the interface without the model. If customers don't engage with the simulation, the real AI won't help.
What is pretotyping for AI?
Pretotyping for AI uses the same validated methodology created at Google, applied specifically to AI product ideas. It tests whether customers want the AI-powered outcome before investing in model development, training data, and infrastructure.
Stop building AI products nobody wants.
Talk to Leslie about validating your AI ideas before investing in development. Or test your idea right now with our free AI Idea Validator.