A working AI product can still fail because nobody changes how they work around it. Bring one live product, pilot, or solution and identify the adoption constraint most likely to stop users or buyers before the market finds it for you.
Leave with a practical evidence plan to pressure-test behavior, trust, workflow fit, and adoption before you spend more money on features, rollout, training, or sales.
We will identify the #1 adoption constraint and build a practical evidence plan to pressure-test it.
Adoption depends on three connected conditions. The workshop scores the live product against each one, then focuses the evidence plan on the weakest constraint.
Define the actual adopter, current behavior, target behavior, workflow trigger, friction, and why the new behavior is worth choosing over the familiar one.A feature is what the product can do. Adoption is what the user stops, starts, or changes because it exists.
Identify where trust rises or breaks. Define what the AI should do, never do, communicate uncertainty about, or hand to a human when information is incomplete, contradictory, unusual, or high-risk.Trust is not one global opinion about AI. It is learned interaction by interaction.
Determine what shows users are choosing the product, where they hesitate or verify it, where they return to the old workflow, and what users, sponsors, and buyers need to see before scaling.Do not test whether people like the product. Test whether the product changes what they do.
Adoption does not average. If Behavior Fit is an 8 and Trust Design is a 3, you do not have a 5.5 adoption problem. You have a trust problem.
Traditional UAT tends to ask whether the system produced an acceptable result. Adoption-led UAT asks harder questions:
UAT is not the destination. It is the mechanism for getting real humans close enough to the product to expose what will stop adoption before the market does.
The best validation result is not a sign-off. It is evidence that the product has earned the next behavior.
The workshop is most useful when the technology works, but usage, reliance, buyer confidence, or the behavior change required for adoption remains uncertain.
Evaluate the live product against Behavior Fit, Trust Design, and Adoption Evidence.
Identify the weakest part of the system—the adoption assumption most likely to prevent users or buyers from depending on the product.
Turn that constraint into specific scenarios, behaviors, trust conditions, user evidence, buyer evidence, and a practical next validation experiment.
One focused diagnosis and the next evidence move—not a giant strategy document.
No. We are not building a sales funnel or messaging campaign. We are identifying the behavior, trust, workflow, and evidence conditions that make it easier for users and buyers to confidently say yes.
Because adoption assumptions need evidence. Adoption-led UAT puts real users into realistic workflows, ambiguity, decisions, and failure conditions so the team can see whether people understand the product, trust it appropriately, change behavior around it, and recover when the AI reaches its limit. UAT is the validation mechanism; adoption is the outcome we care about.
No. Testing is part of the mechanism, but the workshop is designed around a product and commercial question: what has to be true for users to depend on this product and for buyers or sponsors to confidently scale it?
Bring one working AI product, pilot, prototype, or live workflow. The more concrete the user, workflow, current behavior, and adoption concern are, the more useful the session will be.
Bring someone who owns the product or solution, someone who understands the business workflow, and someone accountable for implementation, risk, or adoption. Including someone close to the actual user experience will make the session substantially stronger.
Bring one live AI product. We will find the adoption break while there is still time to do something about it.
