AI Experience consulting
You built an excellent AI product. So why isn't it used the way the design promised?
The model performs. The demos landed. The rollout happened. And yet the adoption you expected hasn't arrived, or the numbers look fine and nothing has actually changed. The reason usually isn't the model. It's the experience of trusting it.
What actually changed is a person's reaction to it
A model doesn't get adopted. A person decides — every time they see its output — whether to act on it, double-check it, defer to it, or quietly go back to their old way. That decision turns on one thing: whether they understand where the system is right and where it isn't. When they can't tell, their trust drifts away from what the system actually earns. That drift is what we measure.
AI Experience is whether people rely on your AI — and whether they should
It sits above the model and beneath the outcome. It's the difference between a system people trust correctly and one they ignore, fear, or believe too much.
Can the model produce a good answer? Benchmarks measure this. It's necessary, and for most teams it's no longer the bottleneck.
Are people opening the tool? Dashboards measure this. But mandated use and willing use look identical from here.
Do people rely on the output, and are they right to? This is where a capable model turns into real reliance, or quietly fails to.
The under-used product from the top of this page is an AI Experience failure. And unlike a vague adoption problem, it's diagnosable — which means it's fixable.
Your product is under-used because of one of two failures. We find which, and where the cause lies.
Reliance breaks in two opposite directions. Each looks different, each needs a different response, and a blended metric hides both. Here's the failure, and here's what we do about it.
People don't believe it, so they keep the manual process or abandon the feature after one wrong answer. The capability is real and unused — this is failed adoption and failed product: the investment booked, the return quietly never arriving.
We look for the specific reason reliance never formed — a missing signal, an unrecoverable error, a step people don't believe — and recommend the change most likely to address it. Not "improve UX." A named cause and a specific recommendation.
People act on confident output without checking, because nothing signalled the uncertainty. The capability is real and now actively harmful — the wasted-investment and no-recourse failures, and the costliest of all.
We measure trust against reliability and surface where belief outruns what the system earns, then recommend the friction that should be there — a confidence cue, a recourse path, or dropping AI from that step entirely.
Both start the same way: we score how much each audience trusts the system and how often it's actually right — internal users and customers separately — trace each gap to its likely cause, and give you specific changes to make. We record the baseline so that if you re-measure later, the two readings are comparable.
And left alone, none of it corrects itself
Neither failure resolves with time. Both persist, quietly, while the metrics you watch stay green — which is exactly why the cost keeps climbing.
- Low adoption stays low. People who worked around the tool once keep working around it. Every week it's the default a little less, and no dashboard shows the drift.
- Over-trust keeps compounding. Each confident wrong answer that goes unchecked trains people to check even less. The exposure grows silently until something forces it into view.
- The spend keeps reporting as a success. Benchmarks hold, logins hold, and the gap between what was promised and what changed widens on paper you can't see.
- The longer it runs, the harder it is to unwind. Habits set, workarounds harden, and a fixable experience problem becomes an organisational one.
A drift you can name is a drift you can stop. So the question is how you'd reverse it — which is exactly what our approach is built to do.
Our framework designs the experience that earns adoption
Both failures above — routing around it, or believing it too much — come from the same root: the user can't tell where the system is right and where it isn't. Fix that understanding and both reverse. So we don't chase usage; we build understanding on purpose, because a person relies on a system exactly as far as they understand it.
Watch it happen at the scale of one user and one output — then multiply it across every person who touches your models. A restaurant owner is handed a daily guest forecast. Left as a bare number, they don't trust it, so they override it — under-trust, adoption dies. Our framework turns the same forecast into something they understand:
- It confirms what they already know — the forecast rises on holidays, just as the owner expected. Their instinct is validated, so they start to trust it.
- It shows them something they didn't — a lift on paydays they'd never accounted for. Now the model is teaching them, and they lean on it more, not less.
- It reveals where it's blind — an off day no rule explains, because the model was never given a local events calendar. Knowing that, they trust it on ordinary days and check it on unusual ones.
Each of these is a designed moment, not an accident. And this is why it drives adoption: understanding → warranted trust → reliance. A user who knows where a system is right stops second-guessing it and starts depending on it — that reverses under-trust. A user who knows where it's blind stops over-relying on it where it can't be trusted — that reverses over-trust. Either way, reliance lands where it's warranted. That is adoption — the real kind, not the login count.
The same principle holds for a chatbot, where the signals are linguistic rather than analytical — fluency that reads as competence, confidence that doesn't track accuracy, agreement that quietly erodes trust. Our register holds fifty-one such patterns across analytical and conversational AI, drawn from research on prediction interpretability, persuasion, and how people calibrate trust in machines. We use them to design an experience people can actually rely on — and reliance is what your adoption numbers have been missing.
What the engagement looks like
Typically eight to ten weeks, run as one sequence.
- 01Census
- 02Assess in depth
- 03Score per audience
- 04Diagnose the cause
- 05Recommend changes
What you receive
Concrete artifacts, not a slide deck of adjectives.
- Friction registerFrictions traced to their likely mechanism, with the test that would confirm it.
- Specific recommendationsConcrete changes, scoped clearly enough for your team to act on.
- A prioritised readWhere to start, and why, based on the evidence gathered.
- A recorded baselineThe exact queries, wording and probes we used, so a later re-measurement is comparable.
If you can't yet say whether people rely on your AI, that's the conversation to have
Book a conversation
Tell us what you've deployed and what you can't currently answer about it. A person reads every enquiry and replies within three working days.