You launched it. Are people working differently?

Shipped is not the same as used.

Launch reports count logins and training sessions. The question is whether people now work differently. When they don’t, the cause is rarely the technology alone: people lack the skills or confidence, the work around it never changed, or the organization still rewards the old way. We help the leaders who funded it find out which, and what to do next.

Find out why it isn’t used How the Adoption Review works

Asked by COOs, CHROs and the leaders who own the change.

Share of one team working each way, week by week after launch. People can do both. Illustrative.

The old way won.

What gets reported, and what is happening.

Launch reports measure activity. Adoption is whether the work changed. When it hasn’t, the cause is skills and confidence, the work itself, or the organization around it, and often more than one.

Reported: “Everyone was trained.”Happening: A one-hour demo. Nobody checked that people can do their own work with it.Cause: Skills
Reported: “Usage is up this month.”Happening: Logins, from the same few enthusiasts. The rest don’t trust its answers yet.Cause: Confidence
Reported: “The old report is retired.”Happening: Someone rebuilds it in a spreadsheet every Monday, because that is quicker.Cause: The work
Reported: “It is live.”Happening: Nobody’s targets, decision rights or workload changed, and leaders still ask for the old numbers.Cause: The organization

Meanwhile you pay twice: to run the new system, and for the old way beside it. And the next AI proposal meets a room that has seen this before.

Sometimes the resistance is the evidence. If the new way is slower or less reliable than the old one, people are right to avoid it. Find out what it makes harder before asking anyone to change.

Expand it, rework it or retire it.

Adoption changes when the work changes: the skills people need, the time they have, the decisions they own and what leaders reward. The people who funded it decide what to change, and the answer can differ by team.

Expand what works: When the people using it get better results, and the reasons others do not are known and fixable.; What it takes Spread it team by team, with practice on their own work, sponsors who use it themselves, and a measure that counts outcomes, not logins.
Rework how it fits: When the value is real, but something around it is wrong.; What it takes Fix the cause the evidence points to: the skills, the workflow, the system itself, or the targets and decision rights around it. Then check again by a set date.
Retire it: When people have sound reasons not to use it, and fixing them would cost more than it returns.; What it takes Stop paying to run it, and keep what it taught you about what people will actually use.

Leaving it running unused is the expensive option: the cost continues, and so does the workaround.

Which is it for yours? Bring the usage you have and the people who own the outcome. If the evidence is not there yet, the first decision is what to find out, who finds it and by when.

Review the rollout with us

Or start with your people: the free AI & Data Literacy assessment, twenty-five statements, about twelve minutes, no email required. It shows whether your people can work with data and with AI; it cannot tell you why one system is not being used.

What changed when training fit the role.

An assessed result was measured in skills assessments. An operating result is a change in the work. A self-reported result comes from the people involved. Each is as the client reported it.

A regional health organization, clinical and administrative staff

Trained by role, then checked.

80%of participating staff demonstrated improved data handling and analysis skillsAssessed result
30%increase in the use of data visualization tools for reportingOperating result

Training needs were assessed role by role, for doctors, nurses and administrative staff. More than 300 employees were trained hands-on, and their progress was tracked through regular assessments.

Read the case study

70%of staff at a multi-site care provider reported the new data platform easier to use and to reach. Self-reported result.Read the case study

Adoption Review

Find out what’s preventing the change from taking hold.

A bounded review of one system, program or change, before or after launch. We read how it is used, hear from the people expected to use it without their managers in the room, and look at the work around it. It ends with a live readout, so the leaders who own the outcome can decide in the room.

Delivered on the AI & Data Assessment Sprint format: two weeks, fixed scope, fixed fee agreed in writing first.

Scope an adoption review See the Sprint format

What you get

Where adoption is stuck and why: skills, trust, the work, sponsorship or the organization around it
What the stall is costing you, or keeping you from, with the assumptions shown
What to expand, rework, reinforce or retire, team by team, in order
Who owns each step, the measure of work changed, and the dates to check it

Other ways in

Leaders aren’t aligned on what it is for.Executive AI WorkshopOne working session that aligns the leadership team on what AI is for here, and what comes first.
The gap is skills and confidence.AI & Data Literacy ProgramRole-based tracks on your own tools, with use and judgment measured afterwards.
The system itself is the problem.Production buildRebuilt around how the work is actually done, and handed to a team that can run it.
Was it worth building at all?Question 1: Value & EconomicsWhat it is worth after the full cost of running and adopting it.

The practices behind this page: AI & Data Literacy, Strategy & Governance, BI & Data Visualization and Generative AI & AI Agents.

Questions operating leaders ask first.

How do we know whether it is being used?

Count the work it changed, not the logins: decisions made with it, cases closed through it, reports nobody rebuilds by hand. Then ask the people expected to use it, without their managers in the room.

Is this a training problem or a change-management problem?

Usually both, and neither alone. Skills and confidence are one cause; the work and the organization around it are the others. Training by role, on people’s own work, helps once the work fits and leaders reward the new way. Change management that stops at announcements does not.

Who should own adoption?

The business owner whose number it is meant to move, not IT or learning and development alone. They sponsor the change, and their teams should see them work the new way. If nobody owns it, nobody notices when it stops being used.

We haven’t launched yet. Does this apply?

Yes, and it costs less before launch. The same three causes are the readiness test: can people do their work with it, does it fit the work, and do targets and decision rights reward it? The Adoption Review works the same way on a change you are about to make.

Is retiring it a failure?

No. Retiring a system on evidence stops a running cost and tells you what people will actually use. Keeping it because it was expensive is the failure.

Back to question 1 of 5, Value & EconomicsWhat is it worth, and what will it cost?All five questions

Bring us the change that didn’t stick. We’ll help you decide what happens next.

Thirty minutes with the person who would lead the work: a senior AI and data strategist who has taken programs from strategy to systems running in production, in more than one industry. Whether the barrier is skills, the work or the organization around it, you will leave knowing where we would look first. Retiring it is a legitimate answer, and sometimes the right one.

Book 30 minutes on your rollout

Shipped, but unused? Thirty minutes on the change people work around, with the person who would lead the work.

Talk through your rollout