AI readiness
AI is not your biggest risk. Being unprepared is.
The readiness gap is the distance between the change heading for your organisation and how ready your people are for it today. elli measures that gap, explains where it comes from, and helps you close it.
In five seconds: this page is for you if
An AI rollout succeeds or fails with your people, not with the technology
The split is well known. According to BCG, 70% of the value of an AI initiative sits in people and processes, 20% in technology and data, and 10% in the algorithms themselves (BCG, Where’s the Value in AI?, 2024).
Yet readiness assessments are almost always about infrastructure, data quality and integrations: the 20%. The 70% goes unmeasured, and that is precisely where it goes wrong.
You can see the result in the numbers. And it was rarely the technology.
of companies abandoned most of their AI initiatives in 2025
S&P Global, 2025
was the figure a year earlier
S&P Global, 2025
of agentic-AI projects will be scrapped by the end of 2027, Gartner expects
Gartner, 2025
More AI is being used in your organisation than you think
Leaders estimate that 4% of their employees use generative AI for at least 30% of their daily work. Employees themselves report 13%, more than three times as many (McKinsey, Superagency in the Workplace, 2025).
That gap cuts both ways. If your people are further along than you thought, you miss the chance to roll their way of working out more widely. If they are quietly dropping out, you are building your rollout on support that is not there.
It makes sense, too: almost nobody tells their manager they are afraid of being replaced. Meanwhile 96% of CEOs see AI being used outside approved channels, and 79% lose sleep over the legal risks (Dataiku & The Harris Poll, 2026). Plenty of work is being done with AI, just not where anyone is looking.
of CEOs see AI being used outside approved channels
are worried about legal exposure
Dataiku & The Harris Poll, 2026
AI readiness is two questions: is your organisation ready, and are your people ready too?
Most assessments answer only the first question: infrastructure, data, governance. But it almost always goes wrong on the second.
Question 1: are your people ready?
Three axes, and they measure the same story at three different moments.
Usage: what someone actually does with AI: which tools, how often, at what cost. Behaviour, not opinion. Moves in weeks.
Engagement and experience: how someone relates to the work and to the change: understanding the why, trust, a sense of autonomy. Moves in months, and you have to ask for it.
Performance: what someone actually delivers. Moves in quarters, and is almost always skipped, even though it is what decides whether usage and commitment produce anything at all.
Question 2: can your organisation carry it?
Seven dimensions decide whether AI can land in your organisation: from strategy and data to governance, talent and culture. Established through interviews and document analysis, topped up with the system data you already have.
Rarely the bottleneck, always the precondition: without governance, usage becomes invisible rather than absent.
Why you measure usage, engagement and performance together
The three axes do not move in step, and that is exactly the point. The people who use AI most do not necessarily perform best. Measure usage alone and you label critical but productive people as drop-outs. Measure experience alone and you do not know why someone has stalled. Measure performance alone and you do not know how durable that result is.
And one strong organisational score says nothing about what sits underneath it. A 68 out of 100, strong on talent and weak on governance, is useful for the leadership team but useless as the basis for an intervention.
Usage is the only axis that runs ahead of the other two. It is continuously measurable and changes in weeks. That makes it the only signal that moves before the loss has happened, and so the only one that lets you intervene in time.
Prefer the full story?
The white paper Human-ready is AI-ready works out the method behind this page in full.
One average score says nothing: look per team and per profile
A readiness score is an average, and an average describes a reality nobody actually experiences. Underneath an average of 68, IT can sit at 95 and Finance at 55; that difference decides where an intervention pays off and where it is waste.
Cross engagement with performance and you get nine segments, each with its own approach. Add usage as a third axis and those same segments exist in three layers: an employee is not in a box, but at a coordinate. That is how you tell apart profiles that look alike on the flat plane but ask for the opposite.
Two examples of what that gives you.
The quiet drop-out
Still performing at level and not scoring alarmingly on engagement, but usage has been sliding for weeks. The earliest reliable signal that someone is checking out mentally.
The shadow user
Says they use a lot of AI while measured usage is low. That difference is itself the signal: this group proves there is demand, not reluctance.
So the same low score calls for a different conversation. Without that detail, everybody gets the same training.
A segment shows where it hurts, the drivers explain why
A segment shows where it hurts, not why. Two employees can end up in the same box for opposite reasons, and that reason is not in the KPI itself.
So we correlate the KPIs with the drivers behind them: perceived usefulness, autonomy over the tool, trust in the policy, workload, leadership support and psychological safety. The same logic as classic engagement models (Job Demands-Resources, Self-Determination Theory), carried through to AI.
That distinction decides the intervention: the KPI points at the place, the correlation at the cause.
A low usage score × trust in the policy
= a communication problem
That same score × perceived autonomy
= a different kind of tool or a different role
From insight to adoption: every team blocks on one point
Measuring is not yet adoption. So we use ADKAR as the backbone: five phases, each with its own failure signal.
That happens in ninety-day waves: a baseline per team, the why and the ground rules, role-specific labs, practice with support, reinforcement, and then a re-measurement. By then the blocking point has moved, and the next wave starts there. Not a project with an end date, but a rhythm.
How a programme runs
That last stretch is where this differs from an assessment. Most readiness exercises end at the report. We stay until the behaviour changes.
What you get in your hands
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One score with a maturity level, from wait-and-see to embedded, plus a profile per dimension so you can see which dimension is the brake.
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Readiness per team, because one average steers you wrong.
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Segments and profiles with the recommended approach per group.
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The root-cause analysis: which driver explains the score most strongly in which segment.
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An adoption plan in waves, with an intervention area, an owner and an intended result for every recommendation.
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A re-measurement at fixed intervals, so you know whether it worked.
Reporting is always aggregated, never on a group smaller than five, GDPR- and AI Act-compliant. That choice is not merely legal: a measurement that undermines trust makes exactly the problem it sets out to solve worse.
Why elli
We measure the half others leave out.
The organisational model follows the common standard: seven dimensions, weighted scoring, five maturity levels. Alongside it comes a profile per employee on three axes. That second half is what readiness assessments usually do not do.
We do not stop at the report.
Measure, analyse, advise, guide, implement, and then re-measure. The guidance is not an afterthought: it is where the result comes from.
Continuous instead of once.
Readiness crumbles the moment a rollout reaches teams you never measured. Experience moves in months, usage in weeks. One measurement a year is always too late.
Built in the EU.
Reported in aggregate, with a privacy threshold that is not up for negotiation.
Start with a free baseline
The first measurement is free: one survey among your employees and a readout showing where you stand today and which dimension is your brake. It doubles as the baseline you measure everything that follows against.
From 25 employees. You send the survey to your team yourself; we do the rest.
Request your free AI-readiness check
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