A policy tells people the rules. This shows what they do.
Most organisations have an AI policy, some training, a licence for approved tools, and no real picture of what is happening underneath. Skilly Work builds that picture from the work itself.
The starting point
Where AI already sits in your work
Everyday knowledge work
Analysis, drafting, summarising, customer contact. The volume is already large and almost none of it is recorded anywhere.
Decisions about people
Anywhere AI touches hiring, performance or access, the stakes and the legal exposure both rise sharply.
Shadow AI
Personal accounts and unapproved tools. Blocking them moves the behaviour rather than ending it; a reflective record surfaces it.
The duty
What actually binds you today
We would rather be precise than alarmist. The duties that bind you now are mostly the ones you already carry, not new AI-specific ones.
Confidentiality and customer commitments
What your contracts promise about customer data constrains which tools may touch it, whatever the tool's own terms say.
GDPR
Engaged the moment personal data enters a tool that may retain it, including through an employee's personal account.
Employment law
Wherever AI touches a decision about a person, the duty to make that decision fairly and explainably is already yours.
And the EU AI Act, stated precisely
Under the EU AI Act the duty sits with the organisation deploying AI, not the vendor supplying it. The Digital Omnibus (Regulation (EU) 2026/1744, in force 27 July 2026) rewrote the AI-literacy duty from a duty to ensure literacy into a duty to take measures supporting it, and it carries no standalone fine, though market surveillance authorities have supervised it since 2 August 2026. The Act's sharper duty, the competence of the people exercising human oversight, was deferred to 2 December 2027 and bites where AI is used in a high-risk role. It applies per person, which is the argument for starting now rather than then: a record of judgement counts because it accrues, and it cannot be assembled retrospectively.
Position as at 6 September 2026. This is not legal advice, and a firm evaluating Skilly Work will form its own regulatory view.
The record
What a quarter of it shows you
Your people spend about fifteen minutes a quarter reflecting on real moments of AI use. Each reflection is scored against five observable habits, and the result is a record per person and per team rather than a survey.
Where AI is in use
Which tools, which teams, which kinds of work, tagged against your own AI system register so the picture matches your governance record.
Where the risk sits
Data exposure, unverified output moving onward, unfairness in decisions about people, undisclosed use and over-reliance. A live risk goes to a named person rather than into a queue.
Where capability is thin
Which teams and which levels are weakest on each habit, so training money goes where the evidence says it is needed.
What is working
The teams getting real value and the specific practice worth spreading. Usually the larger half of the return, and the half nobody can currently see.
Where this stops. Skilly Work evidences the people dimension of AI governance. It does not provide model-risk controls, technical documentation or the governance framework itself; those stay your own controls. It is also not monitoring software: nothing reads anyone’s screen or files, and the input is a person’s own account of their own work, which is the only reason it is honest.
Start with one team, one quarter
A structured pilot in one team hands you a real evidence pack at the end of it. Try the interactive demo now with no sign-up, or talk to us about shaping a pilot.
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