Confident, responsible AI use across the enterprise.

An AI capability enablement solution for the enterprise.

Most enterprises are being asked to act on AI without an evidence-based view of their own readiness. VCL AI Adoption establishes that view, builds the capability each level needs, and aligns leaders, managers and professionals on what AI must deliver.

Request a diagnostic How it works

Adoption has outpaced capability.

AI is already in use across most workforces. Capability, governance and evidence have not kept pace, and regulators now expect all three.

58%

of employees already use AI at work, exceeding 80% in Saudi Arabia and the UAE

70%

of organisations use AI in at least one business function

40%

of employers cite skills as the main barrier to AI adoption

+55%

year-on-year rise in publicly reported AI incidents

AI literacy is now a regulatory expectation. Article 4 of the EU AI Act requires organisations that deploy AI to ensure a sufficient level of AI literacy among their staff, and the NIST AI Risk Management Framework and ISO/IEC 42001 both place workforce competence at the centre of responsible AI.

Sources: Stanford HAI, AI Index Report 2026 · OECD AI surveys of employers and workers · OECD, AI and Skills, 2026 · EU AI Act, Article 4

Boards are pressed to act. Without evidence, the choice is between over-investing on hope and standing still on doubt. Neither is a strategy.

AI rewards judgement, not tool operation.

Earlier technologies rewarded the people who learned the tool. AI rewards the people who can judge its output, and exposes the organisations that cannot.

Output must be verified

Generative AI is probabilistic and fluent. The same request can return different answers, and a wrong one reads as confidently as a right one.

Value and exposure grow together

Every productivity gain arrives with matching exposure: data leakage, bias, confidentiality and intellectual property risk.

Behaviour decides the outcome

Over-reliance and quiet resistance are both recognised failure modes of adoption. Neither is solved by a tool licence or a policy memo.

The twelve behaviour patterns that decide adoption

Where knowledge, skills and behaviours meet.

One of the three is never enough. AI competence sits where all three overlap, and that is where it is measured.

One engagement, three levels, four phases.

Leaders, managers and professionals move through the same four phases in parallel, each on its own track. Every phase closes with an output the enterprise keeps.

Assess

3 to 4 weeks

Align

2 to 3 weeks

Deliver

8 to 12 weeks per cycle

Advance

3 to 4 weeks, then quarterly

Executives

Interviews and a leadership readiness read

Direction, decision rights and guardrails agreed

Six briefings, each producing a governance deliverable

Results reviewed, next cycle decided

Managers

Survey and AI tool inventory across teams

Team roles and expectations set

Six workshops with assignments in their own teams

Team readiness re-measured

Professionals

Survey of what people know, do and actually use

Sanctioned tools and rules communicated

Five sessions on their own work cases

Behaviours re-rated four weeks on

Output

A capability baseline per level

In detail

An AI usage policy and targets

In detail

Evidence of knowledge, skill and behaviour

In detail

A results report for the board

In detail
How it works, in full

What changes.

The engagement is judged on the shift it produces, measured against the baseline it started from.

Today

After the first cycle

AI use is uneven, unmeasured and largely unseen

A capability baseline per level and role, on a government-backed standard

Expectations split between hype and scepticism

One agreed direction, with decision rights and accountabilities by level

Confidential data on personal accounts

Sanctioned, observable use on governed tools or a secure sandbox

Training completions and satisfaction scores

Evidence per competency: knowledge, skills and behaviours, before and after

Risk discovered after the first incident

Usage rules, risk entries and an escalation path in place before scale

What you will hold.

Deliverables, not attendance records. Each is produced inside the engagement and retained by the enterprise.

Executives

AI strategy on a page

Priorities, guardrails and decision rights, signed off by the board.

Leadership

AI usage policy

Sanctioned tools, the limits of acceptable use and who decides what.

Executives

Risk register entries and an escalation path

The behavioural risks of adoption, named, owned and routed.

Managers

Team readiness assessment

Risk and readiness per team, with a live compliance view.

Managers

Opportunity cases

Use cases built from team insight, ready for leadership to act on.

The board

Cohort report per level

Capability per competency, before and after, with the workplace evidence behind each score.

Built for the enterprise.

In an enterprise, a direction set at the top holds only if managers can carry it and professionals can apply it. The solution develops all three levels at once, with explicit accountabilities and one measure.

Uses AI to

Accountable for

Leaves with

Executives

Board and executive committee

See the journey

Decide where AI creates value and where it must not be used

Direction, guardrails, investment and public trust

An AI strategy on a page and a governance pack the board can sign

Managers

Middle management and team leads

See the journey

Redesign team work and keep use inside the rules

Team readiness, compliance and the opportunity pipeline

A team readiness assessment and opportunity cases leadership can act on

Professionals

Everyone who uses AI in their role

See the journey

Draft, analyse and decide faster, and verify before acting

Verified output, protected data and escalation when unsure

A verified application log and a personal development plan

How it works For your role

Why it holds up.

Measured

Knowledge tested before and after, skills assessed on the work produced, behaviours rated at baseline and again in the workplace.

On a government-backed standard

Mapped to the UK AI Skills for Business competency framework, so capability is reported in terms a board, an auditor and a regulator recognise.

About the standard

Tailored

Cases from your sector, sessions on your tools, scope set by your baseline.

How tailoring works

Secure by design

Delivered on the AI tools you already govern, or in an enterprise-grade secure sandbox. No enterprise data enters a public tool.

The secure workspace

What it means for your function.

AI raises a different question in each part of the enterprise. Each gets a direct answer.

Executive leadership

Set the AI direction on evidence, and align the enterprise behind it.

A traceable return on AI investment that stands up to the board

AI-enabled products and services in market before rivals

What you get

Human resources

Clear roles and expectations for AI, at every level.

A workforce that adopts AI without the anxiety and quiet resistance that stall new tools

A clear picture of who holds which AI capability across a large, dispersed workforce

What you get

Learning and development

Learning built on your work, measured on knowledge, skills and behaviour.

AI learning that transfers to the job because the examples are the organisation's own work

Cohorts that work at every level of fluency without leaving anyone behind

What you get

Digital and technology

Adoption that keeps pace with the platform, on your security terms.

Adoption that matches the investment in the platform, measured beyond licence counts

Sanctioned, observable AI use in place of workarounds

What you get
All four, in full Why VCL

Find out where each level of your enterprise stands.

Tell us about your organisation and the levels you want assessed. We will come back within two working days to plan the Assess phase with you.

Request a diagnostic See the programme outline

Why AI requires a different capability model

Earlier technologies rewarded the people who learned the tool. AI rewards the people who can judge its output. Four properties of the technology explain why, and twelve patterns of behaviour determine whether adoption succeeds.

Non-deterministic output

AI systems are probabilistic. The same input can produce different outputs, so dependable use rests on verification and professional judgement rather than on operating the tool.

Plausible error

Generative models produce fluent, well-formed content that can be wrong. Human oversight is required before output informs a decision or reaches a client.

Value and exposure scale together

Productivity gains arrive with matching exposure: data leakage, bias, breaches of confidentiality and intellectual property risk grow at the same rate as the benefit.

Over-reliance and resistance

Workforce behaviour divides between automation bias and algorithm aversion. Both are recognised failure modes of adoption, and conventional change management addresses neither fully.

Twelve patterns of behaviour, in three groups.

None of these is a fault in the technology. Each is a predictable pattern of human behaviour, which is why the solution develops knowledge, skills and behaviours rather than tool operation. All twelve are tracked from Assess to Advance.

Trust and belief

How people read the machine

Fluency bias

Confident, well-formed output is read as correct, whether it is or not.

Automation bias

Output that looks complete is accepted, and verification decays into a formality.

System abandonment

One visible failure, and users conclude the tool is unreliable and stop using it.

False authority

Agreement is read as a second opinion, confidence as knowledge and tone as intent.

Judgement and skill

How people work with it

Skill decay

Skills delegated to AI atrophy until people can no longer judge the output for themselves.

Delegation failure

Thin instructions produce poor output; users blame the tool and disengage.

Peer conformity

People who would challenge AI alone defer to it in a group.

Unsanctioned use

Confidential data flows through personal accounts when sanctioned tools fall short.

Organisation and governance

How the organisation responds

Prohibition backfire

Banning AI removes visibility and moves use onto personal devices and accounts.

Risk creep

Use expands from low to high stakes without verification standards rising to match.

Unmeasured adoption

Scale without a baseline: value cannot be demonstrated and errors cannot be traced.

Voice uniformity

Similar tools and similar prompts erode the organisation's distinctive voice.

Request a diagnostic

Assess

3 to 4 weeks

A short survey, structured interviews and an inventory of the AI tools already in use establish where each level of the organisation stands.

What happens

A survey of each level to be assessed, mapped to the competency standard

Structured interviews with executives, a sample of managers and a sample of professionals

An inventory of the AI tools in use, sanctioned and unsanctioned

A baseline of what people know, can do and actually do with AI, per level

You get A capability baseline per level and a recommended scope.

Request a diagnostic

Align

2 to 3 weeks

Leadership agrees the direction and the rules before anyone is trained: what AI is for, which tools are sanctioned, who decides what, and how success will be measured.

What happens

A leadership session that converts divergent expectations into one agreed direction

Sanctioned tools, decision rights and the limits of acceptable use

Roles and expectations for AI, defined per level

Targets for the first delivery cycle, set against the baseline

You get An AI usage policy, agreed roles and targets for the first cycle.

Request a diagnostic

Deliver

8 to 12 weeks per cycle

Each cycle activates knowledge, applies it to real work and anchors it as working practice. Each cycle builds on the last.

What happens

Activate: concise inputs on how AI works, where it fails and how output is verified, with a knowledge check before and after

Apply: practice on the organisation's own cases, on sanctioned tools or in the secure sandbox, assessed against a rubric

Anchor: commitments and workplace assignments, followed up and rated four weeks on

Each level on its own rhythm: briefings for executives, workshops for managers, applied sessions for professionals

You get Knowledge scores before and after, assessed work products, and behaviour commitments followed up in the workplace.

Request a diagnostic

Advance

3 to 4 weeks, then quarterly

The baseline measures are repeated, the change is reported, and the next step is agreed: a further cycle, another level, or hand-over to the organisation.

What happens

The Assess measures, repeated

A results report per level and per competency, with the workplace evidence behind each score

The next step agreed: a further cycle, another level, or hand-over

A quarterly check to confirm that the change has held

You get A results report for the board.

Request a diagnostic

Executives

Board and executive committee. Set the direction, own the governance and sponsor the use cases that matter.

6 briefings · 11-week journey

Short fortnightly briefings with ten-minute pre-reads. Each briefing produces a governance deliverable the organisation retains.

Sessions

AI strategy and value

Briefing · 90 min

Governance, ethics and regulatory readiness

Briefing · 2 h

Investment, procurement and platform decisions

Briefing · 90 min

Workforce transformation and culture

Briefing · 2 h

Risk, assurance and incident readiness

Briefing · 90 min

External representation and public trust

Briefing · 1 h

Deliverables

An AI strategy on a page: priorities, guardrails and decision rights

A governance and regulatory readiness checklist, with criteria for evaluating vendors and platforms

Risk register entries, an incident escalation path, a workforce transformation plan outline, an external positioning statement and a stakeholder map

Request a diagnostic

Managers

Middle management and team leads. Translate the direction into how their teams work, and coach adoption day to day.

6 workshops · 11-week journey

Fortnightly workshops, each followed by an applied assignment completed in the manager's own team: compliance, opportunity cases and vendor oversight.

Sessions

Leading by example: championing responsible AI

Workshop · 3 h

Coaching and first-line support for AI use

Workshop · 3 h

Policy, compliance and risk in the team

Workshop · 3 h · with workplace assignment

Spotting opportunities and feeding back to leadership

Workshop · 2.5 h · with workplace assignment

Managing the team through AI-driven change

Workshop · 3 h

Owning an AI initiative: alignment and third-party tools

Workshop · 4 h · with workplace assignment

Deliverables

A team-level AI risk and readiness assessment with a live compliance view

Opportunity cases, built from team insight, that leadership can act on

A vendor oversight assignment applying the organisation's assurance criteria to a third-party tool

Request a diagnostic

Professionals

Everyone who uses AI in their role. Apply AI to their own work with skill, judgement and accountability.

5 sessions · 8-week journey

Two e-learning sessions, three workshops, a workplace application log and a personal development plan.

Sessions

Recognising AI and your rights as a user

E-learning · 1 h

Personal data, privacy and trustworthy content

E-learning · 1.5 h

Adopting AI tools in everyday work

Workshop · 3 h

Professional judgement and responsible use of AI outputs

Workshop · 3 h

Adapting your role and growing with AI

Workshop · 2 h · with workplace assignment

Deliverables

A workplace application log per participant: the task, the AI output, the check applied and the decision taken

A personal development plan that responds to AI-driven change in the role

Knowledge scores before and after, and behaviour ratings at baseline and four weeks on, per competency

Request a diagnostic

About the standard

The AI Skills for Business competency framework defines the AI capability each role in an organisation requires. VCL applies it to the people who use AI in their roles, at three levels.

Sponsored

UK Department for Science, Innovation and Technology

Funded and led

Innovate UK BridgeAI programme

Developed by

The Alan Turing Institute, with Digital Catapult, STFC Hartree Centre and BSI

Current version

3.0.0, March 2026, refined through industry consultation

How VCL applies it to three levels.

Every responsibility carried at a VCL level is a framework responsibility, and every session traces to the competencies it serves. Two areas are deliberately out of scope: technical build roles and public advocacy, neither of which sits within the population the solution serves.

The framework

6

personas

Who uses AI, from AI user to AI leader

41

duties

What each persona is responsible for

92

competencies

What must be demonstrated for each duty

950+

statements

Knowledge, skill and behaviour statements per competency

Applied to the people who use AI in their roles

The VCL solution

3

levels

Executives, managers and professionals

31

duties

The responsibilities people carry when AI enters their work

58

competencies

Each traced to one or more framework competencies

500

statements

The learning outcomes of every session

Which framework personas each level carries

Executives

Board and executive committee

Leader. 8 Leader duties, unchanged.

Managers

Middle management and team leads

Change Champion, Professional (selected duties). 6 Change Champion, 3 Leader (scaled to team) and 2 Professional duties.

Professionals

Everyone who uses AI in their role

Worker, Citizen. 7 Worker and 5 Citizen duties (the literacy baseline).

Whole persona carried over, or selected duties only, as marked.

Request a diagnostic

How tailoring works

Three inputs, gathered before the first session, shape every cohort.

Your industry

Before the first session we gather the situations your people actually face: the documents they draft, the decisions they check, the customers they answer. These become the cases, so no participant practises on an example from another sector.

Your technology environment

Sessions run on the AI tools the organisation has adopted or is evaluating, or in the secure sandbox where no sanctioned tool is yet in place. Verification practice is taught to hold whichever tool is in front of the participant.

Your baseline

The Assess results set the scope: which sessions each level needs, how far each goes, where the first cycle begins and what the targets are. Each subsequent cycle is scoped against the results of the last.

Session length, cohort size and journey length are agreed in the Align phase.

Request a diagnostic

The secure workspace

Delivery on your sanctioned tools, or in an enterprise-grade secure sandbox. In either case, organisational data never enters a public tool.

Your sanctioned tools

Delivery on the enterprise AI tools the organisation already governs, under its own access controls.

An enterprise-grade secure sandbox

Where no sanctioned tool is yet in place: named data residency, no training on inputs, one login per participant, access closed at programme end.

What the sandbox provides.

Data sovereignty

Data residency the organisation can name and audit.

No training on inputs

Nothing a participant enters is used to train any model.

Model of choice

The same prompt can be run on two or three models. The verification practice is the same whichever tool the organisation adopts.

One login per participant

Issued with the first workshop invitation, sample files pre-loaded, access closed at programme end.

Three questions, asked of every tool.

Safe practice is taught by example. The three questions participants learn to ask of the workspace become the three they ask of every AI tool afterwards.

Which country is my data stored in?

Is anything I enter used to train a model?

Who can see it?

Designed to meet the expectations regulators now set.

Article 4 of the EU AI Act requires organisations that deploy AI to ensure a sufficient level of AI literacy among their staff. The NIST AI Risk Management Framework and ISO/IEC 42001 place workforce competence at the centre of responsible AI. The solution is designed to meet these expectations with evidence per competency rather than attendance records.

EU AI Act, Article 4 NIST AI Risk Management Framework ISO/IEC 42001
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