Free AI Strategy Readiness Assessment

How to Use This Tool ▾

Purpose: This tool is a self-assessment instrument for boards and executives of multinational organisations. It helps leadership judge how ready the organisation is to turn AI from experiment into value, across six strategy dimensions. It is the strategy counterpart to the Celagenix AI Governance Assessment.

Who should complete this assessment: This assessment is designed for completion by the executive team or an AI steering group, ideally as a collective exercise. Individual leaders may also complete it independently and compare scores as a basis for a strategy discussion.

Scoring: For each of the 30 questions, select one of three responses: Yes - In Place (2 points): leadership is satisfied this is comprehensively in place. Partially / In Progress (1 point): some activity exists but material gaps remain. No - Not in Place (0 points): this is not in place, or leadership cannot confirm it.

Interpreting "Yes": A Yes response should reflect leadership's own confident assessment, not an assertion from a project team. If leadership cannot verify a practice is in place and working, the honest response is Partially or No.

After completing the assessment: Review your readiness level and dimension scores. Use low dimension scores to prioritise where to act first. Consider a Celagenix AI Strategy engagement to turn the diagnosis into a prioritised strategy and a first build.

Progress: Your progress saves automatically as you work. To return to a partially completed assessment, simply reopen this file - your responses will be restored. Use Save Progress to manually save at any point. Use Reset to start a new assessment (this clears all saved responses).

Reference Frameworks - Verified as at 13 August 2026

Framework	Version / Status	Custodian	Effective Date
NIST AI Risk Management Framework (AI RMF 1.0)	January 2023	US NIST	In force
ISO/IEC 42001:2023 - AI Management System	2023	ISO / IEC	In force
ISO/IEC 23894:2023 - AI Risk Management	2023	ISO / IEC	In force
OECD Principles on AI	2019, updated 2024	OECD	In force
EU AI Act	2024 (phased)	European Union	Phasing in from 2025
SAEF - Sovereign AI Execution Framework	v1 · 2026	Celagenix	Current

Note: this readiness assessment is sector-agnostic and not specific to any jurisdiction's law. It draws on widely used AI management and risk frameworks and on the Celagenix SAEF framework. For the governance and legal-oversight side of AI, use the companion Celagenix AI Governance Assessment.

Important Notice - Please Read Before Using This Tool ▾

What this tool is: T-AIS-GL001 is a self-assessment instrument to help boards and executives evaluate their organisation's AI strategy readiness. It is provided by Celagenix as an educational and strategy-development tool.

What this tool is not: This tool is not a formal evaluation, audit, or advice of any kind - strategic, legal, technical or otherwise. Completing it does not confirm readiness or compliance with anything. Scores are based on your own self-assessment and have not been independently verified.

Framework basis: The tool draws on widely used AI management and risk frameworks (NIST AI RMF, ISO/IEC 42001 and 23894, the OECD AI Principles and the EU AI Act) and on the Celagenix SAEF framework. It is sector-agnostic and not specific to any jurisdiction's law.

Governance first: AI strategy is safest to act on once governance guardrails are set. For the governance side, use the companion Celagenix AI Governance Assessment. This strategy tool assumes that baseline is either in place or being established in parallel.

Professional engagement: Celagenix recommends supplementing this self-assessment with a facilitated engagement. For a structured diagnosis and a prioritised strategy, delivered as orchestrated StratDash® sprints, see Celagenix AI Strategy Consulting.

References-verified date: All framework references were verified as at 13 August 2026. This tool should be reviewed by August 2027, or earlier if material changes occur. Scope: Global · Enterprise · Sector-agnostic.

AI Strategy Readiness - Score Dashboard

0% Readiness

Not Yet Started

Complete the assessment below to gauge your organisation's AI strategy readiness. Answer all 30 questions across 6 dimensions to receive your full score and recommendations.

Your AI strategy gap is a value gap.

Your score indicates the organisation is at the Ad Hoc or Developing stage. AI activity may be happening, but without an enterprise strategy tying it to outcomes, most of it will not scale or pay off. A self-assessment is the right first step. Closing a gap of this size needs a deliberate strategy, not another pilot.

A Celagenix AI Strategy engagement (SAEF) provides:

A structured readiness diagnosis across all six strategy dimensions, top-down and bottom-up
A clear enterprise AI strategy tied to the outcomes your leadership already tracks
A prioritised, value-versus-feasibility use-case portfolio and a first build to prove value
Delivery through orchestrated StratDash® sprints, sequenced after your governance baseline

Talk to us about an AI Strategy engagement →

Strong strategy. The gap is in scaling value.

Your score confirms a coherent AI strategy is broadly in place. The dimension breakdown shows where value is leaking, commonly the operating model, adoption, or the discipline of scaling beyond pilot. Closing those gaps efficiently is where a focused engagement pays for itself.

A Celagenix AI Strategy engagement (SAEF) provides:

An expert readiness diagnosis that validates your self-assessment and finds the blind spots
A prioritised roadmap that turns your strongest use cases into scaled, measured value
An operating model and delivery cadence (StratDash® sprints) to move from pilot to production
Benchmarking against how comparable multinational organisations are executing AI

Request an AI Strategy diagnostic →

AI-native execution, well evidenced.

Your score indicates an advanced, well-executed AI strategy, tied to outcomes and scaling across the enterprise. Sustaining that advantage as the technology and competitive landscape move requires ongoing discipline, and an outside view to keep compounding value.

A Celagenix AI Strategy engagement (SAEF) provides:

Periodic independent review of AI strategy execution and value realisation
Fresh benchmarking as enterprise AI practice and the frontier evolve
Identification of the next wave of use cases before competitors move
A partner that both advises and builds, when you want the next capability shipped

Schedule an AI Strategy review →

0 - 25% · 0-15 pts

Ad Hoc

AI is experimental and uncoordinated. No enterprise AI strategy. Value is unproven.

26 - 50% · 16-30 pts

Developing

Pockets of activity and some strategy, but fragmented. Most pilots stall before scale.

51 - 75% · 31-45 pts

Defined

A coherent AI strategy is broadly in place. Specific dimensions need strengthening to scale value.

76 - 100% · 46-60 pts

Advanced

AI strategy is embedded, executed, and delivering measured value across the enterprise.

Yes - In Place = 2 points

Partially / In Progress = 1 point

No - Not in Place = 0 points

AI Strategy & Policy

AI Ambition & Strategic Mandate

0/10

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AI delivers value when it is treated as an enterprise strategy, not a set of experiments. This dimension looks at whether there is a clear, owned AI ambition tied to business outcomes, with the mandate, funding and board visibility to steer it. The most common reason AI fails to pay off is the absence of exactly this.

T-AIS-GL001.1.1

Has the organisation articulated a clear, board-endorsed AI ambition that is explicitly tied to its business strategy and to specific commercial outcomes?

SAEF - Aim: The single biggest cause of failed AI is the absence of an enterprise strategy tied to outcomes. A board-endorsed ambition is where value starts. Aligns to NIST AI RMF: Govern.

T-AIS-GL001.1.2

Is there a single accountable owner (an executive or forum) with a defined mandate and decision rights for AI across the enterprise, rather than fragmented ownership by function?

SAEF - Orient: Fragmented ownership produces AI sprawl - many uncoordinated experiments, none scaled. One accountable mandate with decision rights is the fix.

T-AIS-GL001.1.3

Has leadership agreed which business outcomes AI is expected to move (for example revenue growth, win rate, cost-to-serve, risk reduction) and over what horizon?

SAEF - Aim: AI pays off when it moves outcomes leadership already tracks (revenue, win rate, cost-to-serve), not activity. Naming the target outcomes is step one.

T-AIS-GL001.1.4

Is AI treated as an enterprise strategic priority with dedicated funding, rather than as a series of departmental experiments competing for budget?

SAEF - Orient: Enterprise funding separates a strategy from departmental experiments competing for budget. Dedicated investment is a readiness signal.

T-AIS-GL001.1.5

Does the board receive regular, structured updates on AI strategy and the value delivered, sufficient to steer direction and reallocate resources?

SAEF - Orient: Boards steer AI only with structured visibility of strategy and value delivered. Without it, direction and reallocation are guesswork.

Risk & Assurance

Data & Technology Foundation

0/10

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AI runs on data and infrastructure. This dimension assesses whether the data behind your priority use cases is available, trusted and well-governed, and whether your platforms and integrations can move AI from prototype to production at scale. Data fragmentation is the single biggest readiness barrier.

T-AIS-GL001.2.1

Is the data required for the organisation's priority AI use cases available, accessible, and of sufficient quality, rather than fragmented across siloed or legacy systems?

SAEF - Assess: Data fragmentation is the number-one barrier to enterprise AI. Priority use cases need available, accessible, quality data. Aligns to ISO/IEC 42001.

T-AIS-GL001.2.2

Are data ownership, definitions, and access rights clear enough that teams can build AI on trusted data without lengthy reconciliation?

SAEF - Assess: Unclear data ownership and definitions force teams into lengthy reconciliation before any AI is built. Trusted, well-governed data is foundational.

T-AIS-GL001.2.3

Does the organisation have a platform and infrastructure capable of moving AI use cases from prototype into production and scaling them reliably?

SAEF - Assess: Most pilots stall for want of a path to production. Platform and infrastructure that scale reliably is what moves AI beyond the prototype.

T-AIS-GL001.2.4

Are the organisation's core systems and workflows integrated enough that AI can be embedded into them, rather than bolted alongside as a standalone tool?

SAEF - Activate: Value is captured when AI is embedded into integrated systems and workflows, not bolted alongside as a standalone tool. Aligns to NIST AI RMF: Map.

T-AIS-GL001.2.5

Is there a clear approach to selecting, integrating, and governing AI models and vendors across the enterprise, rather than uncoordinated point solutions?

SAEF - Assess: Uncoordinated point solutions create cost and risk. A clear approach to models and vendors keeps the AI estate coherent and governable.

Ethics & Accountability

Governance & Responsible-AI Guardrails

0/10

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Strategy is only safe to act on inside guardrails. This dimension checks whether decision rights, a risk appetite and responsible-AI practices let teams move quickly and safely, and whether that governance baseline connects to the strategy rather than sitting apart from it. It is the bridge from the companion AI Governance Assessment.

T-AIS-GL001.3.1

Are there approved guardrails, decision rights, and a defined AI risk appetite that let teams pursue AI use cases quickly and safely, rather than blocking or ignoring them?

SAEF - Orient / Govern: Guardrails, decision rights and a risk appetite let teams move fast and safely. Without them, AI is either blocked or ungoverned. The companion AI Governance Assessment covers this in depth.

T-AIS-GL001.3.2

Is there a clear boundary for what AI may and may not be used for, including confidentiality, personal data, and privileged or regulated material?

SAEF - Orient: A clear boundary for confidential, personal, privileged and regulated material is a precondition for safe AI use. Aligns to the EU AI Act and ISO/IEC 23894.

T-AIS-GL001.3.3

Are responsible-AI practices (human oversight, bias and reliability checks, auditability) built into how use cases are approved and deployed?

SAEF - Activate: Human oversight, bias and reliability checks and auditability must be built into how use cases are approved, not added later. Aligns to OECD AI Principles; NIST AI RMF: Manage.

T-AIS-GL001.3.4

Does the organisation's AI governance baseline connect to its AI strategy, so that guardrails enable execution rather than sit as a separate policy?

SAEF - Orient: Guardrails should enable execution, not sit as a separate policy. Governance and strategy are two sides of the same coin. See the companion AI Governance Assessment.

T-AIS-GL001.3.5

Are third-party and vendor AI risks (data handling, model reliability, lock-in) assessed before adoption and monitored thereafter?

SAEF - Assess: Third-party and vendor AI introduce data, reliability and lock-in risk. Assess before adoption and monitor thereafter. Aligns to ISO/IEC 42001; NIST AI RMF.

Data & Cybersecurity

Talent, Skills & Operating Model

0/10

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AI ambition needs people and an operating model to deliver it. This dimension examines whether you have access to the right skills, a clear way for central teams, business units and partners to work together, and the leadership fluency and workforce literacy that turn capability into adoption.

T-AIS-GL001.4.1

Does the organisation have access to the skills required to deliver its AI ambition - data, engineering, product, and domain expertise - whether built, bought, or partnered?

SAEF - Aim: Delivery depends on access to data, engineering, product and domain skills, whether built, bought or partnered. Capability is a readiness gate.

T-AIS-GL001.4.2

Is there a defined operating model for AI, clarifying how central teams, business units, and partners work together to deliver and run use cases?

SAEF - Activate: A defined operating model clarifies how central teams, business units and partners deliver and run use cases. Ambiguity here stalls scale. Aligns to ISO/IEC 42001: resources.

T-AIS-GL001.4.3

Are business and functional leaders equipped to identify, sponsor, and adopt AI use cases, rather than leaving AI to a technical team alone?

SAEF - Aim: AI succeeds when business leaders sponsor and adopt it, not when it is left to a technical team alone. Leadership fluency is decisive.

T-AIS-GL001.4.4

Is there a plan to build broad AI literacy across the workforce, so adoption is not limited to a small group of specialists?

SAEF - Activate: Broad AI literacy across the workforce is what turns capability into adoption. Specialist-only knowledge caps the return.

T-AIS-GL001.4.5

Can the organisation retain and develop the people who deliver AI, rather than depending on individuals whose departure would stall progress?

SAEF - Activate: Dependence on a few individuals is fragile. Retaining and developing AI talent protects momentum.

Procurement & Deployment

Adoption, Workflow & Culture

0/10

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AI only creates value when it changes how work is done. This dimension assesses whether workflows are redesigned around AI, whether people actually adopt it, and whether the culture and change management move use cases from pilot into everyday use. Workflow redesign is the number-one driver of AI ROI.

T-AIS-GL001.5.1

When AI is introduced, are the underlying workflows redesigned so the value is captured, rather than layering AI on top of an unchanged process?

SAEF - Activate: Workflow redesign is the number-one driver of AI ROI. Layering AI on an unchanged process rarely pays off.

T-AIS-GL001.5.2

Do employees actually use the AI capabilities the organisation has deployed, with adoption measured rather than assumed?

SAEF - Activate: Deployed capability only creates value if people use it. Measured adoption beats assumed adoption.

T-AIS-GL001.5.3

Is there active change management - communication, training, incentives - to move AI from pilot into everyday use?

SAEF - Activate: Active change management - communication, training, incentives - is what moves AI from pilot into everyday use.

T-AIS-GL001.5.4

Does the culture support responsible experimentation, learning from failed pilots without stigma, while still holding a bar for value?

SAEF - Activate: A culture that experiments responsibly and learns from failed pilots, while still demanding value, is what compounds capability.

T-AIS-GL001.5.5

Are frontline and functional teams involved early enough that AI solutions fit how the work is really done?

SAEF - Prioritise: Early involvement of frontline and functional teams makes AI solutions fit how the work is really done, and adoption follows.

Reporting & Disclosure

Value, Prioritisation & Scale

0/10

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The final dimension is about turning activity into compounding value: prioritising use cases on value and feasibility, measuring against the outcomes leadership already tracks, and scaling beyond pilot. Fewer than one in ten organisations scale AI beyond pilot, and this is where that gap is closed.

T-AIS-GL001.6.1

Does the organisation prioritise AI use cases on a consistent basis (value versus feasibility), rather than pursuing whatever is loudest or newest?

SAEF - Prioritise: Consistent value-versus-feasibility prioritisation prevents chasing whatever is loudest or newest. It is how the first build is chosen well.

T-AIS-GL001.6.2

Is the value of AI use cases measured against the business outcomes leadership already tracks, rather than activity or productivity anecdotes?

SAEF - Measure / Activate: Value must be measured against outcomes leadership already tracks, not activity or productivity anecdotes. Aligns to NIST AI RMF: Measure.

T-AIS-GL001.6.3

Has the organisation moved at least one AI use case beyond pilot into sustained production at scale?

SAEF - Activate: Fewer than one in ten organisations scale AI beyond pilot. At least one use case in sustained production is a genuine maturity marker.

T-AIS-GL001.6.4

Is there a mechanism to stop, scale, or reallocate AI investment based on evidence of value, rather than sunk cost?

SAEF - Scale: A mechanism to stop, scale or reallocate based on evidence, not sunk cost, is what keeps the AI portfolio productive.

T-AIS-GL001.6.5

Is AI value delivery improving over time, with a repeatable path from idea to a deployed, measured outcome?

SAEF - Scale: A repeatable path from idea to a deployed, measured outcome is the sign of a strategy that is executing, not just planning.