StratDash® AI · Free tool
The AI Readiness Assessment
Most organisations do not fail at AI for want of technology. They fail for want of a strategy that aims it. In ten minutes, score your readiness to turn AI into value, across the six dimensions that decide whether it pays off.
30 questions6 dimensionsNo email requiredScored live
0%Readiness
Your standing
Not yet scored
Answer the six dimensions below to gauge your organisation's readiness to turn AI into value. No email required, your answers stay in this browser.
How it works
Six dimensions of AI readiness
Each dimension maps to a stage of the StratDash AI method, Orient, Assess, Aim, Prioritise, Activate. Answer honestly: score your organisation as it operates today, not as you intend it to. Progress saves automatically in this browser.
Yes, in place = 2
Partial = 1
No = 0
01
Aim
AI Ambition & Strategic Mandate
0/10
▾
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.
R-AIM-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?
R-AIM-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?
R-AIM-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?
R-AIM-1.4
Is AI treated as an enterprise strategic priority with dedicated funding, rather than as a series of departmental experiments competing for budget?
R-AIM-1.5
Does the board receive regular, structured updates on AI strategy and the value delivered, sufficient to steer direction and reallocate resources?
02
Assess
Data & Technology Foundation
0/10
▾
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 into production and scale it.
R-ASS-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?
R-ASS-2.2
Are data ownership, definitions, and access rights clear enough that teams can build AI on trusted data without lengthy reconciliation?
R-ASS-2.3
Does the organisation have a platform and infrastructure capable of moving AI use cases from prototype into production and scaling them reliably?
R-ASS-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?
R-ASS-2.5
Is there a clear approach to selecting, integrating, and governing AI models and vendors across the enterprise, rather than uncoordinated point solutions?
03
Orient
Governance & Responsible-AI Guardrails
0/10
▾
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.
R-ORI-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?
R-ORI-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?
R-ORI-3.3
Are responsible-AI practices (human oversight, bias and reliability checks, auditability) built into how use cases are approved and deployed?
R-ORI-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?
R-ORI-3.5
Are third-party and vendor AI risks (data handling, model reliability, lock-in) assessed before adoption and monitored thereafter?
04
Orient
Talent, Skills & Operating Model
0/10
▾
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 and literacy to sponsor and adopt AI.
R-ORI-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?
R-ORI-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?
R-ORI-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?
R-ORI-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?
R-ORI-4.5
Can the organisation retain and develop the people who deliver AI, rather than depending on individuals whose departure would stall progress?
05
Activate
Adoption, Workflow & Culture
0/10
▾
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.
R-ACT-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?
R-ACT-5.2
Do employees actually use the AI capabilities the organisation has deployed, with adoption measured rather than assumed?
R-ACT-5.3
Is there active change management, communication, training, incentives, to move AI from pilot into everyday use?
R-ACT-5.4
Does the culture support responsible experimentation, learning from failed pilots without stigma, while still holding a bar for value?
R-ACT-5.5
Are frontline and functional teams involved early enough that AI solutions fit how the work is really done?
06
Prioritise
Value, Prioritisation & Scale
0/10
▾
This 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 past the pilot stage, so this is where readiness is truly tested.
R-PRI-6.1
Does the organisation prioritise AI use cases on a consistent basis (value versus feasibility), rather than pursuing whatever is loudest or newest?
R-PRI-6.2
Is the value of AI use cases measured against the business outcomes leadership already tracks, rather than activity or productivity anecdotes?
R-PRI-6.3
Has the organisation moved at least one AI use case beyond pilot into sustained production at scale?
R-PRI-6.4
Is there a mechanism to stop, scale, or reallocate AI investment based on evidence of value, rather than sunk cost?
R-PRI-6.5
Is AI value delivery improving over time, with a repeatable path from idea to a deployed, measured outcome?
Your result
Where you stand, and what to do next
Your readiness band updates as you answer. The lower the score, the more likely AI stays a set of experiments rather than a source of value.
0–25% · 0–15 pts
Ad Hoc
26–50% · 16–30 pts
Developing
51–75% · 31–45 pts
Defined
76–100% · 46–60 pts
Advanced
Answer the dimensions above to reveal your readiness band and your recommended next step.
Start with a Strategy Sprint
You have appetite for AI but not yet a strategy to aim it. The StratDash AI Sprint gives you an owned ambition tied to outcomes, a prioritised shortlist of use cases, and a clear first move, in weeks, not quarters.
- An AI ambition tied to outcomes your board already tracks
- A prioritised use-case shortlist ranked on value and feasibility
- A defined first move, with the mandate to run it
Join up the pieces
The foundations are appearing but not yet connected, and pilots stall before production. A StratDash AI Sprint sequences what you have into a coherent strategy, and picks the use cases most likely to reach scale.
- A strategy that connects your data, talent and governance foundations
- A prioritisation basis that stops the loudest idea winning
- A route from stalled pilots to a use case that scales
Compound the return
Your strategy is working. The remaining value is in the operating model, adoption, and the discipline of scaling beyond pilot. We focus a Sprint on the two or three dimensions holding your return back.
- A sharper operating model for how AI gets delivered and run
- Adoption and workflow redesign that captures the value
- A repeatable path from idea to a deployed, measured outcome
Sustain the advantage
You are in strong shape, tied to outcomes and scaling. The task now is to hold the lead as the technology and competitive landscape move, prioritising ruthlessly and scaling what works.
- A live pipeline prioritised on value and feasibility
- A standard every new use case clears before it scales
- Continuous measurement against the outcomes that matter
From assessment to strategy
Turn a score into a strategy
This assessment is the diagnostic that opens a StratDash AI Sprint. When you want the strategy itself, we run the full method to a prioritised roadmap and a clear first move, proven by the products we build and run.
Book a Strategy Sprint →