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AI readiness assessment
AI readiness assessment.
Evaluating your organisation's genuine readiness for AI adoption across data, infrastructure, operating model and cultural capability, before investment decisions are made.
The challenge
Most organisations are not as AI-ready as they believe.
The pressure to adopt AI is significant and accelerating. Boards are asking questions, competitors are announcing initiatives and the technology itself is developing at a pace that makes waiting feel risky. The result is that many organisations commit to AI investment before they have honestly assessed whether they are ready to benefit from it.
Readiness for AI is not primarily a technology question. It is a question about data quality, organisational capability, operating model design and leadership clarity. Organisations that skip this assessment tend to invest heavily in tools and platforms, then discover that the foundational conditions for success were never in place.
A Medasi AI readiness assessment gives you an honest, evidence-based view of where you are and a clear, sequenced path to where you need to be before significant AI investment is made.
A note of caution
AI readiness assessments should be honest, not optimistic. The value of this engagement is in surfacing the gaps, not in validating a decision that has already been made. If your organisation isn't ready, we will tell you that, along with a realistic plan for what readiness actually requires.
Common findings
Data exists but is fragmented, inconsistently governed and not AI-ready
Use cases are aspirational rather than grounded in specific business outcomes
Infrastructure is technically capable but not configured for AI workloads
Governance for AI decision-making and risk is absent or immature
What we assess
Six dimensions of genuine AI readiness.
Data quality & availability
The foundational requirement. AI is only as good as the data that trains and informs it.
Is data consistently defined and governed?
Is it accessible to the systems that need it?
Is there a data quality baseline in place?
Technology infrastructure
Whether the technology estate can support AI workloads — compute, integration, security and scalability.
Is cloud infrastructure configured for ML?
Can systems integrate and share data at pace?
Is security architecture AI-appropriate?
Operating model
Whether structures, processes and accountabilities are designed to operationalise AI outputs — not just produce them.
Who owns AI outputs and decisions?
Are workflows designed to act on AI insight?
Is there a centre of AI capability or excellence?
Leadership & governance
Executive understanding, board oversight and risk governance for AI decision-making.
Does leadership understand AI's limitations?
Is there a framework for AI risk and ethics?
Who is accountable for AI outcomes at board level?
Workforce & culture
The human dimension — skills, appetite for change and cultural readiness to work alongside AI.
Is there internal AI and data science capability?
Is the workforce open to AI-augmented working?
Is there a plan for upskilling and change management?
Use case clarity
Whether the organisation has identified specific, measurable, achievable AI use cases — not just aspirations.
Are use cases tied to specific business outcomes?
Is there a prioritisation framework for AI investment?
Is there clarity on build vs buy vs partner?
How we work
A structured four-week assessment that produces actionable clarity.
1.
Scoping & framing
Agreeing the assessment scope, identifying stakeholders and establishing the evidence base. Week one.
2.
Dimension assessment
Structured interviews, document review and technical evaluation across all six readiness dimensions. Weeks two and three.
3.
Readiness scoring
Synthesis of findings into a scored readiness profile — with evidence, not opinions, behind each rating.
4.
Roadmap & recommendations
A sequenced readiness roadmap — what to fix, in what order, and what investment is required before AI adoption makes sense. Week four.
What you get
Clear outputs that inform real investment decisions.
Readiness assessment report
A scored, evidence-based assessment across all six dimensions, with findings and supporting detail for each.
Capability gap analysis
A clear view of the gaps between current readiness and the requirements for your specific AI ambitions.
Who this is for
Leaders who want to invest in AI when it will actually work.
This engagement is most valuable for organisations that are serious about AI adoption but want to avoid the expensive mistake of investing before the foundational conditions are in place. It is equally valuable for those who have already begun an AI programme and want an independent view of whether it is well-founded.
Chief Information / Technology Officer
Leading technology strategy and AI investment decisions
Chief Data Officer
Accountable for data capability and AI enablement
Chief Executive Officer
Accountable for the AI investment thesis to the board
PE operating partner
Assessing AI capability as part of a value creation thesis
When organisations engage us
Pre-investment
Before committing significant capital to AI platforms, tools or capability — to ensure the investment is well-timed.
Programme checkpoint
An existing AI programme is underway but not delivering expected results — an independent assessment of what's missing.
Board mandate
The board has asked for an AI strategy and the executive needs an honest readiness picture before developing one.
M&A due diligence
Assessing the AI and data readiness of an acquisition target as part of investment appraisal.
Start here
Find out where you really stand before you invest.
A conversation about your AI ambitions and an honest assessment of what readiness actually requires.