AI Readiness Assessment — Discover Your Organisation's AI Deployment Stage

How would you describe your organisation's data infrastructure?

Think about where your core operational and customer data lives and how accessible it is.

Mostly paper and manual records

Operational data is not digitised or is fragmented across individuals.

Basic digital systems, limited integration

Core systems exist but data is siloed — no unified data layer.

Multiple systems with some integration

Some APIs and integration points exist; analytics capability is partial.

Integrated data platform with analytics

Unified data warehouse or lake exists; BI dashboards are operational.

Real-time unified data lake with ML pipeline

Streaming data, ML feature store, and model deployment infrastructure in place.

What is your current cloud adoption level?

AI deployment at scale requires cloud infrastructure. Where are you on the journey?

On-premise only

No cloud usage; all infrastructure is physical and on-site.

Initial cloud migration started

Piloting cloud for specific workloads; majority still on-premise.

Hybrid cloud — 30–50% of workloads

Material cloud presence; active migration programme underway.

Cloud-first — 70%+ of workloads

Cloud is the default for new deployments; legacy migration mostly done.

Cloud-native, full infrastructure-as-code

Containerised, auto-scaled, multi-cloud with full DevOps/MLOps pipeline.

How well documented are your core business processes?

AI agents require clear process definitions to automate. Undocumented processes require re-engineering before AI deployment.

Undocumented — relies on individual knowledge

Key processes live in people's heads; significant institutional knowledge risk.

Basic SOPs exist for some processes

Core processes partially documented but inconsistently followed.

Documented for most critical processes

SOPs exist and are reasonably current; process maps available for priority functions.

Fully documented with process maps and SLAs

Comprehensive process library with decision trees, SLAs, and exception handling.

Digitised, measurable, with clear SLAs

Processes are digitised, tracked, and performance-managed in real time.

How capable is your organisation at managing technology-driven change?

AI deployment requires sustained organisational change. Past change capability predicts AI adoption success.

Strong resistance — past technology projects failed

Technology change has been painful; organisation is wary of new systems.

Inconsistent adoption — significant effort required

Change happens but requires intensive management; adoption rates vary widely.

Moderate adoption with proper training and support

Organisation adopts change when properly supported; track record of managed programmes.

Strong change capability — track record of success

Organisation reliably adopts technology; change champions exist across functions.

Continuous improvement culture — AI/automation champions

Organisation actively seeks automation opportunities; AI adoption is embedded in culture.

Do you have executive-level sponsorship for AI initiatives?

Executive sponsorship is the single strongest predictor of enterprise AI success across our deployment portfolio.

No executive awareness or interest

AI is not on the executive agenda; no budget authority is engaged.

Individual champion but no budget authority

Someone cares about AI but cannot commit budget or programme resources.

C-suite interest but competing priorities

Senior leadership engaged but AI competes with other strategic initiatives for budget.

Named executive sponsor with AI mandate

A C-suite executive owns the AI programme with board visibility and budget authority.

Board-level AI strategy with dedicated programme

AI is a board agenda item; dedicated AI programme office with multi-year funding.

What is your current level of process automation?

Existing automation maturity determines how quickly AI can be layered on and at what scale.

Fully manual

All processes run on human effort; no automation in production.

Basic RPA or Excel macros for some tasks

Some repetitive tasks partially automated; not at scale.

Significant automation in 1–2 functions

A function or two runs material automation — AP processing, customer service scripts, etc.

Automation across multiple functions

Material automation across 3+ functions; dedicated automation COE or team.

Intelligent automation with AI/ML components

AI-driven decision automation in production; models deployed to core business processes.

What AI and data science talent exists in your organisation today?

Internal AI talent determines whether you build, partner, or fully outsource AI deployment.

No data or AI capability internally

No dedicated data science or AI engineering resource.

Basic data analytics team

BI / reporting analysts; no ML engineering capability.

Data science team with limited AI experience

Data scientists capable of modelling; no production ML deployment track record.

AI/ML engineers with deployed models in production

AI models running in production; ML engineering and DataOps capability present.

AI Centre of Excellence — embedded in core products

AI CoE established; AI embedded in product, operations, and customer experience.

How aligned are your systems with AI regulatory and compliance requirements?

EU AI Act, sector-specific AI regulations (MAS FEAT, FCA, HIPAA AI), and data privacy requirements must be addressed before enterprise AI deployment.

No compliance framework for AI

Regulatory requirements for AI not assessed or mapped.

Awareness but no governance in place

Team aware of AI regulations but no governance framework established.

Governance framework in development

AI ethics policy drafted; data privacy impact assessments underway.

AI governance and ethics policy in place

Formal AI governance; explainability and bias monitoring requirements defined.

Full AI risk framework — regulatory compliance mapped

Comprehensive AI risk framework; regulatory submissions made; third-party AI audits conducted.

How clear and quantified is your AI business case?

Clarity on ROI and use case determines how quickly AI can receive board approval and programme budget.

No clarity on AI ROI or use cases

AI is a vague ambition without defined business problem or financial case.

General interest — no quantified opportunity

Enthusiasm for AI but no specific use cases or ROI analysis conducted.

Identified use cases — unquantified business case

2–3 priority use cases identified; ROI analysis not yet completed.

Quantified use cases — board-approved budget

Business case built and approved; programme budget allocated.

AI programme with measured ROI from pilots

Pilots delivered, ROI measured and validated, scale-up investment approved.

How does AI feature in your competitive strategy?

AI's strategic position determines the urgency and scale of your programme — reactive organisations typically spend more for worse outcomes.

Not on the strategic agenda

AI is not discussed at strategy level; no competitive tracking of AI adoption.

Monitoring competitors' AI moves

Aware that competitors are deploying AI; not yet responding programmatically.

AI in strategy — no programme yet

AI mentioned in strategic planning but no funded programme with accountable owners.

Active AI programme — competitive differentiation intent

AI is a strategic priority with programme in flight; targeting competitive advantage.

AI is central to our competitive moat

AI is embedded in product, operations, and strategy; creates durable competitive advantage.