What does an AI Readiness Assessment include?

We review data quality, infrastructure, talent, governance, and compliance to score your AI maturity and outline a phased adoption roadmap.

Do we need a data warehouse in place first?

No, but having centralised data accelerates the engagement. If it’s missing, we’ll recommend quick wins to get you there.

Typically 2 weeks for SMBs and up to 4 weeks for enterprises with multiple data sources.

You’ll receive a maturity scorecard, gap analysis, and a clear action plan covering data, people, and tech investments.

Audit your data, infrastructure, and teams to uncover your AI maturity. We'll map out the gaps and opportunities so you can adopt AI with confidence.

AI can accelerate growth—or drain resources. Our assessment shows if you’re ready, what needs fixing, and how to start safely.

Spot the quick wins

We look at everyday tasks and pinpoint where AI will save the most time or money first.
Fix the data basics

We check if your data is complete, tidy, and secure—so any AI model has solid fuel to run on.
Stay on the right side of the law

Privacy and legal red flags surface early, protecting you from fines and reputation damage.
Follow a simple roadmap

You leave with a step-by-step plan—pilot, measure, scale—complete with realistic timelines and costs.

Establish AI Vision

Through a focused workshop we clarify intended AI use-cases, measurable value, and organisational appetite for change.
Audit Data Assets

We catalogue data sources, quality, governance, and accessibility to determine fitness for machine-learning workloads.
Evaluate Capabilities

Current talent, tooling, and operating processes are assessed against industry benchmarks to reveal capability gaps.
Verify Compliance

Security, privacy, and ethical safeguards are reviewed so adoption plans align with regulatory obligations and brand values.
Score Maturity

Each dimension—data, people, process, and tech—is rated, giving you a concise snapshot of readiness.
Deliver Adoption Roadmap

Pilot projects, investment needs, and change-management steps are outlined to accelerate safe AI deployment.