A few focused interviews. Seven pillars. You'll walk away knowing exactly where you stand — and what to fix first — before you spend another dollar scaling AI.
The numbers that matter
Readiness isn't a feeling. It's a score.
7 pillars
That's the whole picture
Strategy, Value, Operating Model, Governance, Technology, Data, Talent & Culture. Each is assessed on evidence and rolled into a single view, so your whole leadership team can read at a glance.
60 mins
That's one interview
Four to five questions per pillar, answered by the leader who actually owns it. No surveys. No self-assessment inflation. Just what you've done and what you can prove.
2 days
From interview to insight
Your report lands within days of the final review — scores, gaps, and prioritised recommendations, while the conversations are still fresh.
What we believe
Three things we've learned about AI readiness.
TRUTH I
An honest baseline beats a flattering one.
AI doesn't stall because the tech is bad. It stalls on the unglamorous stuff: leadership alignment, data readiness, teams that work across silos. Knowing exactly where you stand on each is what turns ambition into a plan.
TRUTH II
"We're planning to" doesn't count.
We only score what you've actually done: real decisions, real metrics, real artefacts. If you can't measure it, you can't claim it's working. That rule keeps the score honest — and makes it worth having.
TRUTH III
Your gaps are your roadmap.
Every organisation has them — knowing yours is the advantage. Each one shows you exactly where the next dollar should go. The organisations that own their gaps and act on them outperform the ones that ignore them.
The seven pillars
Each pillar receives a score ranging from Absent to Leading.
Each pillar gets a maturity status grounded in interview evidence — so you can see at a glance which foundations are solid and which need work.
1
Strategy
A clear AI vision, ambition tied to measurable value, and leaders who are actually aligned on it.
25% WEIGHT
2
Value Creation
Proven business results, scaled beyond the demo, with ROI discipline to match.
20% WEIGHT
3
Operating Model
Aligned incentives, one cross-functional team, and playbooks that get things shipped.
20% WEIGHT
4
Governance
Responsible-AI guardrails, risk controls, lifecycle gates, and monitoring that doesn't sleep.
15% WEIGHT
5
Technology
AI infrastructure with automation, integration, and observability built in — not bolted on.
20% WEIGHT
6
Data
High-quality, governed, reusable, and there when your teams need it.
25% WEIGHT
7
Talent & Culture
AI fluency, real enablement, and incentives that make adoption stick.
25% WEIGHT
THE FRAMEWORK
Three enablers. Seven pillars. One honest answer.
"Are we ready to scale AI?" is too big to answer in one go. So we break it into seven pillars, grouped under three transformation enablers — each one owned by someone on your leadership team. And the framework isn't our alone: it's built on insights from the world's leading AI maturity frameworks, from McKinsey and BCG to Deloitte and IBM.
Unified Vision
Direction
A clear, enterprise-wide AI ambition, aligned with leadership priorities, guiding where investment goes.
Pillars: Strategy
Change & Value Engine
Impact and Risk
The engine that prioritises high-value opportunities, drives adoption, and turns AI into business results.
Pillars: Value Creation, Operating Model, Governance
Scaling & Foundational Capabilities
Growth levers
The technical, data, and human foundations that make enterprise-wide AI safe, reliable, and scalable.
Pillars: Technology, Data, Talent & Culture
How it works
Light lift now. Compounding returns later.
Week one
Get your baseline
It all starts with a kickoff call, followed by focused 60–90 minute conversations with pillar leaders. These discussions clarify performance, highlight key gaps, and identify obstacles to progress. Within days of the final conversation, you get a report with pillar statuses, key risks, and a prioritised action plan.
Each quarter
A check-in, not a do-over
Same pillars. New evidence. As your organisation evolves, your scores update with it—showing progress, remaining gaps, and emerging risks. This allows you to identify issues early, stay aligned on priorities, and make the right moves.
Each board meeting
Bring proof, not anecdotes
Show AI investment value. Utilising familiar pillar statuses to track changes over time allows leaders to identify progress, setbacks, and areas needing focus. Instead of isolated metrics, it offers a coherent narrative linking AI strategy to tangible results, enabling more confident decisions.
Our Method
We don't score promises. We core proof.
What doesn't count
future plans, what if scenarios, plain "yes" or "no" answers
"Yes, we have a data governance framework."
"We prioritise high-value use cases."
"Our leadership is aligned."
→
What does
results, decisions, displayed commitment and alignment
"We rolled out data governance in Q2 2024. Quality scores went from 67% to 89%, and data access dropped from 6 weeks to 3 days. Here's the dashboard."
"Our scoring rubric helped us kill 3 low-value projects and reallocate $2M to a churn model that cut churn by 12%"
"Our CEO represents AI strategy at quarterly board meetings. 78% of managers can articulate our AI priorities, up from 34% a year ago."
What to expect
Evidence in. Readiness out.
From honest conversations to a roadmap you can act on
1InterviewWe speak with those managing your AI — leadership, data, technology, and talent. Each talk covers one or two of the seven pillars, with 4-5 questions, in 60-90 minutes, focusing on real examples rather than future plans.
2ScoreEach answer is evaluated against seven pillars, each on a maturity spectrum, receiving statuses from early-stage to scale-ready, which combine into an overall organisation maturity.
3Report & RoadmapYou get a pillar-by-pillar report with your status, key strengths, gaps, and recommendations for improvement. It concludes with prioritised next steps in a readout session, and the assessment can be re-run as you progress.
Why this matters now
Scaling on assumptions is expensive. Scaling on evidence isn't.
Without a baseline
You're guessing
isolated evaluations, stalled pilots
Money flows to the wrong projects, and pilots quietly die
Risks hide in data, governance, and compliance until they surface at the worst moment
Every leader has a different story about where you stand
Nobody can say whether the investment is working
→
With the assessment
You know
a fast, low-fit, repeatable diagnostic
Evidence points the spend where it counts
Risks are identified early and mapped against existing AI governance, cybersecurity, and privacy regulations
One shared picture the whole leadership team agrees on
Progress you can actually see, check-in after check-in
The series
An ongoing programme of research, frameworks, and field findings.
