AI footprint & risk assessment
What your organisation is actually using, where the data flows, what your auditors should know — and the policy framework to bring it inside the line.
Enterprise AI Transformation
Ship with AI. Not around it.
Change management, upskilling, and AI capability embedding for organizations beyond the experimentation phase. We help your teams own AI — instead of letting AI tools own them.
- 01 Shadow AI
- 02 Tool sprawl
- 03 Capability gap
Past pilots. Pre-platform.
Most enterprises now sit in an awkward middle: AI proofs-of-concept have been done, executive sponsorship exists, but production-grade impact hasn't followed. The gap isn't the technology. It's the operating model around it.
"Every team is using ChatGPT — and we don't know what data is going where."
You need a sanctioned AI footprint and a governance posture your auditors will accept.
"We've spent 18 months on AI pilots. Nothing's in production."
You need a productionisation playbook — and a way to kill pilots that aren't going to make it.
"Our engineers are using AI tools, but our PMs and analysts aren't."
You need role-specific upskilling, not generic AI training.
"How do we know if AI is actually making us faster?"
You need outcome metrics tied to business KPIs, not adoption metrics dressed up as impact.
Our approach
A staged engagement: assess, embed, scale. We do the work alongside your teams — not in a parallel "transformation programme" they eventually get handed.
Phase 1 · Weeks 1–4
Assess
- AI footprint audit — sanctioned and shadow.
- Capability gap analysis by role.
- Governance & compliance posture review.
- Opportunity prioritisation — where AI changes the cost curve.
Output: a one-page transformation brief leadership can fund.
Phase 2 · Weeks 4–16
Embed
- Role-based AI upskilling: engineering, product, analyst, ops.
- AI SDLC adoption in one or two flagship teams.
- Governance, evaluation, and observability instrumented.
- Sanctioned AI platform stood up (often Cerebro).
Output: two teams operating AI-native with measurable velocity gains.
Phase 3 · Months 4–9
Scale
- Playbook codification — capturing what worked, what didn't.
- Internal capability transfer — your people leading, ours advising.
- Centre of excellence pattern, lightweight not bureaucratic.
- Outcomes review against the Phase 1 KPIs.
Output: AI is in your operating model, not in our retainer.
What we deliver
Role-based upskilling
Engineering tracks (AI SDLC, evals, agent design), product tracks (AI-augmented discovery, eval-driven roadmaps), analyst tracks (data-with-AI patterns). No generic "Intro to ChatGPT" sessions.
Governance & compliance scaffolding
Policies, approval workflows, audit posture, DSAR readiness. Aligned to PDPA / MAS TRM / GDPR / HIPAA / SOC 2 as your regulatory profile demands.
Sanctioned AI platform
A single trusted surface for your teams' AI work — multi-agent deliberation, governed memory, audit trails. Often delivered on Cerebro, sometimes purpose-built.
Embedded engineering
Our engineers pair with yours through the first AI-native delivery cycles. Knowledge transfer happens in the work, not in a binder.
Outcome measurement
KPI instrumentation tied to your business — cycle time, defect rates, customer outcomes — not adoption-rate vanity metrics.
What success looks like at month 9
- Shadow AI has a sanctioned alternative — and your security team knows what's running.
- Two flagship teams are AI-native end-to-end — with documented velocity gains tied to business outcomes.
- A governance framework — your audit, legal, and risk functions can defend.
- Internal capability owns the next wave — you can extend without us in the room.
- An honest retrospective — including which AI bets paid off and which ones didn't.