ADOPTION / DATA / GOVERNANCE
Adoption that sticks. AI that survives an audit.
Most AI value is lost after launch — to adoption gaps and governance debt, not model quality. We build the foundations and the habits that keep the gains, and clear the compliance bar that blocks scale.
The problem
The model works. The organisation doesn't adopt it.
Capability rarely fails on the technology. It fails because the data underneath isn't AI-ready, because people aren't trained or trusted to use the tools, and because security and compliance weren't designed in. Enterprise buyers now rank governance, auditability and security as top requirements — so the work that makes AI durable is the work that lets it scale at all.
What's included
- 01
Data readiness & AI-ready architecture
Quality, access and security audits, then the architecture and governance controls AI depends on.
- 02
AI literacy & role-specific training
Hands-on upskilling, playbooks and prompt libraries that turn tools into everyday practice.
- 03
Change management
Stakeholder engagement, champions and reinforcement loops so adoption survives past the launch buzz.
- 04
Evaluation & observability
Test suites for accuracy, safety and regression, plus drift detection and cost monitoring in production.
- 05
Governance frameworks
Guardrails, access controls and audit logging mapped to standards like the NIST AI RMF and the EU AI Act.
What you get out of it
- Adoption that holds, with real time savings inside the first few months
- Compliant, auditable AI that clears the enterprise security bar
- Internal capability — so you aren't permanently dependent on a consultancy
How we work
The Qanz Loop, applied here.
- 01
Position
Decide what's worth winning
We start from the business problem, not a channel or a model. Positioning, audience, the use cases worth funding, and the data and governance reality behind them — so everything downstream points the same way.
- 02
Build
Ship the system, not a slide deck
Senior people build the working thing: campaigns wired to first-party signals, content and creative systems, or AI features that reach production. Scoped tightly, with human review where it matters.
- 03
Compound
Make the gains stack
We measure what's incremental, fix what isn't, and stay engaged after go-live. Visibility, pipeline and capability are meant to grow without resetting every time a budget pauses.
We build capability, not dependence
The goal is for your team to run the thing without us. Enablement comes with documentation, playbooks and the operating model to sustain it; governance is designed to pass the security and compliance checks that decide whether a pilot ever scales. Success is measured in durable adoption and auditability, not consulting hours.
Questions
Worth asking.
Can governance happen after we've built something?
It can, but it's harder and riskier. Buyers increasingly treat governance as day-one, and retrofitting it is how pilots get blocked from scaling. We prefer to design it in.
What standards do you map to?
Commonly the NIST AI RMF and the EU AI Act, adapted to your sector and risk profile. The point is auditability you can show, not a binder no one reads.
Our team is sceptical about AI. Does training help?
Scepticism is healthy and usually well-founded. Role-specific, hands-on enablement that shows real time saved on real tasks does more for adoption than any mandate.
More services
Often paired with this.
Start the conversation
Let's scope it — honestly.
Tell us the problem you're actually trying to solve. We'll tell you whether it's marketing, AI, or both — and whether we're the right people for it.