AI Readiness Sprint
Don’t let dirty data kill your AI pilots
The demos looked great. The licenses are paid for. But the AI still isn’t running in production — and the board is starting to ask why.
The AI Readiness Sprint scores whether your operational layer can support AI in production, and names the specific blockers in the way. AI amplifies what already exists — we make sure what exists is worth amplifying.
Where teams get stuck
- An AI agent run on today’s CRM data would be confidently wrong.
- Nobody actually owns the documentation for your core workflows.
- Half the AI tools you pay for never made it into daily work.
- Every pilot looks great in the demo and stalls on the way to production.
The real break
AI doesn’t create order. It amplifies what’s there
AI needs clean data, documented workflows, clear ownership, and consistent CRM hygiene. When those are missing, AI just makes mistakes faster and at scale. The blocker isn’t the model — it’s the operational layer underneath it.
Fixed scope · 2 weeks
What the sprint includes
- Assess crm data around your priority use cases.
- Assess handoffs between tools around your priority use cases.
- Assess reporting logic around your priority use cases.
- Assess ownership around your priority use cases.
What you walk away with
A readiness scorecard showing where you stand in each of the four areas
A list of the blockers affecting the AI work you want to do
A 90-day plan with an owner for each task
The sprint ends with a plan, not an implementation. If you want Darwin to do the implementation, we scope that work separately.
Where it leads
Making the stack AI-ready means fixing data hygiene, workflow ownership, and integrations — the same dependencies behind every other GTM problem. Clear one and the next appears. Darwin Flux is the operating model that keeps the layer AI depends on reliable as your tools and team keep changing.
How Darwin Flux works → Explore the full AI readiness service →Questions you’re probably asking
Does the sprint include implementation?
No. The sprint diagnoses and sequences the work. Implementation is scoped separately, and Darwin can stay to build the data, integration, workflow, and governance layer if you need a delivery partner.
Our data is a mess. Is it too early for this?
It’s exactly the right time. The scorecard is built to start from a messy stack — that’s the point. You don’t need to clean anything up before talking to us.
What do we walk away with?
A readiness scorecard, a prioritized list of the specific blockers, and a 90-day roadmap — usable whether or not we do the remediation.