Transformation pods
Build the design: a transformation pod inside the line.
What a pod does
- 01
Stand up the pod.
AI solution specialists with forward-deployed engineers, and an AI strategy and data architect where the foundation needs work, seated with the process owner and the champions.
- 02
Harden the prototype.
Take the champions' prototype or the designed target state to production grade: prompts, skills, agents, guardrails.
- 03
Connect the systems.
In the firm's own tools, whichever model platform it runs; connectors and MCP servers into its platforms, with identity, access and data tagging carried end to end.
- 04
Register and clear.
Every asset registered in the governance platform, tiered by the firm's rules, reviewed in parallel, published to the marketplace.
- 05
Pilot and measure.
A scoped pilot with the owner: cycle time, cost to serve, quality and capacity against the business-case baseline.
- 06
Hand off and sustain.
Monthly mini-deliverables, a handoff at the end, then validation, monitoring and support owned by the transformation office.
The pod moves on at handoff. The method and the registered assets stay.
Where this fits
- Program
- Build
- Install
- Maintain
The three roles
- AI solution specialist.
- A practitioner with credibility in a business function and a product mindset, who maps jobs and workflows with the process owner, agrees the baseline and the value contract, writes the skills, packages assets into the registry, and coaches the champions.
- Forward-deployed engineer.
- Builds connectors, MCP servers, agents and automations from inside the pod; hardens business prototypes rather than starting from scratch.
- AI strategy and data architect.
- Shapes the AI strategy and target-state architecture behind the pod and readies the foundation it builds on: an AI-ready data environment, the knowledge layer, model and runtime environments, identity, access and integration, working with the firm's technology owners and delivery partners.
Scope a pod.
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