AI Readiness & Roadmap
A fixed-scope assessment that maps your data, systems, and workflows — and returns a concrete plan for the first AI system worth building, with the governance to run it safely.
What it is
AI Readiness & Roadmap is the entry point when you know AI should be doing work in your business but not where to start. We inventory your data sources, the systems your teams live in, and the workflows that consume their week. You get back a short list of AI systems worth building, ranked by value and feasibility, with the first one scoped into a build plan.
Why it matters
The most expensive AI mistake is building the wrong first system. A pilot picked for demo appeal stalls the moment it meets real data, real permissions, and real users. Picking a first workflow with reachable data, a measurable outcome, and a motivated owner is most of the difference between a production system and an abandoned experiment.
What you get
A fixed-scope engagement measured in weeks, not quarters. Every artifact is yours whether or not we build the system together.
- Data and systems inventory with owners and access boundaries
- Ranked candidate AI workflows, scored on value and feasibility
- A build plan for the first system: components, integrations, evals
- Governance baseline: what the AI may read, write, and decide
- Cost model: infrastructure, model spend, and the levers on both
What it works with
Feeds directly into Data Foundations when the data needs work first, or straight into Agent Workflows when a workflow is ready to automate. The inventory becomes the seed of your data catalog; the ranked list becomes the roadmap the rest of the stack executes against.
When you need it
Signals: leadership wants an AI strategy and the team has demos but nothing in production; three departments bought three AI subscriptions that don't talk to each other; nobody can say which data an AI system would be allowed to read. If you already know your first workflow and your data is reachable, skip this and start at Agent Workflows.
Related learning
One trusted set of data your dashboards, your AI, and your agents can all read. Clear owners, freshness targets, and access controls from ingest to query.
AI agents that act on business events with the tools, memory, approvals, and traces you can audit. Workflows that ship to production, not just demos.
The policy layer for what an AI system is allowed to read, call, decide, and ship — encoded as configuration the runtime enforces, not as a document on a shared drive.
A navigable map of every system your data lives in — schemas, documents, code, tickets, events, owners, and permissions — so an AI agent can find the right source and respect the right access boundary.