AI strategy consulting: from a list of ideas to a roadmap
The committee approves or rejects AI initiatives on instinct, not criteria. We define which use cases have real business impact, in what order they run, what governance and architecture they need, and how each one will be measured.
What is AI strategy consulting?
It is the service that turns interest in artificial intelligence into a roadmap: which business processes can change with AI, how much changing them is worth, which data and systems are needed and what governance makes them auditable. At Cyrrus the AI strategy is built on three pillars we already work on: AI governance, architecture and automation with agents.
Starting point
The company's real processes and the data that already exists, not a vendor's tool catalog.
Deliverables
Use cases prioritized by impact and feasibility, a roadmap with sequence and owners, data and architecture requirements, and the governance framework.
Public reference
The NIST AI RMF serves to manage the risks of each use case from design; which framework to use is leadership's decision (see AI governance).
An AI roadmap the committee can approve with criteria.
01Inventory of existing AI
Which AI tools are already in use, who bought them and which data they work with, before proposing new ones.
02Use case discovery
Sessions with departments to identify candidate processes: volume, repetitiveness, available data and current cost.
03Prioritization
Business impact, technical feasibility, data needed and risk, with a single criterion for every case.
04Roadmap
Sequence, dependencies (data, integrations, governance), owners and how each case will be measured against the process it replaces.
05Governance and architecture
Which policies, roles and controls apply, and on which architecture models, data and systems connect.
06Measured pilots
The first cases are executed with our automation team and measured against the real process, not a demo.
An AI strategy answers three questions: in which processes AI changes a business outcome, in what order it's worth doing, and what it takes — data, systems, governance — for it to work in production and not only in a demonstration.
That is why we don't start with the tool. First we inventory the AI already in use in the organization and the data that exists; then, with the departments, we identify candidate processes and prioritize them with a single criterion: impact, feasibility, data and risk. The result is a short list leadership can approve or reject with the same criteria.
Each use case in the roadmap carries how it will be measured: against the real process it replaces — time, errors, cost — and not against the demo. It is the same criterion we use to execute automation with AI agents, so strategy and execution use the same yardstick.
The AI strategy rests on governance and architecture: which data each model may touch, who approves and audits, and which systems it connects to. Defining it in the strategy prevents, in our view, two failures we see in our projects: pilots that can't move to production for lack of data, and tools bought by five departments that don't talk to each other.
This service is the entry point to Cyrrus Intelligence Lab and connects with the IT strategy of the CIRA method's Construct phase: AI is one more initiative in the technology portfolio, with its business case, not a separate project. To prepare the committee we offer the AI for executives workshop at Leadership Academy.
The strategy runs on the three pillars
Before approving a use case, read this
Frequently asked questions about AI strategy consulting
When the committee receives AI proposals and has no criteria to compare them; when several departments have already bought tools on their own; when a pilot worked in a demo and didn't move to production; or when the board asks for an AI plan with benefits, costs and risks before approving a budget.
The strategy decides which use cases are done, in what order and with what measurement. Governance defines the policies, roles and controls they are executed and audited with. Strategy without governance produces unauditable pilots; governance without strategy produces rules for initiatives no one prioritized. At Cyrrus they are worked on together.
An inventory of existing AI, a list of prioritized use cases with their criteria, a roadmap with sequence and owners, data and architecture requirements, the applicable governance framework and the measurement plan for each case.
Only after defining the use case, the data and the governance. AI and automation platform selection is done with the same vendor-independent criteria we use in every solution selection.
Case by case, against the real process it changes: time, errors, cost or revenue of the process before and after. That criterion is defined in the strategy and applied when the case is executed.
Approving the prioritized list, deciding the governance framework and the data AI may not touch, and reviewing the measurement. The AI for executives workshop at Leadership Academy prepares those decisions.
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