AI strategy for companies: how to build the roadmap

Three artificial intelligence proposals reach the committee: a customer service assistant, a demand forecasting model and a tool that 'automates everything' in the back office. Each comes with a flawless demo. What doesn't come is the criterion to decide which is worth it, in what order and what it takes to make it work with the data and systems the company actually has.

This guide explains what an AI strategy is, how it differs from buying tools, how use cases are prioritized, what NIST's public framework says about managing risk from design, how the roadmap is built and which decisions belong to leadership.

What is an AI strategy?

An AI strategy answers three questions: in which business processes artificial intelligence changes a measurable outcome, in what order it's worth doing, and what it takes — data, systems, governance, people — for it to work in production and not only in a demonstration. Its output is a roadmap: a short list of prioritized use cases with sequence, owners, dependencies and how each will be measured.

It is not a list of tools or a statement of intent. It is an investment decision, with the same rigor as any other initiative in the company's technology portfolio.

What is the difference between an AI strategy and buying AI tools?

The order. Buying tools starts from what a vendor offers and looks afterwards for where to apply it; a strategy starts from the processes and data that already exist and decides afterwards which technology, if any, improves them. The first route produces what we see in our projects: five tools bought by five departments that don't talk to each other, and pilots that never reach production because the data they needed never existed.

The question isn't what AI can do. It's which process in your company changes an outcome if done differently, and what it takes to do it differently.

Does your committee decide on AI by instinct?

We inventory the AI already in use, identify and prioritize use cases with a single criterion and build the roadmap with its governance, architecture and measurement.

• Short list of prioritized use cases
• How each will be measured against the real process

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How do you prioritize AI use cases?

With a single criterion applied to every candidate, so the committee compares like with like. In our view, four dimensions are enough:

Impact: which business outcome changes — cycle time, errors, cost, revenue — and how much that change is worth.

Feasibility: whether the necessary technology exists and is proven, and whether the team can operate it.

Data: whether the data the case needs exists, with what quality, who owns it and whether it may be used for that purpose.

Risk: what happens if the model is wrong, who is affected and which controls are needed.

The result is a short, ordered list with what each case needs before it starts. What has no data or owner isn't discarded: it is postponed until data governance resolves it.

What does the NIST AI RMF say about managing AI risk?

The AI Risk Management Framework from the U.S. National Institute of Standards and Technology is a framework intended for voluntary use to incorporate trustworthiness into the design, development, use and evaluation of AI systems. Version 1.0 was released on January 26, 2023, and NIST notes it is being revised. Its core organizes risk management into four functions: Govern, Map, Measure and Manage.

In July 2024 NIST published the Generative AI Profile (NIST-AI-600-1), which helps identify the risks specific to that technology. For an AI strategy, the framework's practical value is that it forces you to define, case by case and before building, which risk is accepted, who answers and how it will be measured; which framework the company adopts to govern AI is a leadership decision we cover in the AI governance guide.

From the roadmap to the real process

The first cases are executed with our automation and AI agents team, under the same governance framework and with the same yardstick.

• Agents measured against the process they replace
• Auditable AI governance from the first case

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How do you build the AI roadmap, step by step?

1. Inventory the AI already in use: which tools exist, who bought them, which data they work with and what result they give. In many companies the strategy starts by putting in order what already exists.

2. Discover use cases with the departments, not with the vendor: processes with volume, repetitiveness, available data and a known current cost.

3. Prioritize with the single criterion (impact, feasibility, data, risk) and produce a short list.

4. Define for each case how it will be measured: the real process before and after — time, errors, cost, revenue — never the demo.

5. Resolve the dependencies: data (governance and quality), integrations (architecture) and controls (AI governance). A case with unresolved dependencies doesn't enter the roadmap.

6. Sequence and assign owners: what comes first, what depends on what, who answers for each case in the business, not in IT.

7. Execute the first cases as measured pilots, and review the roadmap with the real results.

What must leadership decide?

Approve the prioritized list with the same criterion for every case. Decide which data no model may touch and who answers for each use case. Choose the AI governance framework. And review the measurement: if a case didn't change the real process, it stops, even if the demo is still flawless.

How we can help

At Cyrrus we provide AI strategy consulting: inventory of existing AI, discovery and prioritization of use cases with a single criterion, a roadmap with sequence and owners, data and architecture requirements, the applicable governance framework and the measurement plan for each case. The first cases are executed with our automation and AI agents team, measured against the real process.

To prepare the committee, Leadership Academy offers the AI for executives workshop. The AI strategy 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.

To go deeper into related topics: what data governance is and how to start, and what RPA is and when it makes sense versus AI.