Build a Practical AI Roadmap for Your Business
An AI roadmap matched to your maturity, your budget and your pace — not someone else's reference architecture.
Most AI roadmaps fail in one of two ways: they're too ambitious for the data foundations in place, or they're a scattergun of pilots with no through-line. A useful roadmap does the opposite — it sequences a small number of bets the business can actually finish.
This guide explains what a practical AI roadmap looks like for a growing business, why most roadmaps stall, how to size and sequence the work, and the concrete steps to put a 30/60/90-day plan in place that the exec team will trust.
What is an AI roadmap (and what it isn't)
An AI roadmap is the sequenced set of AI use cases your business intends to ship, together with the data foundations that have to be in place to support them. It is not a list of tools, a vendor RFP, or a model architecture diagram — those are downstream artefacts.
A good roadmap can be read by a non-technical board member and a senior engineer and have both of them agree on what the next quarter looks like. If only one of those audiences understands it, it's the wrong document.
Match the roadmap to maturity, budget and pace
A pre-Series A team and a 500-person scale-up need very different plans. The right roadmap respects how much change the business can absorb each quarter, and what the data foundations can support today.
A practical sizing test: if the roadmap requires more than two new full-time hires or one new platform in the next 90 days, it's almost certainly too ambitious for the business it's been written for. Smaller, sequenced moves beat heroic ones.
Avoid random pilots
Disconnected pilots create motion without progress. Each one ends with a slide deck and no production system. A roadmap fixes this by tying every initiative back to a specific business outcome and the data work needed to support it.
A useful rule of thumb: don't approve a second pilot until the first has either shipped to production or been deliberately killed. Two pilots are manageable; five concurrent pilots is how AI loses board sponsorship.
Start with business use cases and data foundations
Pick two or three use cases with clear value. Identify the data they depend on. Fix the foundations for those first. Everything else gets easier once a small, real example is working end-to-end.
Strong candidates tend to share three traits: the value is measurable in weeks, not quarters; the data already exists somewhere in the business; and someone in the exec team genuinely wants the outcome. If any of those is missing, you're choosing the wrong first use case.
Practical steps to build the roadmap
Start with the free AI readiness check to set an honest baseline. Run a one-hour workshop with the exec team to surface candidate use cases. Score each candidate on business value, data readiness and risk. Pick two — one quick win, one strategic — and write the data foundation work each depends on into the plan.
Reserve days 1–30 for foundations, 31–60 for governance and a working prototype, and 61–90 for shipping one production AI use case and lining up the next two. Treat the plan as a living document — re-baseline it every fortnight and re-publish.
30 / 60 / 90 day structure
- Days 1–30: Establish ownership, agree two or three priority use cases, baseline data quality
- Days 31–60: Fix the highest-impact data foundations and governance gaps
- Days 61–90: Ship one production AI use case and prepare the next two
- Throughout: Re-baseline the plan every fortnight against business changes
Start the free AI readiness check.
30 seconds. See your score, your biggest blocker and your recommended next step. No credit card.