- A realistic AI roadmap fits on one to two pages and is judged by its first executed decision, not by its volume.
- Every idea runs through the same four criteria filter: problem, value, data, effort.
- You verify the data of the targeted use case, not the whole company: an idea whose data is not ready gets a "prepare" decision.
- Every initiative comes out with an explicit decision: launch, prepare, reframe or defer.
- PlanAxion's AI workshop produces this roadmap in four weeks, with a decision ready file for leadership.
A realistic AI roadmap is built in four weeks, starting from the operational frictions your teams already live with, not from a technology overhaul. It fits on one to two pages: use cases ranked against four criteria (problem, value, data, effort), one measurable first pilot, and a 90 day decision sequence. A heavy transformation is not a prerequisite: it is a risk to avoid.
A realistic AI roadmap is a short document that ranks an organization's AI use cases against four criteria (problem, value, data, effort), designates one measurable first pilot anchored in existing systems such as the ERP, and sets a 90 day decision sequence, without requiring a technology overhaul first.
Why do most AI roadmaps end up in a drawer?
Because they are judged on ambition instead of on their first executed decision. A 40 page plan promising a three year transformation does not survive its first executive meeting: nobody can say what to decide on Monday morning. Our position: a roadmap is judged by its first executed and measured decision, not by its volume.
What does a realistic AI roadmap contain?
Six elements, nothing more.
- Prioritized use cases, ranked with the same criteria.
- Documented assumptions: expected gains and success conditions.
- Identified dependencies: data, systems, constraints to resolve.
- An explicit decision per idea: launch, prepare, reframe or defer.
- One first pilot with its success metric defined before launch.
- An owner and an assignable next step for each initiative.
How do you prioritize without transforming everything?
Run every idea through the same four question filter. Problem: is it real, specific, and lived by an identifiable team? Value: can the gain be measured in time, quality, cost, revenue or risk? Data: is it accessible and reliable enough to test without a preliminary cleanup project? Effort: is the first test proportionate to the expected gain?
A weakness does not disappear because the idea is attractive. The filter is not there to make every idea win: it is there to choose which ones to launch, prepare or defer. It is the same filter we apply in the AI workshop.
Do you need to fix all your data first?
No. You verify the data required by the targeted use case, not the whole company. An idea whose data is not ready gets a "prepare" decision, with the precise dependency to resolve, instead of triggering a six month project that blocks the entire portfolio. That is the difference between a roadmap and a transformation plan. In most SMBs, the cleanest data already lives in the ERP: orders, invoices, payments, inventory movements. That is where first pilots find their material.
What does the result look like? An example.
A fictional scenario: three ideas, three different decisions. Automatically classifying customer service emails: specific problem, accessible data, measurable result. Decision: launch a limited pilot. Forecasting demand by product: high value but incomplete history. Decision: make the data reliable first. Deploying an AI assistant everywhere: problem too vague, success impossible to measure. Decision: defer and define a precise use.
Three lines, three defensible decisions: that is a realistic roadmap.
Frequently asked questions
How long does it take to build an AI roadmap?
Four weeks is enough when the exercise is scoped to one business unit, with about five participants and a shared prioritization filter.
Does an SMB need to hire a data team before starting?
No. The first pilot runs on data already available in existing systems, such as the ERP. Skill needs become clear after the first measured result.
What is the difference between an AI strategy and an AI roadmap?
The strategy sets the direction and ambitions. The roadmap lists the concrete decisions: which use cases, in what order, with what metrics and which owners.
Next step: PlanAxion's AI workshop produces this roadmap in four weeks, with a decision ready file for leadership.
