A GenAI roadmap makes a sequence of business decisions explicit: which workflows to test, what they depend on and what evidence will justify the next investment. Choosing a tool is only one of those decisions.
A team proposes an AI-assisted monthly report using approved data and an existing manager review. Another proposes customer replies, but the product FAQ is outdated and nobody owns its correction. Both could be useful. Which is ready to test?
The reporting task is readier for a limited trial because its inputs and reviewer are already identified. That establishes feasibility; the team still needs to measure whether the complete reporting process improves.
The customer-reply proposal needs preparation. Assign ownership of the FAQ, agree which answers are authoritative and define when a reply needs review. Record those dependencies in the roadmap before funding a broader trial.
List recurring workflows and identify the result each produces. Compare their business value with the information, system access, authority and review capacity available. Select a small initial trial, record its baseline and decide what result would justify continuing, changing or stopping it.
Map recurring tasks such as preparing decision notes, comparing suppliers, reviewing feedback, drafting policies and summarising customer conversations. Identify which team has the information and authority needed to change each workflow.
Group use cases by business value and implementation requirements. Select a manageable set for an initial trial and record what must be prepared before the remaining ideas can proceed.
A pilot needs a business owner, a defined workflow, a baseline and a user group. Agree what evidence will determine whether the work should continue, change or stop.
Use impact and feasibility to compare priorities. High-value, feasible work can be tested first. Valuable work that depends on missing data or governance needs preparation. Low-value work should receive correspondingly less investment.
Employees discover constraints when they apply AI to their own work. Schedule practice, verification and measurement alongside the implementation steps.
Leadership teams need to review the selected use cases and their responsibilities. Managers need review routines, while employees need examples and practice relevant to their functions.
I begin with a recurring business problem, such as an approval delay, reporting workload, customer response or hiring process. We examine the existing work, including earlier AI attempts, before assessing business value, data readiness, review requirements and authority to change it.
Start by mapping recurring work and decisions before choosing tools. Identify workflows with high time cost, clear business value, manageable risk, and teams ready to experiment. Then design training, governance, and adoption metrics around those first use cases.
A technical roadmap focuses on models, data pipelines, and engineering skills. A business GenAI roadmap focuses on workflows, use cases, adoption risk, training, governance, and business outcomes.
A 90-day roadmap should include a review of earlier attempts, selected workflows, named owners, relevant training, verification and approval requirements, and measures of the complete result. Review quality, capacity, process time and costs, including checking and rework.
