AI roadmap and implementation planning
We turn a set of AI opportunities into a sequenced plan with business cases, architecture choices and the governance to run it responsibly. The roadmap is written to be delivered, and we can start on the first release straight after.
What it is and when it fits.
An AI roadmap sets out which use cases to tackle in which order, what each needs in data, systems and people, and how they share common building blocks such as model access, retrieval, evals and monitoring. For each item we compare buying a product, configuring a platform or building custom, and we set out the governance: ownership, human oversight, risk classification and review cycles.
A roadmap is useful when you have more than one serious AI initiative, a board or budget holder who needs a business case, or several teams at risk of solving the same problem twice. If you have a single use case ready to go, a scoped build plan is enough. The roadmap often follows an AI readiness assessment, although it can also start from your own shortlist.
What we build.
Sequenced delivery plan
Use cases ordered by value and dependency, with the shared foundations each one needs built at the right moment.
Business cases
Expected benefits, running costs, effort and risks per initiative, based on your figures and stated assumptions.
Build vs buy analysis
A comparison of off-the-shelf products, platforms and custom builds for each use case, including lock-in and total cost.
Target architecture
A reference architecture for models, retrieval, integrations, evals and monitoring that fits your cloud and security standards.
Governance framework
Roles, approval processes, AI Act risk classification, acceptable use rules and a review cadence for systems in production.
How it works.
- 01
Align on goals
We agree business objectives, constraints and decision criteria with leadership, so every item on the roadmap ties back to them.
- 02
Shape the initiatives
For each use case we define scope, data needs, architecture options and a build vs buy recommendation.
- 03
Sequence and cost
We order initiatives by value and dependency, estimate effort and running costs, and set out the governance around them.
- 04
Move into delivery
We plan the first release in detail, so the roadmap turns into working software without a long gap in between.
Built with.
All technologiesGuides.
AI readiness checklist: what to answer before your first agent goes live
The questions to answer before your first AI agent handles real work, from process and data to approvals, monitoring and ownership.
10 min read
Build vs buy software: a decision guide for operations and product teams
A practical guide to choosing between off-the-shelf and custom software, with a cost comparison, a decision matrix and a checklist for your team.
10 min read
Further reading.
Build vs buy software: when custom development pays off
A practical way to decide between off-the-shelf software and custom development, covering total cost of ownership, integrations and risk.
5 min read
RAG vs fine-tuning: which does your business actually need?
RAG gives a model your knowledge at answer time; fine-tuning shapes its behaviour. How to choose, when to combine them and what each costs.
4 min read
Common questions.
Usually around a year in detail, with a lighter view beyond that. AI tooling changes quickly, so we plan firm commitments for the near term and review the rest every quarter.
We recommend what fits your requirements and have no reseller agreements, so there is no commission behind a choice. Where buying a product is the better option, the roadmap says so.
Each initiative gets an owner, a risk classification, rules for human oversight and a monitoring plan. We keep governance proportional, so low-risk internal tools do not carry the same process as customer-facing decisions.
Yes. We write it with your team's skills in mind and can mix delivery models: your developers building some items, Vantion building others, or us working alongside your team at first.