Templates

AI implementation roadmap template (first 90 days to 12 months)

Turn an AI strategy into dated work: a 90-day plan with gates, a quarterly view for the year, and the owners, metrics, team and budget lines leadership will ask for.

By the Ryz Labs team · Updated October 2026

An AI implementation roadmap turns a list of AI ideas into dated work with owners. The first 90 days usually move through three gates: choose and scope one or two use cases with data access approved, prove them on real data against agreed success criteria, and put the first one in front of real users with measured results. The 12-month view then adds use cases in waves, builds shared foundations (evaluation, tracing, model access, policy) and scales the team. The template below gives you both views plus the owners, metrics and budget lines.

AI implementation roadmap template

1. First 90 days

Weeks	Focus	Key work	Gate to pass	Owner
1-2	Align	Agree goals with leadership; collect use cases with owners and metrics; list data sources and constraints	Leadership agrees on goals and decision makers	[Executive sponsor]
3-4	Choose	Score use cases; pick one or two; write POC success criteria; request data access; staff the team	Use cases chosen; criteria signed; data access requested	[Head of AI or CTO]
5-8	Prove	Build on real data; build the eval set; run evals weekly; track cost per task	POC criteria met, or a clear stop or iterate decision	[AI tech lead]
9-12	Ship	Pass production gates; staged rollout to a pilot group; measure against baseline	First users relying on it, with measured results	[Business owner + engineering owner]

2. Twelve-month view

Quarter	Use cases	Foundations	Team	Results to report
Q1	[First one or two use cases to pilot]	[Eval approach, tracing, approved providers, AI usage policy]	[First pod or core AI engineers]	[Pilot results against baseline]
Q2	[First use case to full rollout; next two use cases into POC]	[Shared retrieval and data access patterns; cost dashboards]	[Add data engineering]	[Adoption, impact, cost per task]
Q3	[Wave 2 use cases to production]	[Central platform for evals, guardrails, model routing]	[Second pod or platform engineers]	[Portfolio impact; decisions to scale or stop]
Q4	[Wave 3; retire low-value use cases]	[Review policy; provider review]	[Plan next year's team]	[Annual results and next-year roadmap]

3. Budget lines

Line	How to estimate	Q1	Q2	Q3	Q4
Engineering team	Engineers x months x monthly cost	[ ]	[ ]	[ ]	[ ]
Model and API usage	Cost per task x expected volume	[ ]	[ ]	[ ]	[ ]
Infrastructure	Vector stores, compute, observability	[ ]	[ ]	[ ]	[ ]
Tools and licenses	Eval, tracing and other tooling	[ ]	[ ]	[ ]	[ ]
Training and adoption	Time from business teams, enablement	[ ]	[ ]	[ ]	[ ]

Use this template

How to use this AI roadmap template

Name an executive sponsor and a decision maker for each use case before week one. Roadmaps without owners become wish lists.
Use weeks 1 to 4 to choose: score use cases, pick one or two, and write success criteria before any build starts.
Request data access in week 3. Approvals are the most common reason AI work slips.
Staff the team that will build, with engineers who have shipped AI to production, and put evals and tracing in place in the first sprint.
Hold the gate reviews on schedule. Stopping a use case at a gate is a valid outcome and frees the team for the next one.
After 90 days, fill the 12-month view with what you learned: real cost per task, real time to production and the next wave.
Report results quarterly against the baselines, not just activity.

Example: weeks 5-8 for a document extraction use case

Week	Work	Result
5	Extraction prototype on real inbound documents; eval set built with the operations team	Baseline accuracy measured on the held-out set
6	Added layout-aware parsing and validation rules for key fields	Accuracy improved on the hardest document types
7	Cost and latency measured on a week of real volume	Within budget after batching
8	Gate review against POC criteria	Go: move to production gates with a human review step

Common AI roadmap mistakes

A roadmap of technologies. "Deploy RAG in Q2" is not a goal. Put use cases, owners and metrics on the roadmap.
Foundations first, use cases later. Platforms built before any use case ships are built for imagined needs. Build foundations alongside the first use cases.
No gates. Without go or stop decisions, every pilot continues and none finishes.
Budget only for engineering. Business teams' time for labeling, review and adoption is part of the cost.
Planning the year in detail on day one. Plan 90 days in detail and the rest by quarter, then update it with real results.

Need a team to execute the roadmap?

Ryz AI pod teams are dedicated pods of senior engineers, typically a tech lead with AI, ML and backend engineers, that build the use cases on your roadmap in your cloud and repos and ship them to production alongside your team. Pods can grow as the roadmap adds waves, or hand systems over to your engineers. Only the top 1% of the tens of thousands of engineers we interview make it, on US business hours including New York hours, and typical cost is $7,000 to $15,000 per engineer per month for the team line in your budget. Talk to us to scope your pod, or estimate with the AI development cost calculator.

FAQ

What should an AI implementation roadmap include?

A detailed first 90 days with gates (choose, prove, ship), a quarterly view for the year with use cases, foundations, team and results, named owners and decision makers, success metrics with baselines, and budget lines for engineering, model usage, infrastructure, tools and adoption.

How long does it take to implement AI in a company?

A first well-scoped use case on model APIs with accessible data can reach real users within a quarter when it is staffed by engineers who have done it before. Broader adoption across the company is a multi-quarter program built in waves.

What comes first: AI strategy or AI use cases?

Do them together. A short strategy sets goals, constraints and decision makers; use cases make it concrete. Shipping the first use case teaches more about data, cost and adoption than a long strategy phase.

How do you measure an AI roadmap's success?

By results against baselines for each use case: time saved, error rates, revenue or cost impact, adoption by target users and cost per task. Report them quarterly alongside what was stopped and why.

Who should own the AI roadmap?

An executive sponsor owns the goals and budget; a Head of AI or CTO owns the roadmap and gates; each use case has a business owner and an engineering owner. Review it quarterly with all of them.

Questions we didn't answer? Email info@ryzlabs.com.