What Is an AI Strategy Roadmap?

Name: DAG Tech
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January 21, 2025
DAG
DAG Tech Blog

What Is an AI Strategy Roadmap?

An AI strategy roadmap decides what your organization will build with AI, in what order, and why — converting a list of possibilities into a scored, sequenced, costed plan that leadership can fund.

At DAG Tech, the AI strategy roadmap is the second deliverable in our Artificial Intelligence SME program. It follows the AI Readiness Assessment, which establishes what you can support, and precedes the AI Implementation Plan, which specifies how each item gets built.

Why AI Portfolios Drift Without One

Organizations rarely lack AI ideas. What they lack is a defensible basis for choosing between them. Without a roadmap, the pattern is consistent:

The loudest use case wins. Selection follows whoever advocates hardest rather than where value is largest.
Pilots multiply and nothing reaches production. Six proofs of concept run in parallel, none with an owner or a path to scale.
Dependencies get discovered late. Two initiatives both need a data platform nobody scheduled.
Spend has no envelope. Individual projects are approved without anyone seeing the aggregate commitment.
Success is undefined. There is no agreed measure, so the programme can neither be declared successful nor stopped.

An AI strategy roadmap makes the selection explicit and challengeable, which is the point of building one at all.

How DAG Tech Builds an AI Strategy Roadmap

1. Business Objectives and Success Criteria

Objective: Anchor the roadmap to outcomes, not technology.

Work with leadership to state the outcomes AI is being asked to produce.
Define measurable success criteria per objective, with baselines where they exist.
Capture constraints: budget envelope, regulatory boundaries, timing, systems off limits.
Establish risk appetite — how much autonomy the organization is willing to grant.

2. Current-State Summary

Objective: Carry readiness findings into the planning work.

An AI strategy roadmap built without this grounding tends to select use cases the environment cannot support.

Data, infrastructure, process maturity, skills and governance findings are summarized with their remediation items, so every use case can be tested against what is actually supportable. Where no readiness assessment exists, this stage includes a condensed version of it.

3. Use Case Identification

Objective: Build the candidate list from the work, not from a vendor catalogue.

Run structured sessions with each business function to surface where time and money actually go.
Look for the repetitive, judgment-light, high-volume work that AI handles well.
Include candidates already circulating internally so they get scored on the same basis as everything else.
Describe each candidate in business terms: what it does, who it affects, what it changes.

4. Scoring and Prioritization

Objective: Rank candidates on evidence rather than advocacy.

Scoring is the mechanism that makes an AI strategy roadmap defensible in a budget meeting.

Each use case is scored on business impact, technical feasibility given current state, implementation and run cost, risk, and dependency on other initiatives. The weights are published in the roadmap, so a leadership team can disagree with a weighting and see the ranking change rather than argue with a conclusion.

5. Sequencing and Dependencies

Objective: Turn a ranking into an order that can actually be executed.

Map hard dependencies — remediation, connector work and platform decisions that must land first.
Front-load reusable groundwork: an integration layer or governance structure that three later use cases rely on.
Balance early phases toward provable wins that build organizational confidence.
Respect delivery capacity, including how much change the business can absorb at once.

6. Investment Envelope and Commercial Model

Objective: Show the aggregate commitment, not just per-project costs.

Leadership approves an AI strategy roadmap at the envelope level, so the envelope has to be visible.

One-time and recurring cost per phase, and for the roadmap as a whole.
Total cost of ownership across a multi-year horizon.
Expected benefit per phase, with the measurement method named.
Where value lands: cost reduction, capacity released, cycle time, risk reduction or revenue.

7. Governance and Operating Model

Objective: Define how the programme is run and who decides.

Decision rights, approval gates between phases, acceptable use policy, review cadence and the KPIs reported to leadership — structured against recognized guidance such as the NIST AI Risk Management Framework. A roadmap without a governance model becomes a wish list within two quarters.

What the Deliverable Actually Looks Like

The roadmap is a document leadership funds from, so its structure matters as much as its analysis. Ours runs in eight sections.

Sections 1–4: Purpose, approach, objectives and current state

What the roadmap commits to and what stays flexible, DAG Tech’s method, your business objectives and success criteria in your own terms, and the current-state summary carried forward from readiness.

Section 5: The use case catalogue

The core of the document. Every candidate use case is written up with identical fields so they can be compared on the same terms:

Number and name — so it survives into budget conversations.
Business function and current cost — what it addresses, with a baseline where one exists.
What it would do — described in business terms, not model terms.
Systems and data required — and whether those are available today.
Dependencies — remediation items and other use cases that must land first.
Indicative one-time and ongoing cost — ranged at this stage, firmed up in the implementation plan.
Benefit type and estimated size — productivity, resource reduction, cycle time, lower total cost of ownership or risk reduction.
Impact, feasibility, cost, risk and dependency scores — with the resulting rank.

Sections 6–7: Prioritization and the phased roadmap

The scoring model with its weights exposed, the ranked list, and then the sequence: phases with milestones, owners, entry and exit criteria, and the KPIs each phase is judged on. Dependencies are drawn rather than described, so it is obvious why item four cannot start before item two.

Section 8: Investment envelope, risks and the decision page

Aggregate cost by phase and across the roadmap, expected benefit, the assumptions the estimates rest on, risks with mitigations, and a decision page listing every numbered use case so leadership can select what to fund, defer or decline.

That page is the purpose of the document. The roadmap is not a plan to accept or reject as a block — it is a scored, costed menu, and you choose.

Where the Strategy Roadmap Fits

The AI strategy roadmap is the second of five deliverables in DAG Tech’s CxO™ Artificial Intelligence SME program, between the AI Readiness Assessment and the AI Implementation Plan, and ahead of the AI ROI Assessment and AI Adoption Guidance. Each item the roadmap selects becomes an implementation plan of its own.

Organizations with a single obvious use case often skip straight to implementation. Organizations with a dozen competing ideas and a finite budget need the roadmap first, because the expensive mistake is not picking the wrong use case — it is funding six of them simultaneously.

An AI strategy roadmap is worth having only if it can be argued with. Publish the scoring, show the dependencies, and the conversation becomes about priorities rather than opinions.

Contact DAG Tech today to scope an AI strategy roadmap for your organization.

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