AI strategy that ends in a build plan, not a slide deck.

What is AI strategy consulting?

AI strategy consulting from Eazyware is a two-to-four-week engagement that audits your data, stack and team, scores every candidate AI use-case by ROI and effort, selects models and providers with a total-cost model, and delivers a build-ready roadmap. The output is a fixed-price proposal, not a slide deck.

What problem does it solve?

Every team has ten AI ideas and no way to rank them. Vendors pitch what they sell. Internal champions pitch what excites them. Meanwhile the data is messier than anyone admits and the budget is burning.

How do we approach it?

We start by refusing to rank ideas in a workshop on day one. The first week is spent looking at what actually exists: which systems hold the data, how clean it is, who owns it, what the team can operate, and what the budget can support in monthly running cost, not just build cost. Only then do we run the use-case workshops, and by then we can score each idea on evidence rather than enthusiasm. Every candidate gets the same treatment: a one-page brief with the business outcome, the data it depends on, the model class it needs, a rough build estimate and a rough monthly cost, and the risks that would stop it. The scoring is done in the open with your stakeholders so nobody is surprised by the ranking. The roadmap that comes out is sequenced so the first item builds the foundation the second needs, and so a decision to stop after any phase still leaves something useful behind.

What do clients use it for?

• 01Board-level AI roadmap with costed use-cases
• 02Choosing between building, buying or integrating AI
• 03Model and cloud vendor selection with TCO
• 04AI readiness before a funding round or RFP

Is it the right fit?

What do we build?

• AI readiness audit across data, infra, security and team skills
• Use-case discovery workshops with business and tech owners
• ROI and effort scoring for every candidate
• Architecture options with cost, latency and accuracy trade-offs
• Model and provider selection with TCO modelling
• 6–12 month roadmap sequenced by value and risk

What you get

• Readiness scorecard
• Prioritised use-case backlog, top 3 fully specified
• Reference architecture and stack recommendation
• Cost model: build plus monthly run cost per use-case
• Roadmap and build proposal

How does the engagement work?

What does good look like?

A good outcome is a document your board can read in twenty minutes and your engineers can build from in week one. It names three use-cases that are worth doing now and says why the other seven are not, with the numbers. It has an architecture that does not lock you into a single model vendor, a cost model with the assumptions written down, and a readiness list of the two or three things that must change before the first build starts. Most of all it has a fixed-price proposal attached, so strategy turns into delivery without a second procurement cycle.

How does it compare?

Which pitfalls do we design around?

Strategy engagements fail in predictable ways. They rank ideas by excitement instead of by data readiness, so the first build stalls on a data problem nobody scoped. They quote build cost and ignore running cost, and the finance team finds out at month three. They pick a vendor before picking a use-case. They produce a roadmap with no owner, no sequence and no stop points. And they treat governance as a slide rather than as a set of controls that will need to exist before a regulated use-case can go live. We design against each of these explicitly, and we will say plainly when the honest answer is to buy rather than build, or to wait.

What do we measure?

Every engagement is instrumented. These are the numbers you see in the dashboard and the monthly report, not claims on a website.

• Use-cases scored on ROI and effort
• Build and monthly run cost per use-case
• Readiness gaps closed before build

Which technologies do we use?

• Vendor-neutral benchmarks
• TCO modelling
• Architecture review

Who does the work?

A principal engineer who has shipped AI to production leads the engagement, paired with an architect and a delivery lead. Where the domain needs it, we bring in a specialist in retrieval, voice or ML for the relevant sessions.

What do you need to bring?

Access to the people who own the data and the budget, a list of the ideas already on the table, and a frank view of what the organisation can operate. Two to three hours a week from a sponsor for four weeks. Anonymised samples of the data behind the top candidates help us score readiness rather than guess it.

Frequently asked questions