AI readiness assessment,
scored and road-mapped
A 2–4 week audit of your data, systems, and use cases. We score each one by ROI and feasibility, then hand back a written report and a roadmap. No build commitment to find out where AI pays off.
This is the assessment that comes before any AI Development work. If you already know what you want, the build happens through AI Integration Services for existing systems or Generative AI Development for new features. The assessment exists for everyone else — the teams who can feel that AI matters but have no shortlist of where it earns its keep. If you want the bigger picture first, see enterprise AI use cases and adoption.
Advice from
people who ship
A readiness assessment backed by a team that builds, not only advises. We score where AI pays off, then ship the work.
100+
Production software across fintech, health, and manufacturing, the build record behind every recommendation.
2–4wks
From kickoff to a written readout and a scored roadmap.
100%
Senior engineers run the audit; the people who would build it.
30/60/90
A phased plan you can act on the week it lands.
Find where AI pays off before you build
A 2–4 week audit, scored by ROI and feasibility, by senior engineers.
The dimensions of
AI readiness
AI readiness is not one number. We score six dimensions, each for where you stand today — so the report shows not just whether you are ready, but which gap to close first.
Strategy & use case
Is there a use case with real ROI behind it, tied to a goal the business already cares about, or is AI still a solution hunting for its problem?
Data readiness
Whether your data is clean, reachable, and rich enough to feed a model, and how much of the first build is really just getting that data into shape.
Infrastructure & systems
The pipelines, APIs, and compute a model needs to run and to read live state, plus the points where your current stack would need to change.
Talent & ownership
Who builds the thing and who runs it after launch, and whether that ownership already exists in-house or has to be hired or partnered in.
Governance, risk & compliance
Data privacy, regulatory exposure, and model risk — the constraints that decide what you are allowed to build, and where a regulator would draw the line.
Adoption & process
Whether the workflow and the people in it will actually use the feature, because a model nobody adopts returns nothing on the spend.
A written report
you can act on
Six concrete deliverables, all on paper. Nothing here is a verbal recommendation or a workshop you have to remember. Every call is written down with the reasoning behind it.
Readiness report
One written document covering where you stand, what AI can do for you now, and what has to change first. The document you forward to whoever signs off the AI budget.
Use-case scoring
Your candidate use cases ranked on two axes — the ROI if it works and the feasibility given your data. The shortlist, with the math shown.
Data & systems audit
An honest read on whether your data is clean, accessible, and rich enough for the use cases you want, and which systems would need to feed them.
Risk register
The risks named before they bite: data gaps, compliance exposure, model accuracy, vendor lock-in. Each one carries a severity and a way to handle it.
Build-vs-buy calls
For every recommended initiative, a clear verdict on building it versus buying an existing tool, with the cost and control trade-off spelled out.
30/60/90-day roadmap
A phased plan that sequences the first build, the next, and the groundwork, so the report ends in a sequenced plan your team can start on.
Five steps to a
delivery roadmap
A fixed two-to-four-week arc. We agree scope on day one and end with a readout you can take straight to a decision. No open-ended discovery.
Kickoff & scope
We agree the use cases on the table, who we talk to, and which systems we look at. The end date is fixed here, not discovered later.
Data & systems review
We inspect the data and the systems behind each candidate — quality, access, and where the gaps sit. This is what feasibility rests on.
Use-case workshop & scoring
A working session with your team to size the value of each use case, then a score on ROI and feasibility that produces a ranked shortlist.
Risk & build-vs-buy
We log the risks for the shortlisted work and write the build-versus-buy call for each, with the cost and control reasoning attached.
Roadmap readout
A live readout of the report and the 30/60/90 roadmap, handed over as a written document your team and your board can act on.
Where the audit
led to a build
Engagements where scoping the right use case first kept the build aimed at value — across real estate, crypto finance, and a clinical platform.
Creator monetization platform development case study
A creator monetization platform that scores audience quality with AI and pays creators on performance, not follower counts, with Instagram, TikTok, and YouTube metrics normalized into one score.
Crypto trading platform with wallet, card & academy
An integrated crypto trading platform that puts trading, hardware-wallet custody, a debit card, fiat on- and off-ramps, and an education academy under one account.
Medication management software for patients and care teams
Medication management software with two sides, an accessible patient app and a clinical adherence dashboard, joined by bidirectional HL7/FHIR EMR sync.
Notes on scoping
AI that ships
Field notes from the engineers who run these audits — on picking use cases, reading data, and knowing when AI is the wrong tool.
- AI & Machine Learning · 20 min · January 7, 2026
Model Context Protocol (MCP) for Developers
- AI & Machine Learning · 19 min · January 5, 2026
Claude Code Skills: Teaching Your AI Coding Agent Your Stack
- AI & Machine Learning · 18 min · January 2, 2026
The AI Software Engineer: What the Role Actually Is Now
- AI & Machine Learning · 18 min · December 31, 2025
AI Pair Programming: How Senior Engineers Work With AI
- AI & Machine Learning · 18 min · December 29, 2025
AI Coding Tools in 2026: The Landscape, Honestly
Readiness assessment
questions
What teams ask before booking a readiness audit.
An AI readiness assessment is a packaged audit of your data, systems, and candidate use cases that scores where AI pays off and where it does not pay off yet. It is an assessment, not a build. Over two to four weeks we review what you have, rank the use cases by ROI and feasibility, and hand back a written report you can act on. The point is to know the first thing worth building before you spend a budget building it.
It measures readiness across six dimensions: strategy and use case, data, infrastructure and systems, talent and ownership, governance and compliance, and adoption. Each is scored for where you stand today, so the report shows not just whether you are ready but which gap to close first.
You get a written readiness report, your candidate use cases scored on ROI and feasibility, a data and systems audit, a risk register, build-vs-buy calls on each recommended initiative, and a 30/60/90-day roadmap. Everything is written down and defensible, so you can take the report to an exec team or a board and get a decision without a second round of meetings.
Two to four weeks. A focused review runs in two; four weeks covers a wider use-case list, more systems to inspect, or stricter compliance ground to walk. We agree the scope at kickoff so the timeline is set before we start, and the readout lands on the date we name.
Each use case gets two scores: business value (the ROI if it works) and feasibility (whether your data and systems can support it today). Plotting both separates the initiative that reads well in a deck from the one that can actually ship next quarter. The roadmap sequences the high-value, high-feasibility work first, so the first build targets real ROI.
Pick a provider whose engineers will build the thing they recommend and stay to ship it. The feasibility scores are only worth trusting if the people writing them have shipped AI in production and know what your data will and will not support. Our readout is a delivery roadmap, costed and sequenced, and the same engineers can build the first initiative on it.
You start the top-scored initiative. If it adds AI to a system you already run, that is AI Integration Services. If it is a new LLM or generative feature, that is Generative AI Development. The roadmap already names which path each initiative takes and in what order, so there is no fresh discovery phase. The build picks up where the assessment left off.
Find out where
AI pays off
Book a 2–4 week readiness audit with Idealogic — your data, systems, and use cases scored by ROI, with a written report and a roadmap to act on.