Know where you stand before you spend on AI.
A fixed-scope diagnostic that grades your data, use cases, technology, team, governance, and ROI against what a real AI deployment demands. You get a written scorecard, a ranked use-case shortlist, and a phased roadmap — delivered in a few weeks, not a months-long study.
So the first dollar you spend on AI funds the thing that moves the business. From a Stanford-rooted Responsible AI lab that also builds the work.
Because the organization committed budget before it knew whether the foundation could hold the weight. The model was never the problem — the data was unlabeled and scattered, the use case demoed well but had no measurable return, governance hadn’t been thought through, and three months in the real costs surfaced. The pilot quietly gets shelved.
The numbers bear this out, and they point straight at readiness. An AI readiness assessment is the cheapest insurance against being in that 60%: a few weeks of structured grading that tells you, before the budget is committed, exactly which gaps would have sunk the project.
That diagnostic is the entire purpose of the assessment — to make those calls deliberately, with evidence, before a dollar is spent on building.
This isn’t a maturity quiz or a vague “AI strategy workshop.” It’s a structured audit across six dimensions, each one a place AI initiatives reliably fail. For each: what we grade, the decision it informs, and how that plays out.
Grades the quality, access, structure, and lineage of the data your use cases would actually run on — and flags the gaps that quietly break models in production. Benefit — you find the data problems before development, not 5 months into a stalled build.
For example, a team certain its CRM data was “clean enough” learns in the audit that key fields are 40% blank and duplicated across two systems — so the roadmap sequences a data fix first instead of discovering it after the model is half-built.
Separates the AI ideas that demo well from the ones with a measurable business return, and grades each on value versus the effort to deliver it. Benefit — you fund the work that pays, not the work that looks impressive in a board slide.
For example, a flashy customer-facing copilot scores lower than an unglamorous claims-triage automation with triple the ROI and a fraction of the risk — so the boring winner ships first.
Assesses whether your current stack — compute, integration points, deployment environment, security controls — can realistically run the use cases you have in mind, and names what has to change. Benefit — no nasty surprises when the model is ready but nothing around it can serve it.
For example, an assessment surfaces that a target use case needs real-time data access the current pipeline can’t provide — a gap that’s an afternoon’s planning now and a derailed launch later.
Grades your existing capacity against what adoption will demand, and maps the honest path — hire, partner, or upskill — for each gap. Benefit — the plan accounts for who will actually run the thing after launch, so it doesn’t stall the day the consultants leave.
For example, a company learns it has strong data engineers but no one to own model monitoring — so an enablement track goes into the roadmap instead of a capability cliff after go-live.
Reviews your current AI oversight, compliance exposure, and the guardrails a deployment in your sector would require — before the first model ships, not as an audit scramble afterward. Benefit — initiatives that clear legal and risk review on the first pass instead of stalling in it.
For example, a regulated lender gets decision-logging and human-review thresholds written into the plan up front, so the eventual build is approvable rather than stuck in compliance review for a quarter.
Costs each candidate — build, run, and the token/infrastructure economics — against projected value, and ranks the portfolio so funding goes in a defensible order. Benefit — leadership funds AI on numbers, not enthusiasm, and the first invoice is a forecast they’ve already seen.
For example, the top-ranked item carries a modeled 9-month payback with usage-scaling run costs, so the board approves it knowing exactly what “good” looks like and when.
This is a bounded diagnostic with a defined set of deliverables you own — not an open-ended advisory retainer. The scope, line by line.
Each of the six dimensions graded, with the evidence behind the grade and the specific gaps that would put a deployment at risk — in plain language for a CFO and a board, not buried in jargon.
Your candidate AI use cases ranked on value versus effort, each with build, run, and infrastructure economics modeled against projected value — so the sequence is defensible and leadership funds the work on numbers it has already seen.
For each prioritized opportunity, a scored call on building in-house versus configuring an off-the-shelf tool — on cost, control, switching risk, and time-to-value.
The concrete list of what your data and systems need before each use case can run — the fix that, sequenced now, is planning rather than a derailed launch, and which our data analytics engineering team can carry out if you choose to act on it.
Current oversight assessed and the guardrails a deployment in your sector requires, drawn from our human-led, responsible-AI practice — so governance is part of the plan, not a later scramble.
The prioritized work sequenced over time with dependencies and milestones, plus a leadership session that aligns the room on where you stand and what to fund next — the difference between a report that ships and one that sits in a drive.
What you get when you hire us — all documented and owned by you
It runs on the same delivery discipline behind all our AI development work, tuned for a diagnostic — one accountable lead, a deliberately bounded scope, no handoff to a sales team.
We agree the questions the assessment must answer, the use cases on the table, and the data and systems we’ll examine.
Output: a bounded engagement plan, fixed up front
We work through the six dimensions with your team, evidencing each grade against what a real deployment demands rather than a generic maturity scale.
Output: a graded scorecard & a ranked use-case shortlist
We model the ROI, run the build-vs-buy call on each priority, sequence the roadmap, and read it out to your leadership.
Output: the full readiness report & roadmap, owned by you
Transition the top-ranked opportunity straight into a costed build plan with the same accountable lead, under full work-for-hire IP assignment — or take the roadmap to any partner.
Output: implementation, owned by your team or ours
A focused, fixed-scope engagement that lands in a few weeks — not a months-long study; build engagements that follow typically reach steady state in 4–8 weeks.
A readiness assessment is only as good as the production reality behind the people grading you. A firm that only audits has never had to live with the gaps it waves through; we grade readiness because we know — from running software-critical systems in production — exactly which ones sink a deployment and which ones are noise.
The clearest evidence is BJ’s Restaurants, a 200+ location chain we’ve supported for four years. Coming in, production shipped roughly every two weeks — slow and batched.
Getting a traditional, multi-location operation to ship safely and often is precisely a readiness problem: process, quality gates, and risk controls all have to be honestly assessed before you change the cadence without breaking the business. We graded those gaps and restructured how work flowed through their existing team and stack — and they now ship twice a week with zero critical defects sustained across four years. The same eye that judged what that operation was ready for is the one we bring to grading whether yours is ready for AI.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. We’ll tell you plainly when you’re not ready, and which gap to close first — which a vendor paid to start building won’t.
We build what we assess. The grades come from a team that ships production AI, so “you’re not ready for this one” and “fix the data first” are honest calls — not a setup to sell you the most expensive build.
A bounded scope with a defined deliverable. This AI readiness assessment is a fixed-scope diagnostic that lands in weeks with a report you own — not an open-ended advisory engagement that bills indefinitely. You know what you’re getting and when.
Responsible AI is the founding charter. Governance, model risk, and accountability are graded as first-class dimensions, drawn from our Human-Led AI practice — not bolted on after a problem surfaces.
Founder-led, one accountable lead, no lock-in. The person who scopes the assessment answers for it; the roadmap is yours to execute with any partner or with us.
The grading carries the most weight where regulation and production risk make a wrong first move expensive.
Readiness graded against HIPAA and clinical-risk constraints, with the data, governance, and audit-trail gaps surfaced before any patient-facing or clinical use case is funded. Healthcare software →
Data lineage, model-risk controls, and explainability assessed up front for fraud-detection and real-time-decisioning use cases, where a governance gap is a regulatory exposure. Fintech software →
For chains, marketplaces, and multi-site businesses, readiness is graded against the reality that a pilot which never ships is an expensive mistake — exactly the environment we’ve run for years.
What teams want to know before they commit to an AI readiness assessment.
An AI readiness assessment is a fixed-scope diagnostic that grades your organization across six dimensions — data, use-case potential, technology and infrastructure, team and skills, governance and risk, and ROI — against what a real AI deployment actually demands. The output is a written scorecard, a ranked use-case shortlist, an ROI model, and a phased roadmap you own. In short: it tells you where you stand and what to fund first, before you commit budget to a build.
A written readiness report in plain language, a prioritized use-case shortlist ranked by value and effort, an ROI model for the candidate opportunities, a build-vs-buy recommendation per use case, a data and infrastructure gap analysis, a governance and risk review, a phased roadmap, and an executive readout. Everything is documented and owned by you — take it to any partner or have us deliver it.
A few weeks, not months. The scope is deliberately bounded so the assessment delivers quickly — long enough to grade the six dimensions honestly against your real data and systems, short enough that it doesn’t become a consulting study that outlives the decision it was meant to inform. We agree the exact timeline with you before any work starts.
The readiness assessment is the scoped diagnostic — a bounded audit, with a fixed deliverable, that answers “are we ready, and what do we fund first?” AI consulting is the broader, ongoing advisory engagement that builds on that diagnostic to shape strategy, sequence a multi-year roadmap, and steer build-or-buy across the portfolio. Many engagements start with the assessment and continue into consulting or directly into a build; the assessment stands alone and obligates you to neither.
Against the requirements of a real deployment, not a generic maturity ladder. For each dimension we gather evidence with your team — data samples and lineage, system and integration constraints, current governance, the skills actually on staff — and grade it on whether it can support the specific use cases you’re considering. The grade always comes with the evidence behind it and the concrete gap to close, so it’s actionable rather than a number on a slide.
No — finding those is the point. We assess your data and systems and surface the use cases as part of the engagement. If the data foundation isn’t ready, identifying and sequencing that fix is one of the most valuable things the assessment produces — given that data readiness is the single biggest predictor of AI project abandonment (Gartner, 2025). Starting before you have it all figured out is normal and expected.
Your call, with no obligation. You own the roadmap and can act on it however you choose — with your own team, another partner, or with us. If you’d rather not re-scope with a new vendor, we can transition the top-ranked use case straight into a costed build plan under full work-for-hire IP assignment. The assessment’s value doesn’t depend on you hiring us to build.
Enterprise leaders deciding where and how to invest in AI — CIOs, CTOs, CDOs, and executives weighing a first or next AI program. It’s especially valuable for organizations running demanding production environments, where a pilot that never ships is an expensive mistake and the cost of getting readiness wrong is measured in stalled budgets and quarters lost.
Thirty minutes · No pitch deck
Tell us what you’re weighing — we’ll explain how the assessment grades your readiness, what you’ll walk away owning, and how quickly you’ll know which use case is worth funding first.