Service · AI
Know where you stand before you spend on AI.
A fixed-scope diagnostic that grades your data, use cases, technology, team, governance, and ROI — so your first AI dollar funds the thing that moves the business. A scorecard, ranked shortlist, and roadmap you own, in a few weeks.
Readiness scorecard
GRADING…Illustrative grades — every real grade comes with its evidence and the gap to close.
The real problem
Why so many AI projects die before they reach production.
Because budget gets committed before anyone knows the foundation can hold the weight. The model is never the problem — the data is scattered, the use case demos well but has no measurable return, governance was never thought through.
A readiness assessment is the cheapest insurance against that: a few weeks of structured grading that tells you, before the budget is committed, exactly which gaps would have sunk the project.
Of AI projects unsupported by AI-ready data will be abandoned through 2026.
What it grades
What the assessment grades — and what each dimension tells you.
Not a maturity quiz — a structured audit across six dimensions, each one a place AI initiatives reliably fail.
Data readiness
Grades the quality, access, structure, and lineage of the data your use cases would run on.
You find the data problems before development, not 5 months into a stalled build.
Use-case potential
Separates the AI ideas that demo well from the ones with measurable return, grading each on value versus effort.
You fund the work that pays, not the work that looks impressive in a board slide.
Technology & infrastructure
Assesses whether your stack — compute, integration, deployment, security — can run the use cases you have in mind, and names what has to change.
No surprise when the model is ready but nothing around it can serve it.
Team & skills
Grades your capacity against what adoption will demand, and maps the path — hire, partner, or upskill — for each gap.
The plan accounts for who runs it after launch — so it doesn't stall the day the consultants leave.
Governance & risk
Reviews your AI oversight, compliance exposure, and the guardrails your sector requires — before the first model ships.
Initiatives clear legal and risk review on the first pass instead of stalling in it.
ROI & prioritization
Costs each candidate — build, run, infrastructure — against projected value, and ranks the portfolio in a defensible order.
Leadership funds AI on numbers, and the first invoice is a forecast they've already seen.
60% of AI projects on un-ready data get abandoned. A few weeks of structured grading is the cheapest insurance — it tells you which gaps would sink the build before the budget is committed.
As of June 2026 · revisit quarterly
What readiness does to the odds — the measured impact.
Independent industry findings, cited as third-party evidence — not Silicon Prime's own client results.
Lack the right data practices. Of organizations don't have, or aren't sure they have, the data management practices AI needs.
Blame data readiness. Of enterprises say more than half their AI projects were delayed, underperformed, or failed because of data-readiness issues.
Are AI high performers drawing more than 5% of EBIT from AI — separated from the rest by workflow redesign, not the model.
What's delivered
What the AI readiness assessment delivers.
A bounded diagnostic with deliverables you own — not an open-ended retainer.
A written readiness report
Each of the six dimensions graded, with the evidence and the gaps that would put a deployment at risk — in plain language for a CFO and a board.
A prioritized shortlist & ROI model
Your candidate use cases ranked on value versus effort, each with build, run, and infrastructure economics modeled against projected value.
A build-vs-buy call per use case
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.
A data & infrastructure gap analysis
The concrete list of what your data and systems need before each use case can run — which our data analytics engineering team can carry out if you choose.
A governance & risk review
Current oversight assessed and the guardrails your sector requires, drawn from our human-led, responsible-AI practice — so governance is part of the plan, not a later scramble.
A phased roadmap & executive readout
The prioritized work sequenced with dependencies and milestones, plus a leadership session that aligns the room on what to fund next.
What you get when you hire us — all documented and owned by you
How it runs
How the assessment runs.
The same delivery discipline behind our AI development work — one accountable lead, bounded scope, no handoff to a sales team.
STEP 01
Scope
We agree the questions to answer, the use cases on the table, and the data and systems we'll examine.
Output: a bounded engagement plan, fixed up front
STEP 02
Grade
We work through the six dimensions with your team, evidencing each grade against what a real deployment demands.
Output: a graded scorecard & a ranked shortlist
STEP 03
Report
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
STEP 04
Build first use case (optional)
Transition the top-ranked opportunity straight into a costed build plan under full work-for-hire IP — or take the roadmap to any partner.
Output: implementation, owned by your team or ours
Track record
A diagnostic from people who run AI in production.
A readiness assessment is only as good as the production reality behind the people grading you. We know from running software-critical systems which gaps sink a deployment and which are noise.
A Stanford-rooted Responsible AI lab, founded 2011, run by founder Kelvin Tran — 20+ years of production engineering. We'll tell you plainly when you're not ready, and which gap to close first.
Readiness, graded then fixed · 200+ locations · 4 yrs
For BJ's Restaurants, shipping a 200+ location operation safely and often was a readiness problem — process, quality gates, and risk controls all had to be honestly assessed first. We graded those gaps and restructured how work flowed through their team and stack, and they now ship twice a week with zero critical defects sustained across four years. The same eye is what we bring to grading whether yours is ready for AI.
Why have us grade your readiness.
We build what we assess. Grades come from a team that ships production AI, so "fix the data first" is an honest call — not a setup to sell you the most expensive build.
A bounded scope with a defined deliverable. A fixed-scope diagnostic that lands in weeks with a report you own — not an advisory engagement that bills indefinitely.
Responsible AI is the founding charter. Governance, model risk, and accountability are graded as first-class dimensions, drawn from our Human-Led AI practice.
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.
Where it matters most
Where a readiness assessment matters most.
Where regulation and production risk make a wrong first move expensive.
Healthcare
Readiness graded against HIPAA and clinical-risk constraints, with data, governance, and audit-trail gaps surfaced before any clinical use case is funded.
Healthcare software →Fintech
Data lineage, model-risk controls, and explainability assessed up front for fraud-detection and decisioning use cases, where a governance gap is a regulatory exposure.
Fintech software →Enterprise & multi-site operations
Production-heavy operations where a pilot that never ships is an expensive mistake — readiness graded against what real deployment at scale demands.
Enterprise apps →Questions buyers ask before they commit.
Thirty minutes · no pitch deck
Ready to know where you stand before you spend?
Tell us where you want AI to take the business — we'll grade the six dimensions honestly and hand you a scorecard, a ranked shortlist, and a roadmap you own.