AI Readiness Assessment
AI readiness assessment: data scored, cloud checked, the build order priced.
Before expanding AI across the business, establish what your data can support, where the system will run, and what the team needs for rollout. A readiness assessment answers those questions before you commit to the next build.
Built with the leading AI platforms
NVIDIA
Anthropic
The backwards order
The AI readiness assessment was supposed to come first.
From a check-in call with a multi-company construction group — an electrical contractor and its sister companies: one AI tool built and deployed, roughly thirty people waiting on rollout, and then every new AI initiative frozen for a six-week data-capability audit.
The pause came after the build
The group reported a delivered tool and a pending rollout, followed by a six-week outside evaluation that paused new AI work. That sequence illustrates why data and deployment readiness need attention before expansion.
“We don’t really have a cloud”
The infrastructure truth surfaced after the tool existed: no cloud environment to run it in, one engineer learning how to deploy on Azure, the team literally going back to the drawing board. Where AI runs is a readiness question — and nobody had asked it.
Data capability limitations, found late
The demo data behaved; the production data is another matter. What the company’s data capabilities actually are — which sources exist, what shape they’re in, what they can support — is being mapped for the first time, by an outside firm, after the money is spent.
The marketplace freeze
Every vendor conversation is on hold until the report lands — then they plan to “enter back into the marketplace on the tools.” Six weeks of momentum gone, and the re-entry still has no criteria beyond whatever the audit recommends.
Thirty people waiting on “ready”
Rollout is pending — around thirty users who need to be “ready to go when it comes time.” Nobody measured what ready means: no adoption baseline, no training plan, no definition anyone signed.
No build order, no prices
After the audit, then what? Which workflow is second, which is third, what each costs and what each pays back — the group has no roadmap for the next several weeks, let alone a priced order of work.
What building blind costs
What skipping the AI readiness audit actually costs.
What it looks like when the readiness questions get asked after deployment instead of before the build. Your numbers will differ — the first two weeks of an engagement put figures on yours.
From an anonymized engagement — a multi-company construction group with one AI contract tool deployed and every new AI initiative paused
How the assessment runs
The cloud readiness assessment comes before the build order.
Two weeks inside your operation, a fixed fee, and an answer you own either way — including when the answer is no. Most AI implementation services pitches start at the build; this is the step that decides what the build should be.
- 01
Score the data capabilities
What your data can actually support: where contracts, prices, schedules, and customer records live, how current they are, what shape they’re in, and which candidate workflows they can feed. Data readiness for AI is the section vendor demos skim; here it is the first gate — a data readiness assessment in plain language, with the limitations named.
- 02
Check the cloud and tenant reality
Where would the system run — your Azure tenant, a tenant that doesn’t exist yet, something on-prem? Whose security perimeter, whose keys, whose logs? “We don’t really have a cloud” is a finding, not a failure: it prices the environment work into the plan instead of discovering it after deployment.
- 03
Price the build order
Each candidate workflow gets a payback estimate against build cost, assumptions marked. The output is a ranked order — what to build first, what second, what to leave alone — and, where the data or the environment can’t support a build yet, an honest no with the fixes priced.
Where an AI maturity model helps — and where it’s astrology.
The readiness market is two things: self-score quizzes from the platform vendors selling the destination, and AI maturity model frameworks from the AI strategy consulting firms. The vocabulary is useful — experimenting, scaling, embedded is a decent shorthand for where a company sits in a board meeting.
The astrology starts when a score out of five becomes the verdict. No maturity quadrant can tell you whether your contract data can feed an AI review tool, whether you have a tenant to run it in, or which workflow pays back first. Those things are measured, not scored — and that measurement is the assessment.
The assessment is also the before, not the after. Who owns the system once it’s adopted — the policy, the supervision, the ownership model — is AI governance consulting, a sibling engagement that starts where this one ends. And if your team is still in “we’re in discovery phase” mode, this is the same instinct with numbers attached.
Led by Peter Enestrom.
Questions
The AI readiness checklist, answered straight.
What operators ask before they let anyone score their readiness.
What’s holding your business back?
A workflow ready for automation. An AI product you want to build. A problem that has sat on the roadmap for years. Let’s talk about what it would take to solve it.
Talk to Zaigo