· AI
What an AI Readiness Assessment Covers, and What It Should Actually Cost
Most of what separates a two thousand dollar AI readiness assessment from a two hundred thousand dollar one is not rigor. It is how many people have to sign off on the invoice.
A small business owner asking "are we ready for AI" usually gets one of two answers: a free quiz that spits out a generic score, or a quote that starts at five figures and climbs from there depending on how many business units show up on the call. Neither answer tells you what you are actually paying for. So here is the plain version.
An AI readiness assessment is a structured look at whether your data, your processes, your people, and your governance can support an AI project before you spend money building one. Strip away the branding and most frameworks converge on the same handful of questions. Perceptive Analytics scores six dimensions: cloud and infrastructure, data quality, governance, analytics maturity, operational readiness, and whether proposed use cases tie to a real business metric. Layer3 Labs uses seven, adding vendor readiness and budget to the list. UKG's version collapses it to three: process and outcomes, data and technology, people and governance. Different vendors, same diagnostic.
The steelman for paying a lot
There is a real argument for the expensive tier, and it is not just that big firms charge more because they can. According to a UD Hong Kong analysis citing Cabin Consulting's 2026 pricing study, an enterprise-practitioner engagement running $40,000 to $120,000 produces six concrete artifacts: a scored maturity assessment, five to ten ranked use cases with rough ROI attached, a data and systems gap analysis, a governance and risk review, a twelve to eighteen month roadmap, and a capability plan for the team. A Big Four engagement at $150,000 to $500,000-plus adds a target operating model and a change management plan across multiple workstreams, often 200-plus stakeholder hours of work. Entrans separately notes the same pattern: price climbs with data estate complexity and regulatory domain count, not company size alone. A multi-unit company in a regulated industry, financial services and healthcare are named specifically, has real coordination and compliance exposure that a $3,000 checklist will not surface. There, a bigger team and a longer engagement buy actual additional work, not just ceremony.
Why most small businesses are overpaying for that
Here is the math that argument skips for a business with one location and a handful of core systems. The same Cabin Consulting data shows a boutique fixed-fee assessment running $2,000 to $25,000, taking two to four weeks and 20 to 40 stakeholder hours, with a comparable scored assessment and roadmap as output. Entrans puts its boutique tier at $3,000 to $15,000 over one to three weeks, with prices for narrow assessments starting around $1,500. Do the division: the boutique tier runs roughly $50 to $700 per stakeholder hour depending on scope, while the enterprise tier runs $330 to $2,000 per hour for a process that is structurally the same, just scored across more systems and more business units. Cabin Consulting's own pricing note says three business units in scope costs about 2.5 times what one costs. A business with one business unit is paying for workstreams it does not have.
The harder admission, and this is a case Layer3 Labs makes against its own interest since it sells the paid version: a lot of what the expensive tiers charge for is not diagnosis, it is documentation and consensus-building across people who were never going to agree anyway. Its own checklist argues that data readiness and process readiness are the two things worth fixing before you spend anything, because, as it puts it, a good vendor "can't rescue messy data." You cannot automate a process nobody has written down. That is a $0 finding. It does not take a consultant, let alone 120 stakeholder hours, to notice that your customer records live in three disconnected spreadsheets.
What this means for your next move
If you are a single-location business or a small multi-location one without a regulatory compliance burden, start with the free self-service tools from your existing cloud or software vendor, Microsoft, AWS, and most ERP vendors publish one, then fix what it finds in your own data and documented processes before paying anyone. That alone resolves the failure mode that actually kills most AI projects: not a bad model choice, but garbage inputs and an undocumented workflow nobody can hand to a vendor. Once that groundwork is done, a boutique, fixed-scope assessment in the $2,000 to $15,000 range, two to four weeks, a defined deliverable, buys you an outside, structured second opinion and a ranked list of where AI actually pays off for your specific business. You do not need the six-workstream enterprise engagement unless you have multiple business units, a regulator to satisfy, or genuinely tangled systems that the free tool cannot untangle for you.
This is the actual job of fractional, part-time AI advisory work done right: scope the assessment to what your business is, not what the vendor's price list defaults to, and say plainly when the $2,000 version is the right one. If you want a second opinion on what tier you actually need before you sign anything, that conversation is free. Start it at mojoaiservices.com/#contact.
Sources
References used in this article. Links also appear alongside the relevant claims.
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