An AI readiness assessment is a short, fixed-fee diagnostic that tells a business what to build first: which workflows are worth automating, whether the underlying data and systems can support it, what a production build would cost, and what architecture it would need. Mihron AI runs this as a 2–3 week AI Readiness Sprint, fixed at CA$5,000–9,500.
Most Canadian business owners and operations leads already believe they should be doing something with AI. Fewer know what that something should be, and building the wrong thing first is an expensive way to find out. According to MIT NANDA, 2025, roughly 95% of enterprise generative-AI pilots produced no measurable return — not because the models underperformed, but because the work of connecting them to a business's own data and tools never got finished. The same research found a sharp split by who built the system: specialist-built deployments reached production about 3x as often as ones built in-house.
That gap is what a readiness assessment closes. Rather than starting with a build, it starts with a bounded, priced diagnostic: what should this business automate first, is the data and systems foundation actually there, and what would a real implementation cost. It produces a plan, not a pilot that stalls in a spreadsheet.
The AI Readiness Sprint is a fixed-fee, 2–3 week engagement priced at CA$5,000–9,500. Across a small number of working sessions, it maps the workflows across a business, examines the data those workflows depend on — where it lives, how clean it is, whether it is reachable through an API or trapped in someone's inbox — and reviews the systems an AI system would need to connect to, such as a CRM, EHR, ERP, or scheduling tool.
These are the shape of the deliverables; the exact outputs are scoped to the engagement, since a five-person clinic and a fifty-person logistics operation are not assessing the same thing. The plan belongs to the client at the end — it can be acted on with Mihron AI, handed to an internal team, or taken to another vendor.
Some of this a business can work through on its own, before ever commissioning an assessment. The honest answers here are usually a better predictor of success than any vendor pitch.
Where does the data this workflow depends on actually live, and can a system reach it programmatically — through an API or a database — or does it currently live in PDFs, spreadsheets, and someone's inbox? A workflow with clean, accessible data is a materially smaller and cheaper build than one that starts with data cleanup.
Which existing systems would an AI layer need to read from and write to — the CRM, the practice management system, the scheduling tool, the ERP? Every integration point is a place the project can succeed or stall; knowing the surface before scoping avoids the most common source of cost overruns.
Is there a named person accountable for how this workflow runs today, who can approve changes to it and answer questions about edge cases? A workflow with no clear owner is difficult to automate well, because no one can confirm what "correct" looks like.
What measurable outcome would prove the system is working — calls handled, response time, error rate, hours saved? Projects that skip this question tend to ship something that runs but that no one can say succeeded or failed.
Does this workflow touch personal information subject to PIPEDA, or health information subject to PHIPA in Ontario? Compliance requirements — data residency, retention, audit logging, a Data Processing Agreement — are architecture decisions, not paperwork to add after launch.
For a business reader, not an engineer, a production AI system is not one piece of software — it is a small number of moving parts that each carry cost and risk, and the decisions made about them at the assessment stage are what determine what the system costs to build and to run.
Decisions about integration and guardrails made early are cheap to change on paper and expensive to change once a system is live. That is the core reason a readiness assessment happens before a build, not alongside one.
Not every readiness assessment should end in a build with Mihron AI, and the Sprint is not structured to pressure that outcome. Where the plan does point to building something, it typically leads into one of two next stages, priced and scoped separately.
A business with one clear, high-priority use case usually moves to Pilot to Production, a 6–10 week fixed-fee build that takes that use case from spec to a live, measured system. A business with a broader, ongoing AI roadmap — several candidate use cases, not just one — is often better served by a Fractional AI Lead retainer, which provides part-time senior ownership of vendor evaluation, prioritisation, and monthly reporting rather than a single scoped build.
Some AI Readiness Sprint and Pilot to Production engagements may be eligible for Canada's Scientific Research and Experimental Development tax incentive. Eligibility is determined by the CRA on a case-by-case basis; this is not tax advice, and businesses should confirm eligibility directly with the CRA or a qualified SR&ED advisor.
For a full breakdown of what drives cost across these tiers, see the forward-deployed AI cost guide. For more on who actually does this work, see what a forward deployed engineer is. For the compliance layer underneath every engagement, see the PIPEDA/PHIPA guide.
The Readiness Sprint answers that in 2–3 weeks, fixed at CA$5,000–9,500.