AI Readiness Checklist for Midsize Businesses

Why readiness matters now

Tools get trialed, pilots stall, value is unclear. MIT Sloan reports many firms historically saw little measurable value when projects never reached meaningful deployment [2]. Meanwhile, broad adoption is climbing across Stanford’s AI Index reports—your competitors are moving, even if unevenly [4]. Readiness isn’t budget or hype—it’s clarity: the right data, the right problem, the right guardrails, and a pilot small enough to prove ROI quickly.

1) Is your data clean, accessible, and governed?

AI needs structured, accurate inputs. If customer records live in scattered spreadsheets or conflicting systems, you’ll accelerate the mess, not the value. OECD firm-level work repeatedly flags data availability and quality as key enablers—and barriers—of AI ROI [1]. Minimum bar: one source of truth (or reliable reconciliation), clear ownership for critical datasets, and basic privacy/retention/access policies aligned to the NIST AI RMF “Govern/Map” functions [3]. Quick win: profile the data behind your target use case (completeness, duplicates, freshness) before you buy anything.

2) Are you solving a problem that actually moves the needle?

Pick a business pain with visible cost or revenue impact—something leaders watch monthly. Common mid-market wins: intake & triage, document processing, forecasting & planning. Stanford notes adoption advances fastest where there’s direct process impact (time saved, error reduction, conversion lift) [4]. Acid test: if success wouldn’t move a KPI, pick another use case.

3) Are your workflows documented (in writing)?

AI thrives where the flow is understood. Capture inputs, steps, handoffs, exceptions, and “done.” HBR shows that process clarity and change readiness matter as much as the model itself [5]. One page per flow is enough.

4) Do you have governance & risk guardrails?

You don’t need a big committee—just explicit rules: what data’s allowed, how outputs are reviewed (human-in-the-loop for critical decisions), and how drift/errors are measured. NIST AI RMF is a practical scaffold: Govern → Map → Measure → Manage [3]. Start light: identify risks, assign owners, monitor and adjust.

5) Do the people who will use this actually want it?

Projects fail when frontline teams see “yet another tool.” HBR notes adoption hinges on behavior and incentives, not just tech [5]. Involve users early, design for fewer clicks, and show them what they’ll stop doing. A 60-minute co-design session often beats a 60-page spec.

6) Can you start small—and prove value in 30–90 days?

WEF emphasizes focusing on ROI evidence, not experimentation for its own sake [6]. Design a pilot that’s narrow, testable, and reversible: one workflow, one department, light integration, a tight data slice. If metrics don’t move, stop or tweak—no sunk-cost spiral. Stanford’s AI Index shows organizations progress faster with short, focused experiments that scale after proving value [4].

7) Will you measure business value (not just model metrics)?

Don’t stop at precision/recall. MIT Sloan argues leaders should insist on money-and-time metrics [2]. Back office: cycle time, touches per item, error/redo rate, throughput per FTE. Front office: conversion, time-to-first-response, containment rate, CSAT on the changed path. If none move, that’s a learning signal—refine or pick a better target.

8) Tech fit: can you connect to what you already have?

Readiness also means plumbing. You’ll go faster by wrapping with a lightweight front-end that talks to existing systems via APIs than by replacing your ERP/CRM. OECD and WEF frame modernization as integration-first for firms locked into core platforms [1][6]. Checklist: are APIs available, where will the model/service run, and how will you log usage/errors?

A 5-minute readiness score

• Clean, accessible data for the target process.
• Use case tied to a watched KPI.
• Workflow documented (inputs → steps → approvals → done).
• Basic guardrails (data allowed, human review, monitoring).
• Pilot scope tiny (30–90 days) and reversible.
• Business metrics agreed in advance.
• Integration path known (APIs/data access).
• Users involved and want it to succeed.

Score: 6–8 = ready to pilot. 3–5 = fix gaps, then start. 0–2 = don’t buy tools yet—do the groundwork.

The takeaway

Being “ready for AI” isn’t about budget size. It’s about clarity and fit. The research is consistent: companies that align AI to documented processes, measure business outcomes, and govern risks responsibly are the ones that see returns [2][3].