AI Readiness Assessment
AI Readiness Assessment: where is your AI spend getting wasted first?
Find out where your AI spend gets wasted first. Free readiness assessment. Score your governance, data, skills and strategy in under 5 minutes.
Find out where your AI spend gets wasted first. Free readiness assessment. Score your governance, data, skills and strategy in under 5 minutes.
AI readiness is whether you are set up to adopt AI safely and get value from it: ownership, data, skills and governance. AI maturity is how far along that journey you already are. This assessment gives you a readiness verdict and names the specific failure mode most likely to waste your spend, rather than a vague maturity score.
You need ownership and a clear first problem more than a long strategy document. Most failed AI adoption is not a strategy gap, it is un-owned pilots, shadow AI and data that was never ready. This check finds which of those is your real blocker, so any AI strategy you write starts from the truth.
This assessment uses eight questions across ownership, data, governance and delivery to predict where your AI investment is most likely to leak value. Instead of scoring you on a maturity ladder, it names the dominant failure mode, un-owned AI, shadow AI, data not ready, or perpetual pilot, and tells you what to fix first.
An AI readiness assessment tests whether the conditions for successful AI adoption are in place before money is spent: ownership, data quality, usage visibility, and a disciplined approach to use cases. This one predicts which failure mode your business is heading for, not where you sit on a maturity ladder.
One possible verdict is that you should do less AI than you are planning. No vendor assessment will ever produce that output, which is rather the point of this one. If ownership is absent or data is fragmented, the honest advice is to fix that first, and the tool says so.
Shadow AI is AI use that has not been approved, documented, or risk-assessed: employees using consumer tools on company or customer data without IT or compliance sign-off. IBM's 2025 breach data found around £530,000 in additional breach costs at organisations with high shadow AI use (IBM figure converted from USD at mid-2025 rates). It is the fastest-growing unmanaged risk in most mid-market businesses.
Eight questions carry different weights based on evidence of business impact. Ownership, shadow AI visibility and data readiness carry triple weight; use-case discipline and workflow redesign carry double; governance, vendor scrutiny and measurement carry single weight. Maximum raw score is 80, displayed as a percentage. The score informs the verdict but the verdict is a failure-mode prediction, not a score band.
Eight questions. You receive a named failure-mode verdict (the way mid-market AI spend most likely fails given your answers), an area-by-area breakdown, the single highest-leverage action to take first, and a crosslink to whichever other assessment matters next.
CEOs, managing directors, and owners of mid-market businesses: people who make or approve AI investment decisions but are not necessarily running the technology themselves. No technical knowledge is needed. The questions ask what you could evidence today, not how your systems are configured.