According to a 2025 MIT Sloan study, over 70% of enterprise AI projects fail to move beyond pilot. The reason is rarely the technology. It is organisational readiness: messy data, undocumented processes, unclear goals. An AI readiness audit is a structured €2,000-€5,000 assessment, completed in one to two weeks, that answers a single question before you spend on development: are we ready, and if not, what changes first?
The five pillars
1. Data readiness. AI needs data that is structured, clean, accessible, and sufficient. The audit checks where data lives, how it is labelled, and whether it is reachable via APIs. Common finding: data exists but is scattered across five systems with no single source of truth.
2. Process readiness. AI automates processes, but the process must be documented, stable, and repeatable. Common finding: companies want to automate their most chaotic process. Start with the most stable one - that is where process automation delivers the fastest return.
3. Team readiness. Someone needs to own the initiative. The audit checks whether your team understands AI’s limits and whether the project has a realistic champion. Common finding: executives are enthusiastic, but operational staff have not been consulted.
4. Infrastructure readiness. Can your stack support AI? The audit evaluates cloud, APIs, and security. Common finding: the software is adequate, but no APIs connect key systems. Building those usually becomes phase one. See API integrations.
5. Business case readiness. “We want AI” is not a business case. “Cut invoice processing from 4 hours/day to 30 minutes” is. Common finding: a vague desire to use AI without a specific, measurable target.
Scoring and outcome
We score each pillar 1-5. A company scoring 3 or higher on all five is ready to start a focused project. Below 3 on any pillar means remediation first. Most audits land between 2 and 4, with data and business case as the weakest points.
The deliverable is a written report: scores, gap analysis, prioritised roadmap, ranked use cases, budget estimates, and a clear go/no-go. The report is yours - take it to any partner.
When to wait
An honest audit sometimes says: not yet. Signs include messy data, unstable processes, no clear ROI, or a team that resists the initiative. Spending €3,000 to learn you are not ready is far cheaper than spending €30,000 to discover it the hard way.
Audit answers “should we invest in AI at all, and where?” - a Discovery sprint answers “how exactly do we build this?” They are sequential.
Frequently asked questions
How long does it take? One to two weeks, depending on departments and systems. Includes stakeholder interviews, data assessment, infrastructure review, and report preparation.
Do we need to share sensitive data? No. The audit evaluates structure, accessibility, and quality - not content. We work under NDA without accessing confidential records.
Can we do it ourselves? You can use the framework as self-assessment. An external audit adds an unbiased perspective and technical depth on infrastructure and data architecture.
What if we score low across the board? That is a valid outcome. The audit becomes a remediation roadmap: fix data in Q3, document processes in Q4, revisit readiness in Q1.
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Find out if your company is ready
We run AI readiness audits across Croatia and the EU. The deliverable is yours; the recommendation is honest. Reach us at info@tsunami-digital.com or via the form on our homepage.