Generic AI readiness assessments return a five‑level maturity score and a generic improvement roadmap. Useful for nobody. Our AI readiness audit is scoped to a specific AI use case you are actually considering, run by the engineers who would build it, and it ends with a technical report: data gap map, infrastructure review, EU AI Act compliance checklist and a scoped first project. At Pixelfield we audit AI readiness for organisations where stalled pilots, unclear data, or regulatory exposure have made the next AI move feel out of reach.
Delivered by senior AI engineersScoped to a real use case, not a generic scoreData, infrastructure, compliance, teamTwo to four weeks, free initial audit
You kicked off three pilots, two produced demos that looked great on a laptop, and nothing made it into production. That pattern is almost always a readiness problem dressed up as a technology problem. Our audit finds the actual reason — usually data access, integration, or unclear ownership — and writes the shortest path back to a shippable system. If the right answer is to stop the pilot, we say so in writing.
August 2026 is not a theoretical deadline. General‑purpose AI obligations under the EU AI Act become enforceable, with fines up to €35M or 7% of global turnover. Most UK organisations we audit have either no risk classification in place or one written by a legal team with no visibility of the actual AI pipeline. Our audit pairs engineering reality with regulatory mapping so the compliance file survives internal audit, not just board review.
The phrase "our data is a mess" means very different things across organisations. Sometimes it means fixable lineage problems. Sometimes it means no single source of truth for the entity the AI needs to reason over. The audit distinguishes the two and sizes the remediation work honestly. Clients often come out with a data programme to run before the AI programme, and thank us for it twelve months later.
Boards are asking CTOs to show the AI governance framework, and most of what exists on the internet is abstract. We audit what you have: approval workflows, model inventory, prompt and output logging, bias and drift monitoring, incident response. You get a governance checklist grounded in the actual systems you plan to run, not a generic policy template, and a clear statement of who owns what once the first AI system is live.
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We begin with a 90‑minute working session with your technical and business leadership. The goal is to pin the audit to a specific AI use case, the business outcome it is supposed to produce, the constraints that matter (latency, cost, compliance, timeline) and the stakeholders we need access to.
Generic audits fail here; scoped audits start here. You leave the kickoff with a one‑page audit brief and a list of what we will look at, and we agree which three to eight people we need to interview during the engagement.
With read‑only access, or sample extracts if access is constrained, we review data schemas, quality metrics, pipeline lineage, warehouse and lake footprint, identity and access controls, and the inference path a new AI system would run on.
For most clients this is where the surprises live: the data you think you have is not the data you have. We document findings in plain English, with schema‑level detail for the engineers, and size the remediation work honestly. Seemingly simple use cases almost always hide data issues — the audit is where we uncover them, not during rollout.
In parallel with the infrastructure work, our compliance‑aware engineers map the proposed use case against the EU AI Act risk tiers, UK GDPR, DPA 2018 and any sector framework (FCA SYSC, NHS DSPT, PCI DSS) that applies. Gaps are listed with the specific artefact required to close them.
We also interview three to eight people — the sponsor, the engineer who would own the system, the domain expert whose data feeds it, and the compliance or risk contact. Interviews are 30 to 45 minutes. We do not ask you to fill in a 200‑cell maturity spreadsheet.
We deliver the report in a 90‑minute working session with your leadership. Format: a written document (typically 20 to 35 pages) plus the key artefacts — the first project scope, the dependency map, the compliance checklist and the cost envelope.
We walk through the findings live, answer the hard questions, and leave you with a roadmap you own. If a proof of concept or remediation project makes sense, we quote for it separately. If it does not, the audit stands on its own.
Frequently asked questions about our AI readiness audit <strong>services</strong>, process and deliverables.
An AI readiness assessment is a structured review of whether a specific AI use case can actually be built and run inside your organisation. It covers four layers: data availability and quality, technical infrastructure, compliance and regulatory exposure, and organisational ownership. Our audit ends with a scoped first project, a dependency map and a cost estimate, not a maturity score.
A maturity assessment returns a score on a five‑level scale and a generic improvement roadmap. Our readiness audit is scoped to a specific business objective and returns a technical report: can you build this AI system, with your data, on your infrastructure, under your regulatory constraints, with your team. If the answer is no, the report tells you exactly what needs to change first.
Two to four weeks, depending on the size of your data estate and how many stakeholders we need to interview. The initial scoped readiness audit is free. Deeper engagements — portfolio prioritisation, full PoC feasibility — are priced by scope. We quote before we start and there is no commitment to a follow‑on build.
Yes. For any organisation operating in the EU or with EU customers, we map the proposed AI use case against the EU AI Act risk tiers and produce a gap checklist against the general‑purpose AI obligations that take effect on 2 August 2026. We also cover UK GDPR, DPA 2018, FCA SYSC and NHS DSPT where the buyer segment requires it.
That is a useful outcome, not a failure. We have told clients to fix a data warehouse before funding a model, or to delay a customer‑facing deployment until EU AI Act documentation is in place. The report is written so you can hand it to an internal team or another vendor and it still stands up. After the audit, most clients move into a scoped proof of concept, a structured remediation project, or a larger AI build. A smaller group takes the roadmap internal and executes it themselves. Either way, the roadmap is yours.
