31 August 2026
Enterprise GenAI Audit: method, deliverables and pricing
A practical guide to enterprise GenAI audit scope, steps, deliverables, timeline, pricing, and roadmap decisions.
Why run an enterprise GenAI audit
An enterprise GenAI audit turns a broad interest in artificial intelligence into operational decisions. Leaders want to know where to invest. Business teams want to understand what changes in the day-to-day workflow. Technical teams want a reliable frame for building, integrating, and maintaining systems. The audit creates that shared frame. It starts from use cases, data, existing systems, risks, and delivery constraints, then produces a clear path toward production.
The right moment often comes after several experiments: tools tested by different teams, a promising POC, a busy product roadmap, or a leadership team ready to accelerate with discipline. The goal is simple: know what deserves to be built, in which order, with which architecture, budget, and internal effort. A useful audit gives a prioritized view, a practical plan, and enough technical detail for teams to act.
What a serious GenAI audit covers
I begin by mapping current usage and business friction. Where do teams lose time? Which decisions depend on slow document search? Which workflows involve repeated synthesis, writing, quality review, extraction, or classification? This first layer connects generative AI to concrete problems: internal support, contract analysis, market intelligence, editorial preparation, sales assistance, operations, knowledge management, or team productivity.
The second layer looks at the foundations: available data, access rights, document quality, SaaS tools, internal APIs, security, legal constraints, governance, and skills. A GenAI audit also evaluates team maturity: who owns the product, who validates outputs, who maintains prompts, who measures quality, who handles incidents. This keeps the discussion larger than model selection. The model matters, while value comes from the complete system around it.
The two-to-four-week method
An effective format usually fits into two to four weeks. The first week is about scoping: objectives, perimeter, sponsors, stakeholder list, inventory of tools already tested, access to key documents, and decision criteria. I try to understand the real operating context: business language, validation constraints, publication cycles, sensitive data, traceability needs, and the expectations of the users who will rely on the tool.
The following weeks combine interviews, document review, architecture review, and prioritization. In a strategy audit, interviews matter a lot: more than twenty conversations can be useful when several departments are involved. In a technical audit, I spend more time on data flows, prompts, logs, evaluations, inference costs, and integration choices. The rhythm stays short because the output is a decision and a path to action.
Stakeholder interviews and field analysis
Interviews provide the most important raw material. I meet the decision-makers who fund the work, the managers who arbitrate, the users who live inside the workflow, the data or IT teams that own the systems, and the people responsible for compliance or security when the context calls for it. Each conversation looks for the same things: real work, irritants, quality expectations, exceptions, volumes, and the way an AI answer will be accepted or reviewed.
This phase often creates the first useful tradeoffs. One use case can look spectacular and create limited value. Another can be quieter and remove a repeated daily burden. A mature audit ranks opportunities across four dimensions: business value, technical feasibility, risk, and adoption capacity. That score keeps the leadership conversation legible and gives delivery teams a practical queue.
The deliverables clients should expect
The first deliverable is a prioritized roadmap. It lists the selected use cases, their objective, target users, complexity, dependency on data, risk level, estimated effort, and next step. A strong roadmap separates quick wins, structural work, and topics to explore later. It helps leadership decide what to launch now, what to prepare, and what to fold into a broader transformation path.
The second deliverable is the audit report, usually completed by an executive presentation. It summarizes current maturity, observed gaps, architecture choices, governance needs, risks to monitor, Build, Audit, or Train recommendations, and success indicators. I like to add a very concrete execution backlog: tickets, likely owners, dependencies, expected proof, acceptance criteria, and delivery order.
Architecture and data review
When the company already has a prototype, internal chatbot, RAG system, agent, or automated workflow, the audit needs to inspect the architecture. I review how documents enter the system, how they are chunked, indexed, filtered, cited, and refreshed. I also look at permissions, secret management, traces, costs, environments, human fallback, and prompt quality. The goal is to make the system more operable, measurable, and maintainable.
This technical review leads to practical decisions: keep the current approach, strengthen evals, reshape the data structure, replace a fragile component, split a workflow into more observable steps, or move from POC to a real product delivery. The useful deliverable shows the target architecture with enough detail for the team to understand the choices and continue after the audit.
The quick-win backlog
An AI audit should also produce immediate action. The quick-win backlog gathers improvements that need few dependencies and create a visible signal: clarify a system prompt, add validated sources, improve metadata, measure recurring queries, reduce a model call cost, instrument errors, formalize human review, or turn individual usage into a team routine. These actions create momentum while the more structural work is prepared.
I keep quick wins separate from bigger bets. The first group builds confidence and learning. The second group needs a product decision, an owner, a budget, and real delivery capacity. This distinction protects the roadmap: the company gets visible progress while it progressively builds the architecture, governance, and skills required for durable GenAI systems.
How enterprise AI audit pricing works
Pricing depends mostly on scope, number of stakeholders, technical depth, and the expected level of restitution. A targeted audit focused on one team, one product, or one existing system often sits between €8,000 and €18,000. A broader audit with several business units, architecture review, detailed prioritization, and executive presentation usually sits between €18,000 and €35,000. Multi-entity or heavily regulated programs can go higher, especially when the audit directly prepares production delivery.
The best way to scope the budget is to define the expected decision first: choose the three priority use cases, secure an existing system, prepare a POC, or give leadership a clear investment plan. If you want to frame this type of audit for your organization, you can write to me from the contact section with the context, the teams involved, and the systems already in place. The first conversation confirms the perimeter and the most useful format.