What is an AI Audit and what should it include?

Looking for a clear-eyed diagnosis of where artificial intelligence can deliver real value – and where deploying it would just burn time and budget?

The answer is an AI Audit – a systematic assessment of processes, data, and potential AI use cases that gives you the basis for making the right business decisions.

In this article, we explain what an AI audit is, when it’s worth carrying one out, which areas it should cover, and what results it should deliver. If you’re interested in a broader look at AI applications in business, check out our article 7 Examples of AI Use Cases in Business.

An AI audit is a systematic assessment of business processes, data assets, technology tools, and potential AI use cases within an organization. Its purpose isn’t to evaluate a specific language model or compare available platforms – it’s to answer the question of whether, and where, AI can genuinely improve the way a business operates.

Unlike a pilot deployment or a tool trial, an AI audit covers the entire organization or a selected functional area and looks at the problem from three perspectives at once: process, technology, and organization. That means it assesses not just the technology itself, but also team readiness, data maturity, and process maturity.

The outcome of an audit isn’t just a report. It should be a set of concrete recommendations: what to implement, what not to implement, where to start, and in what order – each backed by a business rationale.

Pressure to deploy AI keeps growing. According to the McKinsey Global Survey on AI 2024, more than 65% of organizations already use generative AI regularly – yet only some of them achieve measurable business benefits.

Many companies invest in AI tools and launch pilot projects without a clear plan or measurable goals. The result: costly experiments that don’t translate into efficiency gains. An AI audit helps identify where artificial intelligence will actually create value – and where deployment has no business justification.

Organizations that use AI effectively don’t start with the technology. They start by analyzing processes, data, and business goals. That’s exactly what an AI audit is for.

It’s worth conducting an AI audit when an organization is facing a specific decision or challenge, for example:

• no clarity on where to start with AI deployment and which areas will deliver the biggest business benefits,
• repetitive, manual processes that could be automated but need to be identified and prioritized first,
• fragmented or low-quality data that gets in the way of using AI effectively,
• underused AI tools, despite licenses being purchased and features being available,
• a planned rollout of AI, automation, or AI agents that calls for an upfront assessment of organizational readiness,
• the need for a business case and ROI evaluation before making an investment decision.

For large projects, complex processes, or work involving sensitive data, an AI audit often becomes an essential part of preparing for deployment.

1. Business process analysis 
   An AI audit starts with an analysis of key business processes to pinpoint tasks that are repetitive, time-consuming, and error-prone. Conversations with the employees who run these processes day to day are a critical part of this step.

   In companies using ERP systems, the greatest automation potential typically lies in period-end closing, invoice processing, purchase order handling, demand forecasting, and reporting. The output of this analysis is a list of processes together with an assessment of their readiness for AI deployment and the expected benefits.

2. Data and systems assessment The next step is assessing the quality of the data and systems the organization relies on. The audit checks data completeness and consistency, integration potential, and how fully existing ERP systems are being used.

   The goal is to uncover gaps that could hinder AI deployment and to identify the preparatory work needed before the project begins.

3. Identifying AI use cases Based on the process and data analysis, a list of potential AI use cases is compiled. Each one is evaluated for business value, feasibility, and impact on cost, efficiency, quality, and customer experience. Use cases are then split into quick wins and more complex projects.
4. Risk, security, and compliance assessment The audit also covers an analysis of risks related to data security, employees’ use of AI tools (shadow AI), and compliance with GDPR, the AI Act, and industry regulations. The result is a set of recommendations for AI governance.
5. Prioritization and ROI assessment Not every project is worth pursuing at the same time. The audit helps rank initiatives by potential benefits, cost, and level of risk, highlighting those that will deliver the fastest return on investment.
6. AI deployment roadmap The final step is preparing a deployment plan that covers the sequence of actions, timeline, required resources, and a recommendation on whether to start with a pilot, a proof of concept, or a full rollout.

For companies using ERP and CRM systems, an AI audit helps assess whether the existing infrastructure and data are ready for AI deployment. It looks at data quality, system capabilities, and available features such as Copilot in Microsoft Dynamics 365 Business Central.

The audit answers questions such as which processes can be automated within the current system, and which require additional integrations or dedicated AI solutions. It also assesses regulatory compliance, including the AI Act, particularly when processing financial data, employee data, and customer data.

The most common mistake is starting an AI deployment by buying tools instead of analyzing processes and data first. An AI audit helps reduce the risk of poorly targeted investments and makes better use of ERP and CRM system capabilities.

The biggest mistake is skipping the diagnostic stage. An AI audit shows where artificial intelligence will deliver real business value – and where deploying it would just burn time and money.

A good AI audit should deliver more than just a report – above all, it should provide concrete recommendations for further action, including:

• a map of processes with AI deployment potential,
• a list of recommended use cases with a business rationale,
• implementation priorities,
• an assessment of data quality and organizational readiness,
• a risk and compliance analysis,
• an estimated ROI for key projects,
• an AI deployment roadmap,
• a recommendation for the next step: a strategy, a PoC, a pilot, or a full rollout.

If an AI audit ends with nothing more than a report – with no clear priorities and no action plan – it’s hard to call it real support for business decision-making.

An audit is the examination: making the diagnosis and drawing up a treatment plan. Deployment, in turn, is the treatment itself – putting that plan into action. Starting treatment without a diagnosis is risky, and costly. And, after all, no one likes wasting time and money.

Sometimes the outcome of an audit is a recommendation not to deploy AI in a given area. That’s not a failed audit – it’s an honest assessment that lets the company gain without losing.

An audit also limits the risk of burning budget on trendy solutions that don’t fit the organization. In an environment where every vendor claims their tool will ‘revolutionize’ a given area, an independent diagnosis is especially valuable.

For an AI audit to deliver valuable recommendations, it helps to prepare the organization beforehand. The key steps are:

• identifying the processes and teams to be covered by the analysis,
• gathering information about the systems in use (ERP, CRM, and others) and the relevant data sources,
• involving the people who run the analyzed processes on a daily basis,
• defining the most important business problems, such as high costs, errors, delays, or time-consuming tasks,
• being ready to assess not just the technology, but also the processes and how work is organized.

Good preparation makes it easier to quickly identify the areas where AI deployment will deliver the greatest business benefits.

An AI audit can be carried out internally or with an external partner. For smaller organizations without in-house AI expertise, an external audit provides access to methodology, experience, and an independent point of view. If you’re wondering what this process looks like in practice, take a look at our AI consulting offering, or browse our full range of AI services.

Regardless of whether the audit is run by an internal team or an external partner, maintaining neutrality is essential: the outcome should be recommendations tailored to the organization’s needs – not to any particular vendor’s offering.

An AI audit helps identify where artificial intelligence will deliver real business benefits, which processes are ready for automation, and what steps are worth taking before deploying AI. It’s valuable both for companies just starting out with AI and for organizations already using ERP or CRM systems that are planning to integrate AI with their existing solutions.

Rather than investing in tools through trial and error, it’s worth starting with a rigorous analysis of processes, data, and business needs. An AI audit delivers concrete recommendations, priorities, and a deployment plan – reducing the risk of poorly targeted investments.

If you’re planning an AI audit or an AI deployment at your company, get in touch with us. We’ll help you assess the AI potential within your organization and prepare a plan for a successful rollout.