AI process audit: which processes in your company are truly suited for AI support

My thesis based on conversations, experience, and work with clients

In most organizations I work with, the problem is not a lack of processes that could benefit from AI. The problem is that there are too many such processes, and the selection criteria are too unclear. Teams submit dozens of ideas. The board wants to see results. And between the two, there is no rigorous selection process to answer a simple question: which process do we start with, and why that one in particular.

An AI process audit is not about mapping everything a company does. It is a business filter that identifies where AI will deliver real value — not just a technological curiosity.

What this means in practice

In practice, companies often have anywhere from a dozen to several dozen processes that someone has flagged as potential candidates for AI support. Some come from operational teams, some from IT, some from the board after the latest conference. The problem is that these ideas live in various presentations, spreadsheets, and people's heads, but no one has put them through a common set of criteria.

Not every process is suited for AI. You need to assess several things at once: process repeatability, data availability and quality, impact on business outcomes, regulatory risk, team readiness, and the cost of implementation relative to expected value. Only when these dimensions are compared side by side does a picture emerge that supports decision-making rather than just list-making.

Why this is a problem right now

Because the decision window is narrowing. A year ago, companies could afford to experiment without structure. Today, boards expect results, AI budgets are growing, and at the same time disillusionment is rising with deployments that have not delivered expected value. MIT Sloan research confirms that the biggest problem is not the technology, but choosing the right places to apply it.

At the same time, technology vendors are offering more and more ready-made solutions, which paradoxically makes choosing harder. Companies without clear selection criteria either deploy too many things at once or deploy in places where the impact is marginal. Both paths lead to the same outcome: disappointment and loss of internal momentum.

What actually works

What works is an approach where process evaluation is not purely technical. I have seen many times that a process which looks ideal for AI from a data and repeatability standpoint turns out to be impossible to implement because the team is not ready or the process owner does not see the value. And the reverse: processes that seem less obvious but have a strong business sponsor and good data quality delivered results far more quickly.

That is why I use a multidimensional assessment. I look at repeatability, data availability, business impact, risk, people readiness, and implementation cost. But above all, I look at whether the organization is actually able to change that specific process. Because AI is not implemented in a vacuum. It is implemented in the context of people, processes, and decisions that already exist.

The outcome of the audit is a prioritized list of use cases with a clear recommendation: what to implement first, what to defer, and what not to do at all. This is not a report that ends up on a shelf. It is a decision-making tool.

How I work on this with clients

I start with conversations with the people who know the processes from the inside: operational leaders, teams, IT, and business stakeholders. I do not begin with a list of processes — I begin with understanding what the organization wants to achieve and where it feels the greatest pain.

Then, together with the client, we evaluate the candidates. I use a framework that incorporates business, technical, and organizational dimensions. Every process is assessed against the same categories, which enables an objective comparison and clear prioritization.

What matters is that I do not stop at a document with priorities. I co-create an action plan with the client and take shared responsibility for its execution. That means I am present when the first implementations launch, I help address problems that arise along the way, and we adjust priorities when the context changes. This is not an audit that disappears after the presentation. It is joint work to ensure the choices prove sound.

My takeaway for COOs and operational leaders

Do not try to implement AI everywhere at once. And do not start with the process that is being requested the loudest. Start with the one that meets the best combination of criteria: it makes business sense, data is available, the team is ready, and the risk is manageable. The rest can wait. And that is not failure. That is maturity.

FAQ

How does a process audit differ from an AI readiness audit?

An AI readiness audit answers whether the organization as a whole is prepared to implement AI. A process audit goes deeper into operations and answers which specific processes are worth supporting with AI and in what order.

How many processes does a typical audit cover?

It depends on the organization, but I usually work with a list of 10 to 30 candidates. The goal is not to review everything the company does, but to rigorously assess the processes with the greatest potential that are realistic to implement.

What is the final deliverable of the audit?

A prioritized list of use cases with a multidimensional assessment, a recommended implementation sequence, and a preliminary action plan for the highest-priority processes. This is not a strategic report — it is a concrete decision-making tool.

Does the audit require an existing AI strategy?

It is not a prerequisite, but it helps. If the company already has an AI strategy, a process audit is its natural next step. If it does not, the audit can be a good starting point for a strategy conversation because it provides a concrete picture of what is possible.

Invitation to connect

Dear Reader. If you see that your organization has many ideas for AI but lacks clear criteria for which process to start with, I invite you to a conversation. Not to immediately launch an implementation, but to jointly assess where AI will deliver the most value and how to prepare for it.

For subject-matter review

• Do we want to show sample process evaluation criteria on this page, or leave that for the conversation stage?
• Check whether we can publicly describe E.ON as a proof asset in the context of a process audit.
• Consider adding a section with typical processes most commonly suited for AI support as an educational element.