# How to choose a fractional AI officer

> Justas Butkus is a fractional AI officer based in Vilnius, Lithuania, founder of AINORA, MB and of Impetora, and a graduate of ISM University of Management and Economics.

Justas Butkus is a fractional AI officer based in Vilnius, Lithuania, working with mid-market companies and scale-ups across the UK, EU and US. He publishes the selection criteria he would want a buyer to apply, including the ones he finds difficult to answer.

**Ask what they have personally put into production, who is accountable when a system produces a wrong answer, and what they would tell you not to do. The strongest signal is whether someone will disqualify their own engagement; the weakest is a list of impressive logos.**

Canonical: https://justasbutkus.com/how-to-choose/
Last updated: 2026-08-01

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## A note on who wrote this

I sell this service, so treat this page accordingly. I have written it as the criteria I would want applied to me, including the questions I find least comfortable, because a selection guide that quietly favours its author is worth nothing to you and is obvious anyway.

## What have you personally put into production?

The single most useful question, and the one that most cleanly separates the field.

A great deal of AI advice is delivered by people who have never had to operate an AI system after it launched. That is where the difficulty actually lives: models drift, edge cases arrive, an answer is wrong in a way that reaches a customer, and someone has to decide whether to roll back. Advice from people who have not experienced that tends to be confident about the wrong things.

Ask for something specific and verifiable. Not a client name under NDA, which they cannot give you and which proves nothing anyway, but something you can go and look at.

## What would you tell us not to do?

A supplier who cannot name something they would decline is telling you they will take any brief you hand them.

Good answers are specific: this use case is not worth automating, your data is not ready for that, this problem is a process problem and AI will make it faster and no better. If every idea you float is met with enthusiasm, you are talking to someone selling capacity rather than judgement.

## Who is accountable when it produces a wrong answer?

Every AI system is eventually wrong in a way that matters. The question is whether that was anticipated.

- Is there a defined path for a customer or an auditor to challenge an automated decision?
- Is there human oversight where it matters, and is it real oversight rather than a person clicking approve on a queue?
- Can you reconstruct why the system produced a particular answer on a particular day?
- Is there a rollback that has actually been tested?

Someone who treats these as compliance overhead will build you something that works in the demo. Someone who treats them as design constraints will build you something that survives contact with a regulator or an angry customer.

## What happens if we stop working with you?

Ask it directly and early. You are looking for whether you end up owning the systems or renting them.

- Are accounts and infrastructure in your name from the start, or theirs?
- Is documentation a deliverable, or something produced at the end if the relationship is amicable?
- Could a competent internal engineer pick this up from what is written down?
- Is handover in the contract?

## Do you sell anything you would also recommend?

This one is aimed squarely at people like me, and you should ask it.

Anyone who advises on AI strategy and also sells AI products has a conflict. The conflict is not disqualifying, and in fact the people who build things often give better advice than those who only advise. But it has to be handled explicitly rather than left unmentioned.

What you want to hear is a stated policy: what they will not procure from themselves, what they disclose, and at what point they step out of a decision. If the answer is that there is no conflict, that is either an evasion or they have not thought about it.

## The signals that are worth less than they look

**Selection signals, ranked by how much they actually predict**

| Signal | What it tells you |
| --- | --- |
| Systems you can inspect yourself | A great deal. Hard to fake, easy to verify. |
| A written engagement structure | A lot. Shows they have done this more than once. |
| Willingness to disqualify work | A lot. Judgement rather than capacity. |
| Named clients | Less than it appears. Confirms someone paid, not that it worked. |
| An impressive prior employer | Little on its own. Working somewhere is not the same as having done the thing. |
| Certifications in AI strategy | Very little. The field is too young for the credential to mean much. |
| Confident numbers about your ROI, before diligence | Negative. Nobody can know that yet. |

## Frequently asked questions

### What should I ask a fractional AI officer before hiring them?

What they have personally put into production and can show you, what they would advise you not to do, who is accountable when a system produces a wrong answer, what happens to your systems if the engagement ends, and how they handle any conflict between advising and selling.

### How do I check someone is credible without client references?

Look for things you can verify directly rather than take on trust: systems you can inspect or use, published technical or regulatory depth, and a written engagement structure. Then run a working session on a real problem of yours before committing.

### Is it a problem if they also sell AI products?

Not inherently, and builders often give better advice than pure advisors. It becomes a problem when it is unmanaged. Ask for a stated policy on what they will not procure from themselves and when they step out of a decision.

### How many candidates should we talk to?

Two or three is usually enough, because the differences show up quickly once you ask what they would decline to do. Long processes tend to select for people who are good at long processes.

### What is the biggest mistake buyers make?

Choosing on confidence. The category rewards people who sound certain, and certainty at the selection stage is unwarranted, because nobody can know what your data will support before they have looked at it.

## Related

- [What a fractional AI officer does](/fractional-ai-officer/) — The role, and when it is premature.
- [Which role do you need?](/fractional-ai-officer/vs/) — Consultant, fractional officer, fractional CTO or a permanent hire.
- [How an engagement works](/fractional-ai-officer/how-it-works/) — The diagnostic, the first ninety days, and how it ends.

## Ask me these questions

The list above is the one I would want applied to me. If the answers are useful, that is a reasonable basis for a conversation.

Contact: justas@ainora.lt · [LinkedIn](https://www.linkedin.com/in/justas-butkus/)

## Other languages

- EN: https://justasbutkus.com/how-to-choose/
- LT: https://justasbutkus.com/lt/kaip-pasirinkti/