Here is a pattern that shows up in a lot of mid-sized companies in 2026. Marketing runs three AI writing tools. Sales has a chatbot pilot. Finance is quietly testing something nobody else knows about. The board has asked the CEO what the company's AI strategy is, and the honest answer is "a collection of experiments".

Nobody is doing anything wrong, exactly. The problem is that nobody senior owns the whole picture. That is the gap a fractional Chief AI Officer is built to fill.

What is a fractional Chief AI Officer?

A fractional Chief AI Officer is an experienced AI leader who takes on the CAIO role part-time, typically a few days a month, rather than as a full-time employee. The responsibilities match the full-time version: set the AI strategy, decide which use cases come first, put governance in place, and make sure the organization actually adopts what it builds. The difference is the commitment model.

Miklós Róth describes his work at Roth AI Consulting in exactly these terms. The goal is to help companies turn scattered AI experiments into governed, revenue-driving adoption, without the cost and the months-long search of a full-time hire. He works embedded, coaches the internal team, and builds capability meant to outlast the engagement.

When does a fractional CAIO beat a full-time hire?

The fractional model fits one stage especially well: when a company is past the "should we use AI?" question but not yet big or mature enough to justify a permanent C-level seat. For many B2B SaaS, fintech and professional-services firms in Europe, that describes 2026 almost exactly.

How does an engagement with Miklós Róth work?

1. A 20-minute diagnosis. Roth AI Consulting calls this its High-Velocity, High-Impact (HVHI) model. The point of a short first conversation is not to sell a long project. It is to give a leader one clear, usable priority instead of months of preparatory analysis. Twenty minutes forces focus on both sides of the table.

2. One priority, ranked properly. Use cases are ranked by expected ROI and by feasibility: data availability, process maturity, risk and the team's capacity to absorb change. The most exciting idea and the right first idea are rarely the same one.

3. Embed and build. Róth works inside the team rather than handing over a slide deck. That means coaching the people who will run the systems, shipping real use cases and adjusting the plan as evidence comes in.

4. Govern. Governance is built alongside delivery, not bolted on afterwards. That includes rules aligned with the EU AI Act, a named owner for each AI system, and agreed standards for what counts as acceptable output.

5. Hand over. The engagement is designed to end with capability that stays in the company: people who can evaluate, deploy and govern AI without needing the consultant in the room.

What makes Róth's version of the role different?

He is vendor-agnostic. Róth works across the major AI platforms and represents none of them, so recommendations follow the client's business outcomes, not a vendor's roadmap.

He has run the operating side himself. Róth has led a company since 2007 and, before that, a multilingual service team at IBM. Strategy advice from someone who has met a payroll and handled client escalations tends to be more realistic about what an organization can actually absorb.

He uses a diagnostic model, not a checklist. His S·I·C·T framework asks whether a company's structure and cohesion can keep pace with the information and change AI brings in. If they can't, adding tools makes things worse. The reasoning is in AI Does Not Fix Confusion. It Scales It.

He works in four languages. Native Hungarian, fluent English and Spanish, and advanced German make it easier to work directly with cross-border European teams.

How do you know your company needs one?

A few honest signs:

• Several AI tools are in use, and nobody can list them all.
• The board or the owners ask about AI strategy, and the answer is a list of pilots.
• Nobody can say who would switch off an AI system if it caused harm.
• AI spending grows faster than any measurable result.
• Your best people spend more time evaluating tools than doing their jobs.

If three or more sound familiar, the problem probably isn't the tools. It is the missing owner.

What should you prepare for the 20-minute diagnostic?

Twenty minutes go fast, so a little preparation pays off:

• A rough list of the AI tools in use, including the unofficial ones.
• Your top three business goals for the next twelve months, in plain numbers where possible.
• One process that hurts: slow, error-prone or expensive, where people already complain.
• Your constraints: data you can't move, regulations you must follow, budget limits.
• A likely owner: the person who would run an AI initiative day to day.

With that on the table, the conversation can skip the generic AI tour and go straight to the question that matters: where should we start?