What makes a decision-maker ready for AI adoption?

There are a lot of foresight pieces on AI. Too many, even. We're now years in since the ChatGPT moment, so let us use hindsight for a change to determine who has succeeded in the race to extract value from LLMs in business.

While business is under pressure of being affected by AI (often in ways that are not well understood as of yet), each C-level suit feels an urge to become a modern Prometheus, bringing forth the fire of AI to enlighten their people and spark a candle of innovation. What a great position to be in! Having been on countless calls with prospective customers, watching how C-level decision-makers reason about AI, I asked myself a question: Are you the one who has what it takes to succeed?

We could not elaborate on the answer two years back when the technology and framework were still in their infancy, and no serious production-grade AI systems had been deployed… with the exception of frustrating support chatbots being more of an annoyance than of value. Fast forward to 2026, and there are now plenty of productive systems out there.

Systems we have built and shipped advises customers on choosing the right options for print products (at Mixam), speed up engineering workflows (at Synera), and accelerate the TRIAGE procedures ( STCC). Each of them became a success when they reach a level of operation that surpasses human peers; in advising, discerning, or designing. And behind each of these successes, there is a successful human being (or team of humans).

Watching them made it finally possible to answer the question: what makes a modern Prometheus?

The 'Let's check and see' attitude #

The making of the right C-level decision-maker to embrace AI is not a hyped up attitude. It is not even technical agility or the "being up to speed" that Silicon Valley trillionaires brag about. Each of the successful project owners we encountered was initially sceptical and timid. The recurring theme in our conversations with them was something like: "I'm not sure if AI could do well here, but prove me wrong" or "AI may work, but let's see if it does." This illustrates their openness to embrace the R&D nature of AI adoption.

There is no outline, no template, no boilerplate to go with as of yet. Maybe in three years there is going to be a collection of common knowledge on how to apply AI in manufacturing or agriculture, but it is too early for that. For now, each method of adopting LLMs to a company's core processes needs to be tested in a trial-and-error approach.

Also, the way those processes will be affected and modified stays unclear, so two degrees of freedom are required: in both tech and in business. Only those decision-makers who could understand this succeeded, and had their projects bringing value after just 6-8 months, with their AI systems blossoming in less than a year.

Recognition that we are all tinkerers #

"I have a detailed spec for you, and I need your engineers to implement it."

We often hear from prospects. And such a sentence lights up red alarm lights in us. The decision-maker or his team cannot specify what they need, simply because the skillset required for that is still so far a rarity. It is mid 2026, and I'm saying that with confidence, we have never seen a company that would be versed in LLMs to define the specs themselves, and if such exists, they are not out there seeking external providers but working on solutions themselves.

What typically happens is that we meet a team of tinkerers who fiddle with LLM technology and do have a pretty good idea of what can be done, which unfortunately is still insufficient for building a productive-grade system. Acknowledging that, they seek external help, but the decision maker, armed with insights from his tinkerers, falls into the Dunning-Kruger effect of being overconfident about their understanding of AI.

It is like coming to a doctor with a self-diagnosis based on Reddit. Please don't. Experts like us appreciate tinkerers. It is a great start for a company to have a solid understanding of what LLMs and AI agents can or cannot do within the team. It is not a shame to hit a roadblock and look for a helping hand. But it is good to acknowledge the limit of your understanding and open up to exploring how to bypass the roadblocks. All successful C-level decision-makers we have worked with have been able to do so.

The openness to pivot #

We have seen projects start as a simple cost-cutting exercise and end up as full business augmentation. Only those decision-makers who are open to thinking outside of the box (or the scope, in this case) win big. It is so because the measurable increments in AI functionality shed light on new opportunities. And the more are kindled, the better lit is the runway to new heights. The best decision-makers used the trial-and-error approach, not to insist on a single predefined target, but to readjust the direction of the project as they went.

We have customers who have made very successful M&A's using AI as a catalyst. Others pivoted their business, tapping into new scaling possibilities. LLMs and Agentic AI is no longer a curiosity and a treat; they are more a tool that has found a way to be harnessed and used.

The lesson we take from this is the following: the C-level decisionmaker needs to be part of the engagement. Even if he or she is initially leading from the back, their involvement is required as AI adoption will present options that require bold decisions.

Engineers at heart #

The trick with LLMs and Agentic AI in business is that it is not enough, even if they seem to work reliably. The stochastic nature of these solutions requires a shift in our thinking about computer-related paradigms. We are not used to that. After decades of having predictable computing, where once something is up and running it does not usually break by itself, we are now dealing with systems where the same input can yield different outputs.

Testing and working with them is a matter of statistics which decision-makers grapple with. The best ones we have worked with embrace the engineering attitude. They do not want you to convince them, they want a proof. Statistical proof, at best. They look for validation in numbers, metrics, and constantly define and redefine them as the project goes.

This is very much in line with how AI engineers need to work and think of progress. After all, the Engineer needs to know if a single change in prompting or tooling gives an anticipated improvement, which is not so obvious in the world of AI as it is in the world of strict logic and rigid rulesets.

Conclusion #

The decision makers who are successful with Agentic AI and LLMs do not resemble corporate managers or typical C-suite executives. They are curious pathfinders. They are outliers passionate about spotting new paths. They seek growth but know that it comes with risk and costs. They are adventurers. Excited with the new tools they use for the climb, but down-to-earth, reasonable about the risks of using them.