The "AI" label no longer sets companies apart

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The “AI” label no longer makes companies special. In today’s world, that is a fact that needs to be accepted.

Even just a year ago, if a business simply mentioned being “AI-powered” it was enough to immediately gather attention and appear innovative. Investors quickly grew excited, and media outlets were quick to pick up the next “hot story.”

Of course, that didn’t always mean that there was truth to such statements. The concept of AI washing — when companies misrepresented or even outright lied about the use of AI tools in their operations — had certainly done its share of damage to this industry while it stayed prevalent.

But today, even without such lies, the effect of AI novelty is disappearing. Every fintech company now has some kind of AI story. Compliance platforms use AI monitoring. Banks and fintechs talk about AI-driven service personalization. Chatbots and AI assistants are utilized practically by every other platform, no matter which industry we look at.

At this point, saying your company uses artificial intelligence feels almost the same as saying you have a website . People have learned to expect it almost by default. And this creates a very different communications environment.

The first wave of AI companies mostly had to convince their audience that the technology was possible. That task has certainly been accomplished. Now, the second wave faces the task of convincing people that their particular implementation of AI is trustworthy, useful, and worth the attention in a market that’s beyond overcrowded already.

That is much harder.

The conversation has changed

AI investment has exploded over the last two years. Enterprise spending on gen AI jumped from $11.5 billion in 2024 to $37 billion in 2025. That’s a whole 220% YOY increase. AI-focused startups continue to dominate venture capital conversations, to the point where nearly every technology company now feels the pressure to position itself as part of this race.

But something else happened along the way: consumers became more educated. If at first, AI sounded like something magical, now people have actually used it. They have seen hallucinations and strange outputs, and they’ve dealt with incorrect recommendations. They’ve had time to realize that AI can be useful, but also that the technology comes with its own frustrations and limitations.

As such, being “AI-powered” is no longer enough to stand out. Consumers have already moved past that stage. One of the biggest mistakes I see today is when companies still assume they have not.

Now, the conversation revolves around a much deeper point: “What does your AI actually improve for me?” For AI-focused companies vying for attention, the benefits of their specific approach are now the key battlefield where they have to win.

This is particularly important for financial and fintech companies, because financial services are built on trust. People may tolerate or even find humor in mistakes from an entertainment app, but they will not be pleased when their money or personal data are put at risk because of AI malfunctions.

The numbers clearly show this trust gap. While AI adoption continues growing, only 13% of consumers truly trust AI systems, and about 30% remain neutral rather than confident. Many people are still uncomfortable giving AI fully autonomous control over important decisions, especially in areas connected to finance or personal data .

At the same time, enterprises themselves also often contribute to the problem of trust by rolling out AI models before building the necessary structures to govern them.

A recent survey by McKinsey discovered that less than 15% of organizations obtain full security and IT approval before deploying AI agents. Or, to put it in other words, the vast majority of these systems come out without proper oversight and responsibility frameworks around them.

This very reality has now become the defining challenge of the next AI adoption phase. The market is no longer asking whether companies can use artificial intelligence. It’s asking whether they can use it responsibly. And that is precisely why I am of the opinion that AI communication from here on out needs to be very different from what we saw during the first hype cycle. It’s no longer about who shouts the loudest.

The main victory condition will be for companies to explain — clearly and realistically — how their systems work and how they benefit their users. And then they will have to continue proving that through sustainable action.

Transparency and governance are now a product feature

One important area to account for is transparency around AI limitations. Now that the broad public already has enough context and experience to understand that AI can fail, pretending otherwise would only damage a company’s credibility.

To earn trust, strong, honest communication is necessary that will openly acknowledge those points of failure. When a business wishes to present its own AI solution, it will need to explain upfront where its model works well and where its shortcomings lie. Customers should have clear expectations and understanding of the risks before they engage with the technology.

Some might think that admitting to shortcomings would be a mistake, a vulnerability. But honesty can yield more results than those people expect.

Clients are generally much more comfortable using services when they understand where the borders are. When they know how decisions are made, that safeguards exist, and that human oversight is still involved in the process, so if something goes wrong, they have someone real to talk to.

This is where competent governance enters the picture.

Be it to partners, clients, or regulators, businesses increasingly need to demonstrate that they are responsible about how they employ AI in operations. Whether decisions can be audited.

A few years ago, these topics were often hidden deep inside technical documentation, if they were brought up at all. Now, explainability of AI models is one of the biggest signals of trustworthiness that a company can project.

And honestly, I think this is healthy for the industry.

By now, AI has stopped being a mere marketing decoration and is leaning more and more toward becoming a full-on infrastructure layer in corporate operations. That means it needs to be properly managed, so that possible mistakes do not create reputational and monetary damage to countless involved parties.

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