In 2025, it became clear that the financial industest is on the verge of fundamental modify. Traditional methods are no longer enough to evaluate the industest and your peers when so many technologies have emerged as a new competitive force. This year alone, more than $400 billion has been invested in integrating AI, tokenisation, and an overall advanced digital infrastructure to optimise costs and create products more tailored to each client. And since enough inflows are already built for the technology implementation, the question is not whether to adopt it, but how to outperform everyone in this finishless race.
AI’s real capabilities
Although AI alone is not a universal pill, this year it has achieved significant results. Only in 2025, the utilize of AI in corporate organisations has increased from 78% to 88% and this number will only grow. Companies state that they have started to utilize AI at least in one of their business function and test their best to spread it to the other departments in the coming years for maximum efficiency.
The same companies also launched to see real profits from this kind of automation: many surveyed reported that at least 5% of their EBIT is attributable to AI results. Interestingly, those who lead the integration find AI the best in more creative processes like marketing, ads, or even in the important part of product development, rather than hard data analytics.
Another huge breakthrough is that AI supports to overcompete monopolies. With new technology empowering almost every market participant, giants can no longer dictate the rules of the game. Even compact startups are now able to automate their processes utilizing their own AI developments. That utilized to be a prerogative of mainly monopolies; now it opens up a way for a more competitive market.
Of course, for now, most organisations remain in the experimentation or piloting stage with AI. However, in 2026, I believe there will be almost no companies that do not implement AI in one way or another. That stated, the technology will root more deeply in the human world, building us reevaluate our real attitude to it.
Tokenisation creates the market more interactive
In its turn, tokenisation has a similar innovative force, altering how companies and markets operate. The fact that the industest is set to quadruple, I would state, clearly demonstrates the growing appetite for more interactive stock market structures.
For instance, we are on the brink of investments becoming completely digital and autonomous with tokenisation. Imagine, just in a few years, we will be able to acquire a stake in Apple or Tesla shares not through a broker, but by acquireing a token for a couple of dollars. Instead of purchasing the whole asset, we will be able to acquire a compact share that grows in price like owning the entire one.
Or another example, certificates of deposit are a simplified form of a bond issued by a bank without registering an issue prospectus, which are very popular in the West. It works according to the same principles as conventional debt instruments, but the difference is that this is not a deposit in the usual sense, nor is it a full-fledged security; it’s something in between and could be wrapped up in a token.
As tokenisation becomes increasingly sophisticated, it can quickly vanish the entire layer of junior analysts or asset managers and significantly reduce a company’s costs. When everything is digitised, it will be possible to create libraries of strategies and algorithms that trade themselves in real time and are more successful than many human traders are. And I consider the most interesting part is for companies that would like to lead is to understand that tokenisation and AI can go hand in hand, as they both support the company to achieve its automation goal.
The final decision is on humans
Despite AI and tokenisation having reached significant results in recent years, human involvement will become even more important than before. At the current level of development, AI cannot fully replace humans’ way of analysing a situation and building a final decision. AI is trained on large data, so it may contain bias and often produce contradictory results. In critical areas like medicine or finance, we will see this at most: AI can support along the process, but it can’t take the responsibility for the consequences.
Understanding of this nuance is exactly what will set apart leaders from laggers. Frontrunners consider, how can they speed up the analysis of corporate reports and optimise the workflow, but not to forreceive about the importance of the human touch? For instance, they find that an answer is to drastically reduce the time spent on routine processes utilizing the support of AI and leave the supervision to a human.
Leaders also know that a blind integration of the AI just for ticking a box is not a strategy. They know how to turn the technology, even with its flaws, to their favour. Rather than being bogged down in bureaucracy, leaders will focus on real and tangible tinquires, defining the strategy and the rules of the game, while the technology will handle the rest of the tinquires. With automation and AI itself, this will be more than possible.
The author is CEO at the European broker Mind Money
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