Artificial intelligence defined with new electricity. CEOs want to implement, but they can't

Ashley Davis17/05/2025

0 220 4 minutes read

The source of frustration are also two important restrictions indicated by the tested CEOs: no sufficiently large staff with AI competences and difficulty in measuring the business value of implementation.

The competence gap is not limited to operational managers – it also applies to supervisory boards themselves. The global Deloitte survey among almost five hundred directors and board members showed that AI has not yet been in the agenda of almost half of the advice (45 percent), and 79 percent. respondents admitted that it only has residual knowledge of this technology. Only 2 percent She described herself as experts. The lack of a systematic debate at the top later translates into chaotic investment decisions and bypassing regulatory or ethical risks.

Meanwhile, the scale of planned investments is growing. According to the January report, McKinsey as much as 92 percent companies intending In three years, increase the expenditure on AIalthough only 1 percent Leaders recognize his organizations as mature enough in terms of implementation. Effect? Half of the projects stop at the pilot stage, and only 19 percent. Board members today see an increase in revenues exceeding 5 percent. Thanks AI. Leaders also indicate a deficiency of talents (46 percent) and a unclear return on investment as a barrier to scaling of solutions.

Check also: Shorter full -time, the same performance. And will help companies balance a six -hour working day

Artificial intelligence difficult to tame

Uncertainty is also reflected in the annual PwC study. At least a third of presidents already observe an increase in revenues and margins thanks to generative AI, only 33 percent. He intends to include this technology in the strategy of the development of his staff competences.

This means that Even companies that have noticed AI's business potential rarely translate it into a plan for systematic education of employees and boards. It is so often for a simple reason: boards do not quite understand what exactly to do, how to measure the effects, and how much it should cost.

These challenges overlap the accelerated changes in the labor market. According to the Future of Jobs 2025 report, 170 million new jobs will be created until the end of the decade, but 92 million will be liquidated, and 39 percent. Key skills will be exchanged for others. Competences in the field of AI and data analysis were at the top of the most desirable listand employers declare more and more investments in the reskilling of their employees.

Why is AI so important and should be important for business? First, the scale of potential revenues. McKinsey estimates the upper ceiling of an additional global value at $ 4.4 trillion. annually. Second: competitive risk. Companies that do not learn to quickly use advanced analytics and generative models can be pushed into the defensive by aggressive, innovative rivals. Third, there is also regulatory pressure. EU AI ACT and growing requirements in the area of data ethics mean that the lack of technological competence at the top also means legal and reputational risk.

Read also: Each of us will be the head of AI employees. Change comparable to the arrival of the Internet

Management boards can adapt

So how can boards catch up? Gartner and Deloitte pay attention to a triple approach. The first step is Introduction of permanent AI meeting points – not occasional presentations, but cyclical sessions in which not only CIO participate, but also the financial director and head of risk.

The second element becomes Intensive training program: From practical workshops with generative models, through decision simulations, to short postgraduate studies prepared jointly with technical universities. More and more advice – 8 percent in Deloitte – it supplements the composition with independent AI experts, which accelerates the process of learning through practice.

The third pillar is a partnership with the ecosystem. Common innovation centers with cloud suppliers, programs for new talents in cooperation with universities and Corporate Venture Capital funds allow faster Build a portfolio of projects and acquire specialists who are missing on the labor market.

In practice, AI competences in supervisory boards cover several ranges. The first is understanding of technology – at least enough to distinguish hype from real business scenarios. The second is the ability to assess the risk and order of data, i.e. awareness of how algorithms affect cybersecurity, lawfulness and reputation. The third concerns the ability to quantify value – Directors must learn how to ask about the profitability threshold, the cost of learning models and impact on EBITDA. Finally, the fourth area is ethics and sustainable development, because more and more often investors expect that artificial intelligence will be used with respect for human rights and goals.