A lack of a systematic approach and clear strategic goals are among the main reasons Russian companies struggle to implement artificial intelligence (AI), according to Dmitry Sytsko, CIO of BCS Bank.
There is a growing tendency in the market to adopt AI simply for its own sake, driven largely by the rapid push toward digitalization. This approach is both common and misguided: the desire to “have artificial intelligence” shapes decisions, yet there is often no clear understanding of which problems AI should solve or what value it is expected to deliver. As a result, AI turns into a costly and opaque project that generates no tangible benefits, the expert notes.
Another major mistake is overestimating a company’s readiness to implement AI. Many organizations overlook critical factors such as the level of process automation, data quality, and the coherence of their internal infrastructure. When some processes run in one system, others through email, and still others via messaging apps, collecting and structuring data becomes nearly impossible. Under such conditions, effective deployment and model training cannot take place. Even large companies do not always have access to complete and consistent datasets, which degrades model performance and drives up implementation costs, posing a significant limitation.
Another serious mistake is the absence of a technology strategy, Dmitry Sytsko continues. Companies often fail to decide whether they should develop their own AI models, rely on off-the-shelf solutions, or adopt external platforms. Without clarity on this choice, it is impossible to build a sustainable AI system or allocate resources effectively. The result is fragmented experimentation, duplicated functionality, and excessive costs.
According to Sytsko, human factors frequently become decisive. During AI implementation, companies often neglect employee engagement and training, failing to involve staff in the process. Without internal buy-in, employees grow wary of new technologies, fears emerge, and resistance to change increases. In such an environment, even well-designed solutions lose effectiveness, while implementation requires far more time and resources.
“The mistakes made in implementing artificial intelligence show that the success of such projects depends not only on technology, but also on effective management, infrastructure readiness, and employee engagement. Only by addressing these issues can AI become a genuine driver of growth and competitive advantage, rather than just another fashionable but ultimately ineffective initiative,” the expert concludes.