Artificial intelligence (AI) is becoming an increasingly prominent topic among business leaders, yet many organizations still struggle to integrate the technology effectively and quantify the business value it creates. According to EY, long-term successful AI transformation rests on three main areas: developing employee skills, managing data properly, and strengthening cybersecurity.
Introducing AI technology alone is not enough to unlock its broader business potential. EY experts note that AI adoption often focuses on deploying new applications — for example chatbots, e‑mail assistants or document-generation tools — while overlooking the organizational and operational conditions required for responsible, effective use.
"A significant portion of domestic companies still treat artificial intelligence primarily as an IT project, whereas its implementation is at least as much a human and organizational challenge. AI can support not only IT staff but employees across the organization. To generate real business value, competencies are needed at every level so employees can recognise the technology's possibilities and understand its limitations," said Erik Slooten, EY partner leading AI services.
Data management: a prerequisite
EY experts consider proper data management one of the most important preconditions for successful AI initiatives. While corporate attention often centres on new models and applications, the reliability and performance of solutions built on them depend fundamentally on the quality, organization and accessibility of the underlying data. Many companies hold large volumes of data, but those data frequently reside in isolated systems and in disparate formats. Without appropriate data structure and governance, AI cannot deliver consistent and trustworthy results.
"Companies possess significant amounts of data, yet these are often stored in separate systems and various formats. Without adequate structure and data governance, artificial intelligence cannot provide consistent and reliable outcomes. In many cases, the best investment is not developing a new AI application, but organising, standardising and consciously managing existing data," added Erik Slooten.
Cybersecurity and risks of generative AI
As generative AI spreads, security risks become increasingly important. Companies must leverage new technologies while protecting sensitive information and IT systems. EY stresses that governance of generative AI, data protection, access controls and cybersecurity are now closely linked areas.
"Artificial intelligence offers significant development opportunities for business operations, but it also creates new risks. Regulation of generative AI use, data protection, access controls and cybersecurity are now tightly connected areas. Security therefore cannot be treated as a task separate from AI strategy: it must be an integral part of it and receive at least as much attention as innovation," Erik Slooten emphasised.
Which organizations will lead?
EY's experience indicates organizations that treat AI not as a standalone IT project but as part of a broader business and organizational transformation are best positioned to lead. Preparing employees, consciously managing data and reinforcing cybersecurity together lay the foundation for an effective, sustainable AI transformation that delivers long-term value.
Source: Magnific
