Opinion: Tech enables transformation, people achieve it

Daire Cunningham explains the importance of the human element in digital transformation, particularly with AI adoption. While AI has become widely accessible, only a third of organizations have successfully scaled their AI programs. Access to AI alone is no longer the main barrier; the real challenge lies in understanding where AI can create value and what changes are needed within the business.

Organizations often start by asking which AI tool to use, but a better approach is to identify the specific problem they aim to solve. Once that is clear, leaders can work backwards, determining the required information, where it resides, who should have access to it, and where existing processes create bottlenecks. They can then decide which decisions should remain human-driven and where technology can improve outcomes.

The primary obstacle to AI transformation is not a lack of sophisticated technology, but the current state of the organization. A 2026 Dun & Bradstreet survey revealed that while most businesses report some financial return from AI, only 6% have fully prepared their enterprise data. Accumulated information across various systems and evolving permissions have created complexities that AI can both exacerbate and reveal.

AI assistants connected to internal information can unintentionally expose unauthorized access, highlighting the importance of proper identity, permissions, and governance. McKinsey found that redesigning workflows is strongly associated with financial impact from generative AI, emphasizing the need for organizations to rethink how work gets done.

While AI can accelerate coding, testing, and analysis, it does not replace the need for professional discipline. Engineers must still review generated code, test systems, and ensure security requirements are met. This human oversight becomes even more critical as systems become more capable.

A practical example is the music industry, where AI-driven metadata analysis helps identify fraudulent registrations, reducing processing times by 70%. The value lies not only in efficiency but also in allowing skilled professionals to focus on cases requiring investigation and expertise.

Ultimately, the strongest applications of AI are those that address specific problems, rather than being an end in themselves. The value of AI should be measured beyond automation and efficiency, considering how it enables professionals to focus on high-value tasks and make informed decisions.