'Move fast, but do it with trust built in': EY CIO tells us why the rapid pace of AI means trust is now a critical business imperative

The rapid advancement of AI technology has brought about significant changes in the way companies operate, and it is also influencing their strategic moves. AI has transformed the risk profile of innovation, as it can expose companies and users to risks earlier, faster, and at a larger scale. Previously, businesses could afford to delay digital transformation decisions, but now competitors can leverage AI to accelerate their processes while companies waiting for absolute certainty may fall behind before even starting.

This underscores the critical importance of trust as a business imperative, and organizations must enhance governance, privacy, and security to mitigate regulatory and reputational risks. An interview with Joe Depa, EY Global CIO, sheds light on the reasons behind this shift and the factors that distinguish companies that successfully realize business value from those that remain in "pilot purgatory."

Many businesses have experimented with AI in the past two years, but only a few have achieved measurable business impact. The key to success lies in moving faster with trust, which involves understanding and optimizing the AI value equation. Companies that achieve tangible ROI on AI typically have trusted data as the foundation.

This refers to proprietary, secure, and well-governed data that reflects the unique knowledge, judgment, and expertise of the business. On top of this foundation, they have trusted processes and technology that connect data to real workflows, decisions, and business outcomes. Rather than using AI to merely speed up existing processes, successful companies rethink how work gets done, identify new value creation opportunities for AI, and build governance into the way AI is deployed and scaled.

A crucial aspect of scaling AI adoption is investing in upskilling employees to build their confidence in using AI. When people trust the data, process, technology, and their own ability to use AI effectively, they are more likely to adopt it confidently, thereby turning passive users into change agents. This upskilling is essential as it transforms employees from passive users into proactive contributors to AI-driven growth.

As highlighted, AI value realization is not solely about minimizing token costs but rather focusing on measuring business outcomes. The focus should shift from merely measuring usage to measuring the tangible business impact of AI initiatives. EY has developed best practices to optimize model selection against the highest-value use cases, train teams, and implement governance around AI usage.

This approach has resulted in reducing overall token consumption by 60% while increasing business value. However, balancing the pace of AI adoption with the need for robust governance remains a challenge. The tension between rapid innovation and the necessity of implementing proper controls is significant. Companies that succeed in this balance establish trust in AI from the outset, enabling their teams to innovate without the fear of unwarranted risks that need to be addressed later.

EY's Responsible AI Pulse survey indicates that companies that prioritize responsible AI practices report gains in innovation and revenue growth. Moreover, real-time monitoring enhances revenue growth and cost savings. Building effective governance requires integrating controls into the system early on to avoid technical debt and ensure scalability.

As AI agents begin to execute actions, governance must be embedded within the system itself, covering access control, behavior monitoring, bias checking, and safeguards for AI actions. In summary, the key to harnessing AI's potential lies in moving forward with confidence built on strong governance, trusted data, optimized processes, and well-equipped employees, thereby achieving measurable business outcomes and mitigating associated risks.