Operation visibility and understanding: The shared foundation of AI trust and performance

The ability to trace AI decisions is far more than a compliance concern, it is also the root of Enterprise AI success. To keep AI systems optimized and aligned to evolving needs, it's essential to understand and update the data foundation and business rules that enable the platforms.

Increasingly this makes AI governance and performance a function of its ability to see and understand an organization's operational reality. After all, you can neither fix nor govern what you can't see and do not understand. When an AI agent operates across fragmented enterprise systems, a flawed credit decision, a supply chain disruption, or a procurement error cannot be properly understood. Not without visibility into and understanding of the full chain of execution that led to that outcome. The question regulators and businesses will increasingly need to answer is not just 'what model was used?' but 'why did this decision happen, and can we demonstrate appropriate oversight?'.

However, operational visibility and understanding are often obscured by fragmented legacy tech stacks, disconnected data platforms, low data quality, and siloed functional processes. For example, 87% of respondents to PwC's2026 Digital Trends in Operations Survey say poor data quality has impacted their organization's ability to achieve value for digital initiatives. Similarly, 42% cite siloed organizational structures and processes as among the biggest barriers to achieving the horizontal, networked operating model needed for cross-functional insight and end-to-end visibility.

Addressing these issues starts with generating an accurate, real-time picture of the enterprise as one connected, dynamic system rather than patchwork of disconnected parts.