Agentive AI reshapes data use

Agentive AI systems are changing how companies use data by integrating directly into business processes. These systems not only analyze data but also make decisions or take actions based on it, making data quality a critical factor in their effectiveness.

Data quality becomes decisive

In agentive AI, incomplete, outdated, or misinterpreted data can lead to inappropriate actions. A single decision often requires data from multiple sources, which may have different formats, definitions, and update schedules. For example, processing a refund might require cross-referencing several data points that individually are accurate but collectively lack coherence.

Preparing data for agentive AI

Beyond making data accessible, companies must define its meaning, verify its validity, and establish its usage rules. A data point's quality depends on its intended use. What is sufficient for a monthly analysis may be inadequate for real-time decisions. Consistent definitions across systems are also necessary for data to be useful.

Governance and traceability

Effective governance requires linking data points to the rules that govern their use. Metadata provide essential details, such as definitions, sources, and update dates. Traceability helps track a data point's journey and identify how an error may have spread. These elements support checks when anomalies arise, allowing companies to trace and correct errors efficiently.

Shared data foundations

Addressing data quality on a per-project basis can lead to conflicting definitions and redundant controls. A unified foundation of well-documented, governed data allows teams to reuse this work. Shared definitions, sources, and rules make it easier to develop new applications and verify results.

Human oversight is insufficient

Even with human involvement, controlling AI agents' actions remains challenging. The rapid evolution of AI tools means that risks can quickly outpace human oversight. Some AI agents have more permissions than human employees, enabling them to perform tasks like deployments or data modifications automatically.

Risks of AI agents

AI agents can cause significant damage, such as deleting entire databases in seconds due to errors in data quality. This has already occurred, and the risks are growing as agents become more common. According to a McKinsey study, over 60% of organizations are using AI agents, many with extensive privileges that could lead to serious consequences.

Security strategies need revision

Traditional security approaches, like Zero Trust, are not designed for AI agents that operate at machine speed and can bypass existing governance systems. A more granular approach, integrating security directly into data through classification and masking, is needed. This allows rules to be applied before an agent can act.

Restoration processes must evolve

Current restoration strategies, which rely on human intervention or system-wide resets, are not suitable for agent-based AI. A change-based restoration approach is necessary, allowing precise correction of specific errors without disrupting the entire system. This requires mechanisms that track changes at the record level and consider dependencies.

Anticipating and managing errors

Companies must prepare for the inevitability of AI errors and implement precise restoration strategies to minimize damage. While human oversight remains essential, the focus should be on ensuring that corrections can be made with real granularity. This ensures that AI agents can be trusted to operate effectively without causing catastrophic consequences.