The biggest risk in enterprise AI is the data, not the models

The rapid rise of AI agents reveals the hard truth that without a strong data foundation, scale creates risks rather than benefits.

AI agents are rapidly moving from experimentation to execution. Organizations across industries are deploying agents to answer questions, summarize information, and support real-time decision-making. While the underlying technology continues to advance rapidly, the data infrastructure supporting these systems often struggles to keep up.

Too many AI agents still rely on fragmented, outdated, or incomplete data. Once these systems are integrated into core business processes, their weaknesses move beyond technical limitations and become enterprise risks. At scale, the performance and reliability of an AI agent depends more on the strength, reliability, and governance of the underlying data infrastructure than on the sophistication of the model.

Recent research by Boomi highlights this disconnect. In a survey of 300 global data and analytics leaders, 77 percent cited trust in their company’s AI systems, but less than half said they trusted the integrity of their organization’s data. This gap is both a vulnerability and an opportunity. As AI agents scale up across the enterprise, data quality, data context, and access will determine who wins in the next stage of agent transformation.

AI agents only work with data that they have access to. When data is accurate, consistent, and well-connected, agents can generate meaningful insights and support better decision-making. Otherwise, output that appears authoritative but is fundamentally untrustworthy can be produced, a dangerous combination in an enterprise environment.

For years, manual data management approaches were sufficient, even if they weren’t always efficient. Teams can tune systems, fix errors, and maintain a baseline level of trust. But that model is not scalable. As AI agents span marketing, finance, operations, supply chain, and customer experience, the volume and velocity of data exceeds what manual oversight can realistically maintain.

As one of the data leaders in Bhumi’s research pointed out, without automated quality control, lineage, and proper human oversight, organizations lose visibility into where their data is coming from and whether it can be trusted. At enterprise scale, that uncertainty directly undermines the trustworthiness of AI-driven decision-making.

AI is no longer limited to isolated pilots or innovation teams. It is increasingly integrated into live workflows, customer interactions, and decision-making processes. Each new implementation increases data accuracy, transparency, and explainability.

At the same time, regulatory oversight is increasing, customers are demanding greater accountability, and the pace of AI innovation leaves little room for retrospective correction. According to Boomi research, 83% of organizations plan to integrate additional data sources for AI in the next year, but less than half believe their data automation capabilities are mature. This gap will determine who can responsibly scale AI and who will struggle with it.

Enterprise-grade AI agents need more than just access to data. You need a reliable foundation.

It starts with connection. Agents need unified access to data across the business, from customer and product systems to finance, human resources, and operations, so they can work with complete context rather than partial views.

Quality is equally important. Data must be accurate, consistent, and continuously maintained as it moves between systems and as agents generate new output. This cannot be addressed with regular cleaning activities. Automation must be built into the data lifecycle.

Governance is what turns data infrastructure into a trusted corporate asset. Clear ownership, lineage, and explainability ensure that organizations understand how data is used, how decisions are made, and where accountability lies, even when AI systems operate at high speed and scale.

Building a strong data foundation is more than just a technology endeavor. That is the responsibility of the leader. Business leaders must set clear standards for how data is managed, shared, and governed across the organization. It starts with assessing your existing data pipeline, identifying structural weaknesses, and investing in automation to enforce consistency at scale.

A cultural change is also needed. Data quality isn’t just the responsibility of IT departments. Every team that creates, uses, or relies on data plays a role in shaping the effectiveness and trustworthiness of AI. As AI adoption accelerates, treating data as a shared enterprise asset is essential to maintaining trust.

Organizations have invested heavily in advancing AI capabilities. The next stage of progress is determined by the strength of the underlying foundation.

Enterprises that prioritize a connected and well-managed data infrastructure will move beyond reactive automation to proactive intelligence, enabling AI agents to operate with greater precision, resilience, and real business impact.

In the era of enterprise AI, success is built from the ground up.