Tech Explained: Enterprise AI’s biggest risk isn’t the model — it’s the data in Simple Terms

The rapid rise of AI agents reveals a hard truth: without strong data foundations, scale creates risk rather than advantage.

AI agents are rapidly moving from experimentation to execution. Across industries, organizations are deploying agents to answer questions, summarize information and support real-time decision-making. While the underlying technology continues to advance at speed, the data foundations supporting these systems are often struggling to keep up.

Too many AI agents still rely on fragmented, outdated or incomplete data. As these systems become embedded in core business processes, that weakness becomes more than a technical limitation, it becomes an enterprise risk. At scale, the performance and trustworthiness of AI agents depend less on model sophistication and more on the strength, reliability and governance of the data infrastructure beneath them.

Recent research from Boomi underscores this disconnect. In a survey of 300 global data and analytics leaders, 77 percent cited trust in their AI systems, yet fewer than half trust the completeness of their organizational data. That gap represents both a vulnerability and an opportunity. As AI agents scale across enterprises, data quality, data context and access will determine who wins in this next phase of agentic transformation.

AI agents operate entirely on the data they can access. When that data is accurate, consistent and well connected, agents can generate meaningful insights and support better decisions. When it is not, they can produce outputs that appear authoritative but are fundamentally unreliable, a dangerous combination in enterprise environments.

For years, manual data management approaches were sufficient, even if not always efficient. Teams could reconcile systems, correct errors and maintain a baseline level of trust. But that model does not scale. As AI agents span marketing, finance, operations, supply chains and customer experience, the volume and velocity of data exceed what manual oversight can realistically sustain.

As one data leader noted in Boomi’s research, without automated quality controls, lineage and appropriate human oversight, organizations lose visibility into where data originates and whether it can be trusted. At enterprise scale, that uncertainty directly undermines the reliability of AI-driven decisions.

AI is no longer confined to isolated pilots or innovation teams. It is increasingly embedded in live workflows, customer interactions and decision-making processes. Each new deployment raises the stakes for data accuracy, transparency and explainability.

At the same time, regulatory scrutiny is increasing, customers are demanding greater accountability and the pace of AI innovation leaves little room for retrospective fixes. Boomi’s research shows that 83 percent of organizations plan to integrate additional data sources for AI in the next year, yet fewer than half consider their data automation capabilities mature. That gap will determine who can scale AI responsibly, and who will struggle to do so.

Enterprise-grade AI agents require more than access to data; they require dependable foundations.

That starts with connection. Agents need unified access to data across the business, from customer and product systems to finance, HR and operations, so they can operate with full context rather than partial views.

Quality is equally critical. Data must be accurate, consistent and continuously maintained as it moves across systems and as agents generate new outputs. This cannot be addressed through periodic clean-up efforts; it requires automation built into the data lifecycle.

Governance is what transforms data infrastructure into a trusted enterprise asset. Clear ownership, lineage and explainability ensure organizations understand how data is used, how decisions are made and where accountability resides, even as AI systems operate at speed and scale.

Building strong data foundations is not simply a technology initiative; it is a leadership responsibility. Business leaders must set clear standards for how data is managed, shared and governed across the organization. That begins with assessing existing data pipelines, identifying structural weaknesses and investing in automation that enforces consistency at scale.

It also requires a cultural shift. Data quality cannot sit solely with IT. Every team that creates, uses or depends on data plays a role in shaping the effectiveness and reliability of AI. Treating data as a shared enterprise asset is essential to sustaining trust as AI adoption accelerates.

Organizations have invested heavily in advancing AI capabilities. The next phase of progress will be defined by the strength of the foundations beneath them.

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

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