In 2026, organizations across industries are moving beyond generative AI and exploring the next frontier: Agentic AI. Much of the conversations have centered around what these agents can do: reason, plan, and act. But far less attention has been paid to what they need to succeed.
For years, organizations have prioritized data volume, investing in new tools, building new dashboards, and collecting more information under the assumption that more data would naturally create more value. The reality is often the opposite. Many are experiencing the illusion of data abundance while drowning in fragmented, low-quality data. According to Gartner, poor data quality costs organizations an average of $12.9 million annually through wasted resources and missed opportunities.
Autonomous systems operate on the same data humans rely on, only at machine speed and scale. If poor data is already slowing decision-making, creating inefficiencies, and eroding trust among employees, AI agents will only accelerate those outcomes. Rather than unlocking productivity, organizations risk scaling poor decisions, introducing new operational risks, and limiting the return on their AI investments.
Therefore, the future of successful AI won't be determined by who deploys agents first. It will be determined by who builds the data foundation that enables those agents to reason, learn, and create meaningful business value.
The costs of poor data quality can go beyond inconsistent reporting and dashboards; it directly impacts productivity, decision-making, and ultimately, business performance.
According to a McKinsey report, knowledge workers spend 30–40% of their time searching for data when there isn't a clear data inventory of available information, while another 20–30% of their time is spent cleansing data because quality controls are weak.
The difference is that human workers can often recognize when data is incomplete, question inconsistencies, and apply business judgment to fill in the gaps. AI agents, however, depend on the quality of the data they're given. They can't reliably reason over fragmented, poorly governed, or low-quality data. Without the right foundation, agents are more likely to generate inaccurate outputs, hallucinate by confidently presenting false or unsupported information, making poor decisions, and introducing unnecessary operational or security risks.
The goal isn't to let autonomous agents inherit the same data challenges humans have struggled with for years. It's to build the right foundation first, one where data is trusted, well-governed, and enriched with the context AI agents need to reason, act, and continuously learn.
Traditional analytics answered a simple question: What happened? But Agentic AI requires much more than that.
If AI agents are only as good as the data they’re given, then organizations need to rethink their data products. The traditional data products that supported reporting and analytics in the past are no longer enough. In this era, we need agent-ready data products designed to support reasoning, decision making, and autonomous action.
During one of the latest AWS Summits, Executive in Residence Tom Godden emphasized an important shift: data products need to be treated as purpose-built agent-ready assets with clear ownership, rich metadata, and governance built in. You must shift your view of data products from a collection of information to the essential foundation for building reliable autonomous AI agents.
This shift sounds simple, but significant for business outcomes. Agentic AI doesn't just need to know what happened; it needs to understand why it happened, how information is connected, and what context should influence its next decision. According to Tom Godden, that foundation is built on three essential capabilities:
Together, these capabilities transform data from a static repository of information into a strategic asset that enables AI agents to reason, learn, and make better decisions over time.
While Agentic AI can automate reasoning and execution, it still can’t replace human judgment. It makes the role of humans in the loop more crucial.
Stephen Brozobich, Executive in Residence at AWS, remarked in his AWS Summit session “...the valuable human in the loop is the polymath with steering hands.” This is a powerful reminder to everyone that successful AI initiatives require people who can connect business strategy, data, and technology.
These individuals provide what AI cannot: judgment, context, prioritization, ethical oversight, and business intuition. They understand not only how the technology works, but also the business outcomes it's intended to achieve. Their role transforms from performing every task to guiding intelligent systems, validating decisions when needed, and continuously improving the data and processes that agents rely on.
Therefore, in the agentic era, the most valuable human isn't the one competing with AI, it's the one steering it.
Before deploying AI agents, start by identifying the problem you’re trying to solve. Then determine what data will help solve that problem, ensure that data is trusted, well-governed, and rich with context, and treat it as a purpose-built product rather than a byproduct of operations.
As Tom Godden puts it: identify the business problem, determine what data will help you solve it, get that data right, then lather, rinse, and repeat. Creating value with Agentic AI isn't a one-time implementation; it's a continuous cycle of improving data, refining context, and enabling better decisions over time.
The organizations that succeed with Agentic AI won't necessarily be the first to deploy autonomous agents. They'll be the ones that build the data foundation that allows those agents to reason, learn, and create meaningful business value.
Whether you're just beginning your AI journey or looking to improve existing initiatives, the right data foundation makes all the difference. Connect with our team to assess your data readiness and discover how purpose-built, agent-ready data products can help you unlock real business value from AI.