Give an AI agent bad data, and it can make bad decisions at machine speed.
AI agents are ushering in a new era of business automation, completing tasks, making decisions and taking action with far less human intervention. But their ability to deliver useful results depends on the quality, accessibility and governance of the data behind them.
Most businesses do not need more data. They are already drowning in it. The real challenge is turning fragmented, inconsistent and poorly governed information into data that AI agents can understand and use safely.
Gartner has predicted that inadequate data management infrastructure will be responsible for 60 percent of AI project failures. With autonomous agents, the danger is even greater because errors can spread rapidly across workflows, customers and business systems.
Data readiness is therefore becoming central to successful agentic AI. Here are four practical steps businesses can take to prepare their data, reduce risk and turn AI agents into a genuine source of value.
A Different Strategy
Traditional business approaches to data management have revolved around the fact that information is used solely by humans to make decisions.
This meant that businesses stored and processed information in ways that made it useful to us. As long as employees and managers could get at what they want, when they need it, that’s all that mattered.
This isn’t true anymore. Agents promise huge speed and efficiency gains, but to achieve them, our data needs to be useful to them too.
This means it can’t be kept in proprietary, disconnected tools and platforms, outdated spreadsheets or isolated departmental systems.
And that’s just the tip of the iceberg. Working with agents involves considering issues like privacy, compliance and accountability in a new way. Failing to understand how agentic AI exposes businesses to new risks here would be just as serious a mistake as ignoring its opportunities.
So where do we start?
Four Steps To Agent Readiness
The good news is that the most important steps don’t necessarily involve expensive overhauls of your data technology stack. Your first moves are more about making small changes to processes, culture and governance to get onto the right track.
Start by understanding what data you have and where it is. As mentioned, it’s normal for companies to have disparate platforms and cloud services sprawled across different departments, often with little understanding passed between them of how they work. Clearly mapping data assets and creating an ongoing process for keeping the map up-to-date is a priority.
Once that’s done, you can start to establish clear governance frameworks around access, ownership and accountability. This means knowing who is responsible for data as well as which humans or agents can access it, what’s off-limits to agents, and where there’s need for human oversight.
We also need to think carefully about data quality. Clean, standardized, non-duplicated data is the fuel for business decision-making, and bad data means bad decisions. There are also critical differences in the ways that humans and agents spot and mitigate errors in data. Humans rely on intuition, context and experience, whereas agents spot patterns and analyze how data performs against predefined rules.
And then we need to consider how accessible our data is to agents. This means breaking down silos and building systems that allow data to pass through APIs and into whatever systems need it. It requires a jump in mindset, with a new focus on making data continuously discoverable and available to autonomous machines.
For businesses ready to scale AI from pilots into production, these changes, taken together, can be the building blocks of a basic agentic data strategy.
What’s Next?
Agents are moving beyond acting as standalone assistants towards becoming integrated, intelligent cogs in a connected, always-on ecosystem. The businesses that prepare their data and data strategy for this now will be best positioned to reap the benefits.
They’ll find it easier to identify potential use cases, achieve organization-wide buy-in, and scale successful projects to the point of generating meaningful results.
Critically, they’ll also be less likely to fall foul of governance or regulatory hurdles that can scupper projects and destroy trust.
As with many aspects of the AI revolution, this won’t solely come down to our skills at utilizing technology. It will also depend on our ability to manage new types of frictions that occur at the meeting-point of machine decision-making and human oversight.
