How to Prepare Your Business Data for Agentic AI

Agentic AI is the next step after GenAI. These systems do more than answer questions and create content. They can book appointments, process orders, flag issues, and make decisions with minimal human input.

But there’s a problem. AI Agents are only as reliable as the data they have access to and can understand. Give one bad piece of information, and it won’t pause to question it. It’ll just act on it.

Of course, businesses already have plenty of data. The challenge is that it’s fragmented, inconsistent, outdated, or trapped in disconnected systems. But data readiness is key if you want to harness the powers of agentic AI. 

Gartner made the point pretty clear in 2025, predicting that organizations would abandon up to 60% of their AI projects not supported by AI-ready data. In other words, having an AI strategy isn’t enough. The data it works with has to be ready, too.

So, where do you start?

Map Where Your Business Data Lives

Start by knowing what data you actually have and where it resides. This sounds obvious, but most companies actually don’t have this information. Data sprawls. It ends up in department databases, cloud platforms, spreadsheets, CRM systems, and business apps nobody remembers installing.

Walk through each department. Find out what they use. Ask who owns each data source and how it’s used day to day. Look for duplicates, isolated pockets, and information that’s hard to access. Build a data inventory that’s actually current. Update it as systems change.

Manish Sood, CEO of Reltio, says it plainly: “If your data is fragmented or conflicting, your AI will be too.”

Once you know where your data is, you can start figuring out whether it’s reliable enough for agents to use.

Clean and Govern Your Data

AI agents use your data to make decisions, so it has to be consistent and reliable. If your records are a mess, agents won’t clean them up. They’ll just spread the problem faster. So your next move is to get that data into shape.

Start with the basics. Remove duplicates and outdated records and standardize key fields like dates, customer IDs, and product names. Then set some ground rules. Who owns each dataset? Who gets to see it? And how do privacy and compliance fit in? Nail that down before agents start running loose. There’s also the part about what AI agents can access and where human approval is needed.

Not many companies have reached this level of data readiness. In fact, according to Deloitte, while nearly 75% of organizations plan to deploy agentic AI in two years, just about 21% have a mature governance model for it.

That gap can become a serious risk when machines start acting on those records automatically.

Make Your Data Accessible to AI Agents

No matter how good your data is, it isn’t very useful if an agent can’t reach it when it needs it. This is where many businesses hit another wall. Their data may be accurate, but it’s locked inside disconnected applications.

You can start breaking down those silos with APIs, integrations, shared data services, and other ways of connecting systems. For some use cases, agents also need real-time or near-real-time information. For example, an inventory agent shouldn’t make a decision using yesterday’s stock count if today’s number is available. That’s one reason businesses consider NoSQL databases for certain applications.

RavenDB, a highly efficient open-source NoSQL database, for example, lets AI agents securely query database information and request approved actions. This gives agents access to business context without giving them unrestricted access to the underlying system.

Not every business needs to replace its current database, of course. The point is that your database architecture should match what you’re building.

Build Human Oversight Into Agentic Workflows

Agentic AI doesn’t mean zero human influence in decision-making. It means putting humans in the right places.

That’s becoming an important consideration as businesses give AI agents more autonomy. KPMG’s AI Pulse found that 52% of business leaders weren’t yet ready to allow AI agents access to sensitive information without human-in-the-loop oversight.

So, how do you implement this oversight?

Figure out what your AI agents can handle on their own. Then decide which actions need a human to sign off first. Basically, the more autonomy an AI agent has, the more important those controls become.

Josh Fecteau, Chief Data and AI Officer at Teradata, makes a similar point in 2026. According to him, “Identify the highest-value portion of your data, structured and/or unstructured, and focus on getting that portion fully described, governed, and agent-ready.”

Your data layer can also help. Choose an open-source NoSQL database or other underlying data platform with strong access controls and auditing. This makes it easier to limit what each agent can access and possibly track its activity.

Agentic AI Data Readiness Checklist

The table below summarizes the steps discussed above and why each one matters.

FAQs

Do I replace my entire data stack before using agentic AI?

Not necessarily. Start with what matters most to your highest-value use cases. Map it out, clean it up, and make it easy to access. Then put controls in place to protect it. Once that’s solid, you can grow from there. The idea is to focus on the most valuable uses first.

What’s the biggest mistake companies make here?

Treating agentic AI as just an AI project. It’s a data project first. If your records are unreliable, your agents inherit those problems and act on them fast. Fixing data after deployment is much harder than fixing it before.

How much human oversight is too much?

That depends on what the agent’s doing. If the AI agent is handling information, it needs more oversight. If it’s just summarizing reports, less oversight is okay. The goal is to find the actions that carry risk. Then put your controls there.

Get the Data Ready Before Scaling the Agents

Agentic AI isn’t just an AI deployment project. It’s a data readiness project, too. Clean, governed, accessible data gives agents a solid foundation.

As we’ve already established, you don’t need to replace your whole data stack overnight. Focus on the use cases that matter more to your business. But first, make sure to prepare your data using the four steps we’ve discussed in this guide.

It goes without saying that the businesses best positioned to scale agentic AI will be the ones that prepared their data before handing more decisions to machines.

Guest Author