Is your data team building with AI-ready data?

The real AI bottleneck isn't model selection or prompt engineering—it's integrating AI into disconnected systems. While teams focus on the latest LLM capabilities, the organizations seeing real AI ROI are the ones who can build applications that actually do something with data insights. Data readiness for AI isn't just about access—it's connecting your data ecosystem in ways that enable action.

When your business data is truly connected and accessible, it naturally enables both immediate self-serve analytics and future AI applications. Unified data is the foundation for both.

With well organized, well documented, structured data, you can convert spreadsheets into apps and even have AI take care of basic analytics. But by far the biggest value add of AI-ready applications is in processing and automatically acting on data. Instead of stopping at producing insights, with AI agents and agentic workflows, it's now possible to turn data into action—automatically.

Using AI to operationalize insights is a relatively new capability, but as more companies experiment with and implement it, acting early and quickly on data will become a competitive moat that compounds over time. But speed isn't the only advantage here: organizations that connect disparate systems can uncover richer, more contextual insights and will outperform those still relying on siloed tools and manual processes.

The value of automating and operationalizing data insights is even greater for unstructured data, which is typically harder or too time consuming for humans to analyze. Your company might already be using a whole suite of tools to work with structured data, but the techniques and applications for working with unstructured data are less mature. Preparing unstructured data for analysis and action has a higher bar for readiness, as we'll explore below.

How do you know if your company's data ecosystem is ready for intelligent applications?

Imagine you have a CFO who knows bare-minimum SQL—enough to say, "select column A from table B where X equals Y." You have everything in place so that you can trust that this CFO can access your data warehouse, consult the documentation available, fetch their own data, and generate their own insights.

If, hypothetically, all of that is true for your organization, the same will be true for an LLM standing in for that CFO, but it will be able to do all of that in a fraction of the time. The data prerequisites for AI-ready applications are some combination of unified data sources, strong data governance and access control, data quality assurances, documentation, and well organized data. Let's explore those in more detail.

AI data readiness will look slightly different depending on your use case for it—you can't just be "ready for AI" without a specific goal in mind. Whatever you're trying to accomplish with AI will inform what data readiness means—it's a tool, not a destination.

But there are some baseline requirements that you can put in place right now.

AI agents are often functionally RAG apps built on standard models that are made more useful by your specific business context. To build AI apps on real-world data that's relevant to your business, you need to bring together data that's scattered across disparate data sources—ideally into a centralized data warehouse.

Platforms like Stripe, Salesforce, and Zendesk all contain information that can help to paint a picture of the business. But intelligent applications or agents can only act on insights when they have the full business context, and for many companies that context is fragmented across silos.

Before AI apps can really come into their own, you need to unify scattered data sources by centralizing as much as you can into one integration. That way the agent can fetch relevant information on an as-needed basis and determine the next step to take. At Retool, most of our enterprise data is in Databricks, where we have some 1,500 quality checks that are run as often as every 30 minutes.

Why is this important? When you task an agentic system with acting on your data, it's critical that your data is trustworthy and accurate. Without all the relevant data, or with conflicting data, an agent might take the wrong action (like automatically sending a discount code to a customer who is not eligible for a specific promotion). Centralized or unified data means you have one system your data teams can rely on to access or query the most highly governed, up-to-date, and high-quality version of your critical data.

You don't necessarily need to store all of your data in the same place (building with a platform like Retool lets you connect to multiple data sources and manage them from one place) but you do need to be able to trust your data, and centralizing is one way to ensure reliability.

Fragmented data also makes things harder to debug, especially when working with LLMs, because it's often harder to pinpoint when their performance degrades, and why. A production agent that's pulling in data from four different data sources means four different places where this data is managed, and possibly four different systems you need to set up to monitor data quality. If something goes wrong, how do you establish if and when the structure of the data changed?

On the other hand, low-friction data integration accelerates development velocity: when building an agent, it's faster to develop tools when you can easily reuse and adapt them. For instance, a tool fetching a customer's name can simply be tweaked to fetch their order history or location, all from the same dataset.

Of course, in an ideal world, all of your data would be stored in one neat data warehouse. But we know that's not always possible. When data is decentralized, each data source comes with its own barrier to entry:

Once you figure all of that out, there is still a risk that someday, an integration that you build through your agent simply stops working because some well-meaning IT person thought it wasn't in use anymore. With multiple data sources, there's more likely to be at least one system lacking in strong governance and reliability.

This is where centralized data governance comes in: having a single control point to connect, govern, and use data across your ecosystem securely and with visibility. You can consistently manage data governance and access in a way that's both secure and without friction, even across multiple data sources.

Tasking an LLM with parsing and reviewing unstructured data presents huge opportunities, but unfortunately, if you simply paste in an enormous string, or data from multiple sources, you're quickly going to see increased error rates from exceeding the context window or hit your token limit for API calls. While context windows have been getting larger, it's still more expensive to process big text blobs.

So you'll likely have to transform your data into a format the LLM can work with (like vectorizing it). Plus, you'll need to be able to extract the specific pieces of information the LLM needs to know and feed only those into it.

Here's an example of transforming unstructured data to make it ready for AI.

At a global manufacturing company, each production facility generates large, semi-structured equipment logs—records capturing machine settings, operational states, error messages, and sensor data. While rich with information, these logs are inconsistent and difficult to process at scale.

To unlock their potential for AI-driven insights, the data team can build a transformation pipeline that:

This pipeline enables the company to build AI applications that detect equipment anomalies, forecast maintenance needs, and optimize supply chain flows with auto-ordering.

With LLM-powered data science, you can automate otherwise manual tasks, but to do it effectively you need an ontology on top of your structured data. Your intelligent application is like a toddler who just happens to be a prodigy when it comes to data science: really great at writing SQL, but lacking the business context to know what every column in the table means.

Feeding the LLM a few table names and a small description of the tables isn't going to result in high-quality code. You need to think about:

An AI application also won't think to stop at a particular step to clarify when something doesn't match its understanding of how the business works. Since you can't rely on human intuition to pick up when something doesn't look right, or for the LLM to DM you for help if something isn't clear, preparing both the data taxonomy and the prompt to be as straightforward and clear as possible is necessary for good results.

Providing clear examples of how to join the data together, or translate a natural language request into a SQL query, for example, will enable it to do the job reliably and effectively.

In addition to providing clear data, instructions, and well-vetted examples, you need documentation that's both thorough and in an LLM-readable format like Markdown (similar to the "agent experience" for AI-ready applications). As with software engineers, the highest-value work for data engineers may now be in creating the systems and resources that agents and LLMs need to do the work, rather than performing the hands-on work themselves.

Garbage in, garbage out. Model performance is only as good as the data you feed in, so quality checks are essential to prevent inaccurate or misleading output.

Data teams are tasked with so much more than making stagnant dashboards—they're leading the charge in making AI useful. Here's how some are doing it today with Retool.

When Retool teammates have a project for which they need data, they can consult the "Find my data" app, which uses a variety of criteria to recommend data sources, including both other Retool apps (such as BI dashboards) and datasets that the individual can query themselves. This is a great example of enabling self-serve analytics, but with unified, reliable data and data governance you can go even further by having agents take action on data insights:

For many enterprises, agents and intelligent applications can feel risky, since it's harder to know for sure that your non-deterministic or less deterministic system is secure. Retool acts as the application layer for AI, handling concerns like centralized data governance and security.

Retool standardizes and creates a single place for managing who can access which data across multiple integrations. If a team member creates an agent that fetches data from, for example, Salesforce and Stripe, they only have access to the information within those systems that your administrator has granted. If a user tries to access data or perform actions for which they lack permission, Retool will block the action and return an error.

Retool also creates a single source of truth for the code you use to fetch and transform the data from those systems. Retool's Query Library lets you create, run, and share canonical queries across your organization and between Retool apps. You can scale that to any number of agents or agent tools, too: you could have 500 agents all referencing the same Query Library query for fetching a specific metric.

Ready to build your first agent and start turning data insights into action? Learn more about Agents.