5 Ways To Tell if Your Data Is Ready for an AI Agent

You know the promise of AI: The models are getting smarter and the tools are getting better. But for most companies, the biggest obstacle to success isn’t the technology, it’s the data. You can buy the most advanced AI in the world but if your data is fragmented, siloed, or just plain wrong, your efforts will be, to put it charitably, underwhelming. 

This is a hard truth to accept but without a sound data strategy, you have no AI strategy. Investing in AI without first preparing your data is like fueling a racecar with water. You can spend big, but the vehicle won't go anywhere. Bad data leads to flawed insights, wasted investments, and models that are more of a liability than an asset.

The good news is that this problem is solvable. This month’s newsletter offers practical strategies, tools, and resources to help you assess the quality of your data, learn how to prepare it for AI, and much more. This is the work that turns your raw data from a stubborn roadblock into your most powerful strategic asset.

Wanna know the secret to high-performing AI agents? 

Is your data "good enough" for AI? The surprising answer: It doesn't have to be perfect. Stop trying to clean all your data and start by identifying a single, high-value business problem you want to solve. From there, you need to only prepare the data an AI agent needs for that specific task. This focused approach saves you immense time and effort, and puts your AI to work fast. To see where you are on the data readiness spectrum and get a checklist to start, take our test.

Raw data has no context — metadata gives your AI the full picture

AI without context is like a photo without a caption. You can see what’s in the photo, but you don’t know where or when it was taken, or who’s in it. This is the core challenge of AI relevance: outputs without meaning or insight.

Metadata solves this. It’s structured data that describes, explains, or provides context for other data. Without metadata, for example, you wouldn’t know what a string of numbers in a customer record means. Is it a phone number, a transaction amount, or a customer ID? There’s no way to tell. Here’s everything you need to know about this crucial layer of context, including its definition, types, and uses.

Here’s a solution for the 90% of data you’re ignoring 

Unstructured data, which accounts for nearly 90% of enterprise information, is your biggest competitive advantage, and your biggest headache. Unlike structured data that’s neatly stored in databases, unstructured data like emails, videos, documents, has no fixed format, making it incredibly difficult to organize and analyze. But it's also the essential fuel for true AI innovation. Three CIOs share their top strategies to turn this challenge into a powerful asset.

AI models are a dime a dozen – focus on this instead

Just as the dot-com era taught us a website was useless without a back end, today's AI models are useless without an ecosystem. That's why the first step in any AI strategy isn't selecting a model, it's building a foundation that connects to business processes, real-time data, and enterprise-grade security. Only when that work is done can you take the next step of selecting the right AI model to power it. Without this unified foundation, even the most advanced AI will remain underused and fail to deliver on its strategic promise. Here’s why.

This newsletter was curated by Lisa DiCarlo Lee, Contributing Editor at Salesforce.