Business intelligence can make a huge difference, and often in ways you don’t expect. In the summer of 2004, Hurricane Frances threatened the Florida coast. Given that shoppers tend to stock up during the calm before the storm, Walmart wanted to know the most profitable items to stock.
Using a huge dataset, the company established some surprising shopping trends in the lead-up to a major storm. Of course, essentials like water sold well, but so did items like Pop-Tarts. During Hurricane Francis, Walmart made huge profits by stocking the products indicated by the data.
In the new era of AI-driven data analysis, it’s possible to find even more incredible insights waiting to be uncovered in your business data.
Why GTM AI Raised the Stakes
Go-to-Market AI, or GTM AI, is a key tool in identifying potentially profitable accounts. Using big-picture data analysis, these systems can find hidden trends or insights, often obtained by analysing thousands of subtle data points.
Using extensive data analysis, a GTM AI agent might uncover accounts more likely to buy that you hadn’t previously considered. But the agent isn’t just responsible for highlighting actionable insight; it can also assist with the action itself. GTM AI is capable of the following:
- Helping you draft an outreach email to a potential client.
- Assisted you in researching potential clients
- Provide you with contact information for the lead
The Importance of Data
While GTM AI agents are capable of incredible insights, they’re only as good as the data you put in. These insights need detailed, up-to-date spreadsheets to be effective. If you only provide your agent with an outdated, incomplete, and improperly labelled Excel file, you won’t have much luck.
A poor dataset can result in inaccuracies and misinterpretations, if not hallucinations.
The API Layer
The AI works in tandem with the API layer. The AI is responsible for thinking, while the API sources the facts. The AI agent can process the data much more quickly than any human trying to read and comprehend the information for themselves.
The API layer is only as strong as the underlying data, so let’s look at some of the best principles for creating data valuable to GTM AI.
The Data Layer
Providing good data means having the right standards. When organizations have documented data collection practices and the training and infrastructure in place to support it. Here are some of the key features of effective datasets
Company and Contact records
Company records are a history of who your company is. For example, records may contain information about the following:
- What kind of software does the company run
- How many people are employed and in what role
- How much the company is earning
- Information about current, past, and potential clients and how to contact them.
The longer companies have collected this kind of data, and the more consistently it’s formatted and labelled, the more effective AI GTM agents will be.
Matching
Without correct labeling, you’ll run into problems with matching data entries. Without an effective matching process, you end up with multiple data entries referring to the same person or entity. For example, the company IBM could have multiple entries in a database because it’s also listed as “I.B.M”.
While AI GTM agents are capable of entity resolution, it’s better if the data is clear with a single entry for each entity before its analysis begins.
Fresh Data
Industries and the people in them tend to change a lot, so data needs to keep up. AI GTM agents are only capable ot effective analysis if they have data that’s up to date.
Agent-Native Patterns
The key to effective analysis is to build an effective API so that both. The Model Context Protocol, an open standard, lets an agent find out what a provider offers and call it in plain language, with no custom connection code.
Several large data providers shipped this kind of interface in early 2026. A developer-friendly sales intelligence API built this way is clear to an engineer and an agent alike.
Privacy and Compliance in Fintech and SaaS
If you sell to banks or software firms, the origins of the data matter as much as correctness. In Europe, the GDPR lets you contact a business prospect without prior consent under what it calls legitimate interest. The conditions are clear:
- The message must be relevant to the person’s job
- You must write down your reasoning
- The person being contacted must have an easy way to discontinue communication.
Put Your Data to Work
Business intelligence rewards the companies that act on it, especially when they use the latest AI/API integrations. The key to success is clean, current data feeding the right tools.
If you’re interested in learning more about similar topics relating to AI and technology, see our other blog posts.
