As GenAI starts taking ground at work, how does an organization make its vast amounts of data useful?

When I first started using GenAI, my first thought was we can skip the fancy visualizations and go straight to asking our questions directly with GenAI. However this would require data to be organized, consistently labeled, and cleaned up. Easier said than done. Plus GenAI would need an assistant to actually do proper calculations.

So let’s start with an org’s existing linguistic data, its knowledge base that defines a company.

Back to basics – text first

Cleaning up, organizing and ensuring that the information is clear and readable. Fancy graphics, flat pdfs, animated presentations are not going to help. With GenAI, text is where it’s at. Is it good for a screen reader? Can we manually highlight the text? Then let’s go!

Yes, boring stuff. Yet it’s the boring stuff that makes GenAI so useful. Manufacturing documents for example usually follow a template, with headers, and clearly laid out text. The qualities that make a document accessible are what make it an asset for GenAI. So start organizing your documents, clean out obsolete, incorrect, draft versions. Take heed from the document-driven functions of your company. Lay it out clearly. And on the surface, it seems tedious, yet when you get into it, it triggers your mind to start understanding the content more. Once you’re organized, it frees you to think more clearly on to other topics. It allows you and others to access information easily, including GenAI. Writing it down and organization as liberation.

For example, at a manufacturing plant, try standardizing equipment maintenance logs. Boring? Absolutely. But this allows the maintenance team to ask GenAI “Which machines had bearing failures in the past six months?” instead of digging through spreadsheets. AI excels at finding patterns in simple, well-organized text and data.

Building bridges

With your documents and data organized, the next step is creating access points. Think about how GenAI will interact with your data. Creating simple indices, metadata tags, and consistent naming conventions is crucial. Your HR handbook, financial reports, and product specs might all live in different systems, and GenAI needs a coherent way to find and interpret them.

The question determines the answer

And while GenAI keeps improving, humans are necessary to provide the oversight, knowledge and experience. The key is teaching the team to ask good questions, not just expecting the AI to deliver insights unprompted.

Your AI should have an access badge too

Not all your company data is or should be accessible to all employees, which means your AI should follow the same model.

Good for AI, good for humans

Not surprising, organizing for AI benefits humans too. Clear document structures, consistent naming, and accessible information repositories make life easier for everyone. New employees onboard faster when they can ask basic questions to GenAI.

Start!

Somewhere in your org, there’s already a team or individual who maintains well-structured documents. See how well GenAI can assist in these areas, and spread those learnings.