Why AI Needs Business Data to Create a Competitive Advantage

In a recent LinkedIn post, Clare Kitching discusses why pairing artificial intelligence with a company’s own data can produce more useful insights and a competitive advantage. Her post contrasts the value of data and AI on their own with what businesses may gain when they combine them.

“Combine it with AI and you’re taking it to a whole other level: You’ve made data more accessible and the insights you can find, predict, and create are much more interesting.”

Data gives AI business context

Kitching describes data as useful in its own right: it can help a business understand what is happening and support dashboards that track performance. She argues that AI can make this information easier to access and help people find or generate insights from it. The value, in her account, comes from bringing the two together rather than treating AI as a substitute for reliable, relevant data.

To illustrate the point, Kitching opens with familiar food pairings, writing that “some combinations are magical, like strawberries and cream or Vegemite on toast.” The analogy frames her central argument: each ingredient has value, but the combination can offer something more.

Generic AI may not set a business apart

According to Kitching, AI tools can still help without access to a company’s own data. She notes that they can produce a first draft or something that “looks good,” while cautioning that these outputs rarely provide a competitive edge by themselves.

“AI combined with your business’s data will give you the insights, access, and advantage others can’t replicate.”

Kitching’s distinction is between general-purpose assistance and insights grounded in a business’s particular information. Her post suggests that company data can give AI outputs context that other organizations cannot simply copy, though it does not specify which data practices or AI systems are needed to achieve that result.

Readiness is part of the equation

Kitching also points to a Gartner prediction: 60% of AI projects without AI-ready data will be abandoned by the end of 2026. In her argument, this forecast underlines the importance of preparing data as businesses pursue AI projects. She does not define “AI-ready” in the post, but the reference reinforces her broader message that data quality and accessibility matter to whether AI initiatives deliver value.

By ending with a question about readers’ “secret combination,” Kitching invites reflection on how complementary capabilities can work together. Her post presents the pairing of AI and business data as one such combination, with the potential to turn operational information into more accessible and distinctive insight.