When comparing A/B testing products you'll see claims in marketing material that A/B testing will enable or enhance "data-driven decision making" in your organization.
That phrase refers to some ideal org state where decisions about what goes into a product are made purely via conclusions drawn from hard data.
Lee Maxey makes a good case for preferring the phrase "data-informed" over "data-driven":
My core issue with the term ["data-driven"] is that it establishes unrealistic expectations for most businesses. Sure, the corporate giants of the world (oil companies, banks, media conglomerates) have visibility into massive amounts of data and can make precise decisions that are truly data-driven. But the vast majority of organizations [...] draw from much more limited data. At most, data is a tool that can, along with other tools like observation and experience, guide the inferences that lead to better decisions.
Put another way, in a data-driven org all important changes to your product are justified by hard numerical evidence demonstrating their value to the business, customers, or both. And the opposite should also hold, that product changes not backed by empirical evidence are rejected as inherently biased by personal judgements, etc.
But in a data-informed org, important changes are driven by a mix of numerical evidence and intuition derived from experience. And lacking that evidence (in A/B testing terminology, a "not statistically significant" result) is not sufficient for rejecting a change.
"Data-informed" is definitely a more realistic way to think about the impact of tools like A/B testing platforms. It's terminology that takes into account how easy it is to draw bad conclusions with data of all shapes and sizes, no matter the tool.
In practice
Say your team has decided to improve some metric in your product like total purchases. Based on trends in user behavior you're seeing today + intuition derived from experience, you probably have a handful of ideas on exactly what should change in your product to reach that goal.
In this situation A/B testing can serve as a tie-breaker. You have several ways you can go that all seem plausible, and a properly implemented experiment can help predict the relative impact of each.
Thinking about A/B testing as a form of prediction will set you up with correct expectations. It's not thinking about e.g. a +10% treatment lift as proof that drives your decisions, instead you're preserving the language of uncertainty - "a +10% lift in our primary metric is likely / unlikely if we go with this treatment".
Remember: the experimental methodology means extrapolating something about a generalized population from a randomized subset. A positive / negative finding is not a declaration of proof, it's a declaration of likelihood.