Editor’s note: Olaf Lenzmann is chief innovation and product officer at Market Logic. This is an edited version of an article that originally appeared under the title, “What will it take to enable consumer insights teams to see the wood for the trees?”

Over the past five years we’ve seen a staggering rise in the volume of data pouring into the knowledge pools curated by insight teams. Our thirst for more datapoints from an ever-increasing range of online and offline sources has been accompanied by a dizzying array of AI analytics tools to help slice, merge and pick apart the information that is being presented. Clarity of understanding has not improved at the same pace, however.

For many companies, just integrating this huge swell of data into a single source of truth is an achievement in its own right: for even more companies, it’s a goal they still struggle to achieve.

With the latest wave of generative-AI technologies hurtling toward businesses of all sizes, the processes that were fit for purposes only 12 months ago are straining to keep up.

Tech literacy – mastery, even – of a whole new set of AI tools will become an essential requirement for insight professionals. This isn’t just an imperative to complete their current job with greater efficiency but also to stay relevant in enterprises where access to information is universal.

On the one side, the tool set influencing how research is conducted, analyzed and interpreted is set for yet another transformation. On the other side, there will be a necessity for insights leaders to take on the role of corporate intelligence strategist, planning how insights are woven into the everyday operations of the business.

Until now, AI analytics tools have been mostly focused on spotting and flagging patterns in the data. Moving forward, however, the interfaces between different tools drawing together specialist data analytics will become mo...