The essential role of insights leaders in guiding AI-enabled research in the business process
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.
AI will underlie the researcher tool set
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 more standardized and will equip insight managers to identify more connections, faster. In the coming five years, the AI capabilities of the tool set that insights managers are working with will accelerate quickly. And critically, there will be a shift in focus from simply identifying observable patterns to generating a proactive understanding of why those patterns have occurred.
Quality and accuracy in the reports that are auto generated will still need governance – as much as insights managers retain quality control over how the research they use today is assembled. The authoritative tone of ChatGPT has, for instance, frequently highlighted the gap between how confidently responses are presented to users and the underlying truth contained in those responses. Insights managers will be required to question how AI is collecting information and be able to guide usage safely. The risk of propagating misinformation at higher speeds than ever before will be a challenge not to be underestimated.
As the channels through which “intelligence” flows into the organization change, insights leaders will take on more responsibility for shaping how intelligence reaches and is absorbed by the business. Feeding insights into the heart of business and operational systems will require a much closer collaboration with IT and ops than is common today.
Conversational interfaces will be the catalyst for insight-driven cultures
A dramatic change to the business environment will be the use of conversational interfaces. Instead of logging into an average of 20 business applications to carry out different tasks, we’ll see the emergence of digital assistants or co-pilots that can simply act on natural language instructions. As a manager, you will define an overall task you want to accomplish, for example creating a graphic that illustrates the evolution of interest in low fat foods for a presentation. All the underlying steps, from retrieving the raw data, compiling the data table, creating a chart, and placing it into PowerPoint will be carried out by a linked group of intelligent bots working together. Tasks such as this will be completed in seconds, rather than the hours it takes us today.
Consider then, where insights and intelligence will be connected to business tasks. Which group of AI assistants will be interfacing with other systems assistants to locate and draw in the relevant insight? Importantly, within this new technical architecture, how will the insights leader govern and validate the insights and underlying data that is being shared on a real-time basis?
Developing new competences and growing confidence in how to build your own vision for an AI-enabled insights strategy, starts with gaining exposure to specific AI tools that you can trust to help fulfil a specific need today – for example, using AI such as DeepSights to share insights across the business more easily.
Insights leaders will be able to test and monitor the change in adoption of insights enabled when AI can provide reliable answers to business questions, whenever and wherever they are needed within established workflows. Form your own perspective on the limitation of technologies as well as the opportunities they open. By doing so, you’ll be able to apply AI appropriately to solve business problems successfully.