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How AI is Ending Redundant Research Waste

Adopting an AI-enabled knowledge management platform can save not only time but money. Prevent repetitive research through using past work as a guidebook.

Editor’s Note: Smart companies are looking for cost savings where they can, in every corner of the company. It is a fact of everyday life that Insights departments have to continuously try to do more with the same or less. One strategy is to extract all the possible value they can from the data they already own. Data collected for one reason can be repurposed for another. In a really interesting piece, Scott Litman describes AI-based means by which companies can repurpose large amounts of data, saving money, time and effort in the process. A valuable read.


Data, Data Everywhere

The amount of data available to brands as they track performance and strategize next moves is astronomical. The digital world has created an evolutionary leap, and we’re still inventing new tools to help us survive in it. 

One of the latest tools is the advanced class of knowledge management systems tagged with labels like “cognitive,” “AI-enabled,” or “machine-learning.” As with any new technology, the cost must be justified by value and insight professionals who clearly see the benefit often need to persuade those higher up the food chain. 

One unexpected area AI-powered knowledge management tools are showing their value is in eliminating redundant research. Redundant research is the source of millions of wasted dollars. We know this because global brands themselves have researched the issue. (And extrapolating on the answers they found, we might even guess they have researched the issue several times, come to the same conclusion, then shelved the knowledge away somewhere inaccessible.) 

Improve Efficiency

It’s easy to see how redundant research happens — but it has been hard to see a way to solve it, until now. 

A brand gets a new manager, who identifies the need for a new marketing plan to reach the brand’s target consumer. The brand manager has no efficient way of investigating whether the previous team had valuable insights that could be re-used, or whether other divisions of the company have run analysis on the same sort of consumer. 

They could try asking the Insights Team. Though the Insights professionals are already carrying a heavy workload and have too many demands on their time. They have much more important things to do for the organization than to answer all of the one-off questions across the company.

Plus, there is no humanly way possible to remember every nugget of information ever produced, much less know exactly where each brand team may have stored their assets across company silos. 

With no effective way to quickly find answers, they need to start from scratch. The process takes a month and has a sizable budget. A few years later, they move on to another position. Their replacement starts in the same position—with questions that need to be answered, and with no easy way of searching existing company assets for relevant data. So, the cycle of redundant research begins anew. 

It’s not just a waste of money. Brands that are quickest to react to an insight have a competitive edge. What company wants to take unnecessary time out every few years because their internal knowledge management system isn’t up to the task?

AI Means Big Impact

This is where companies will see a huge impact on the adoption of AI-enabled knowledge management systems. For a system to truly save companies from redundant research, that brand manager I mentioned needs to be able to open a single application, play around for ten minutes asking a handful of questions, and walk away with certainty that the assortment of a dozen charts, reports, and PowerPoint decks their search returned represents a thorough examination of hundreds of thousands of company-owned data assets. 

And when any team creates a report, the knowledge management system needs to automatically ingest it and search it. These are marketers and insights professionals, not archivists, so it’s a waste of their skillsets to make them tag data assets with keywords in the hopes of leaving a trail for their successors. 

Take the example of a manager who was ready to start a research project to gain insights about moms who buy healthy kid’s yogurt. They start by asking the company’s AI-powered knowledge management system, “What do you know about moms who buy healthy food for kids?” Instantly, they find quick facts from a report the past manager did, and even better an intensive research study commissioned six-months ago from a cereal brand team with the exact information needed. The time and money saved from recreating past work can now be used for strategic action.

Solving the Problem

Cloud computing and machine-learning have combined to solve massive storage and smart-search problems. For the first time, inadvertent duplication of research commissions need not be part of the “cost of doing business.” 

A global CPG company who recently piloted an AI knowledge platform called it “game-changing.” Starting with 10,000 documents that the client did not have to tag or help with beyond providing access to file systems for ingestion, they were able to test the system in just a couple weeks. With little to no training, the AI-based system was already providing specific answers to questions. Not only did the marketers using the system report that they were rapidly finding the answers they were looking for, but they also were uncovering key data they didn’t even know existed. 

When different teams operating at different points in time all feed their final insights into a central, shared state-of-the-art knowledge management system, their discoveries remain close at hand. This secure and specific sharing, coupled with the advancements in a search made possible by cognitive computing systems, will change the way resources are allocated.

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2 responses to “How AI is Ending Redundant Research Waste

  1. Interesting, Scott. But I can’t help but be struck by the fact that if my job were to know everything about my brand, it just wouldn’t be that hard. When I started, everything was archived in our research library and easily found. Our first week or two working was to sit in the library and read every study ever done on our brand. We came out of there with a pretty complete knowledge base. We had senior people in each division who were able to give us the big picture – how my brand was similar or dissimilar to other brands in my business unit. We also interacted with researchers in other business units, so we knew what was going on there – with a special meeting if someone did something particularly cool – called by the head of MR, who knew all. I’m not sure this is much better than smart researchers who’ve done their homework and the brand managers who listen to them – not sure you need to overload them with another tool. And let’s face it, if your organization was so poorly organized and poorly staffed that this was game-changing, you may have bigger business issues.

  2. The point isn’t that we can’t do this on our own, the point is that over 90% of data within a corporation isn’t being utilized, and when it is, it’s very inefficiently done. Yes, we can grind through hours of research to gain the required knowledge, but how does that help the company when you move on to the next role? The advantage of AI, when applied thoughtfully is to enhance our efforts, not replace them.

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