Credit from Scott Littman
AI systems are evolving to solve issues related to enterprise search, including issues from human limitations, changing partners, and new staff.
We live in an era when businesses are losing knowledge faster than they can absorb it. If you think about all the PowerPoints, PDFs and Word documents that a business possesses, there’s no way to effectively harness it all. It’s simply too much data and information to keep track of. We’ve even seen situations where trying to find information is so challenging that customers are actually commissioning projects for work that was already done because they aren’t even aware that the work already exists.
With the speed of modern business and high demand for quick, impactful results, businesses need all the help they can get in harnessing their data assets. We are starting to see AI systems evolving to solve issues related to enterprise search—maintaining a bounty of information and being able to pinpoint where specific documents and information are stored. While the idea of asking “What were the biggest drivers of customer acquisition in 2017,” and instantly getting the specific answer from page 23 of a 75-page PowerPoint, seems like a fantasy, AI is making this commonplace. Here are a few scenarios in particular:
We work with a company that has a specialized team of over 30 Insights and Research professionals dedicated to Knowledge Management. The company has invested significant capital into legacy knowledge management systems. Despite this, they still needed to know the specific date, titles or identifying tag of a particular document to find what they were looking for. You can imagine the limitations in that approach.
Not only that, but they were also limited to finding the entire document, rather than the precise location of the information. Good luck finding your answer in a 150-page document that requires you to read and understand the contents within. Through the power of AI, this company can now cut down days or weeks-worth of research into mere minutes.
Another example involves a multinational conglomerate that recently changed agencies. The previous agency was asked to share information with the new agency, so they packaged thousands of PDFs and sent them over to the new team. Some of these PDFs were hundreds of pages long, so realistically, there was no way for the new agency to get up and running quickly without a team full of speed-reading insight experts.
The reality is that this would be impractical, as even reading all of the content wouldn’t help with proper recall of the bits and pieces needed weeks and months into the future. With AI, they are able to ingest every PDF in a system that will remember everything it’s read and recall it when requested at any point in the future.
Another example is a company where people flow from account to account regularly. While this keeps things fresh for staff, nobody knows where documents and information live. Titles and keywords are helpful to an extent, but they provide little access to the actual content that’s in the documents. They are forced to piece together findings based on a deep exploration process, sifting through hundreds of documents created by their predecessors. With AI, they retain the past knowledge which allows new teammates to perform at a high level more quickly.
The challenges outlined above aren’t new challenges for businesses. For years, businesses accepted these challenges as the “cost of doing business” because they didn’t know that this was something that could be solved. AI is changing that. For the first time, these challenges can be easily solved with the latest generation of AI-based knowledge management tools. Now, the game has changed.