Humankind has existed within the Information Era for several decades now, and as tech continues to advance, we keep storing more and more information. So much so, in fact, that it’s becoming increasingly difficult to access the exact piece of info when it's needed.
For a while now, the best thing people had come up with to solve this problem was the diligent use of keywords and tagging. So long as one was diligent and disciplined in their meticulous labeling of their pieces of information, it would only be a mild headache to search for relevant keywords and sift through the results. That method has quite a few shortcomings, though, and has grown increasingly unfeasible as the stagecoach of mankind careens forward, rapidly ballooning with more and more data.
In the face of this challenge, one blossoming tech sector, Knowledge Management, has taken the reins and whispered into the ear of information seekers everywhere: “worry not, I’ll handle it.”
Put simply, KM is exactly what it says on the tin: when a given entity (such as a company, organization or even a book club) has accumulated its own knowledge database, all of those pieces of info can be difficult to sort and find when the relevant data is required. The KM field offers technologies which can take all of that knowledge, parse through it, digest it and call up the utmost relevant information at a moment’s notice – without the need for a melancholy office worker to spend hours of their day tagging documents beforehand.
How is Knowledge Management AI possible?
This is made possible thanks to Natural Language Processing (NLP), a rapidly developing facet of artificial intelligence (AI), which uses machine learning (ML) in order to understand and process human language, written the way that humans write it.
There’s no need for code: whether it’s a college essay or a typed version of your grandmother’s recipe book, NLP can read it, understand it and remember every aspect of it to such a degree that, when paired with good KM software, even a prompt like “what kind of nut does Mimi use in her carrot cake?” will bring up the answer, simply and without the need to sift through irrelevant search results (the answer is “walnuts”).
“Knowledge Management is the next generation; we won’t need to tag anything anymore.”
Sagi Eliyahu
“Knowledge Management is the next generation; we won’t need to tag anything anymore,” said Sagi Eliyahu, CEO of KMS Lighthouse, which is a KM company whose platform utilizes NLP in order to provide these kinds of precise answers to leading companies such as GE Healthcare, ZIM and Bank of America. Its reputation within the field is such that the company was granted the KM Promise Award at the annual KMWorld Conference in Washington DC in October.
IN A CALL with The Jerusalem Post, Eliyahu explained that the kind of KM tech that KMS Lighthouse is developing will put both keywords and tagging firmly into history books, never needing to be used as a viable solution again.
“Today in the industry, each organization has its own knowledge base in order to empower its employees, its customers and its agents to find information quickly and easily,” Eliyahu said. “Before, an organization’s approach was to tag everything. Every business glossary was manually created. Now, you don’t need to do that anymore. You don’t need to tag anything [thanks to] machine learning and AI.”
From a corporate perspective, KM’s value proposition is invaluable: it means access to faster results tailored to the employee searching the company’s database, which results in more productivity and less “fog of war” when it comes to sifting through collected information.
Though it may mean a lot to businesses, what NLP-driven KM means for the everyday layperson, however, is even more promising. Remember the last time you had a question about a flight, but you couldn’t find the relevant answer on the airline’s website and were forced to wait on hold for an eternity, just to ask your question to a representative who may or may not know the relevant answer? Within a few years at its current rate of development, KM tech can also put an end to that.
“The challenge is pulling information quickly, easily and consistently,” Eliyahu said. “Today, if you call a call center, or you speak to a nurse and ask them a question, they will tell you what they believe is the right answer. But that will depend on what they remember, their experience, previous cases… You’re depending on the person providing you the service [to know the exact, correct answer]. What we’re trying to do is to eliminate that. We are reducing the dependency on the specific person that is providing the service.”
KM’s quick and easy navigation of stored data also means that, even amid the current downsizing trend sweeping through Israel as a result of global trends, companies may still be able to handle customer queries without going under – and KM tech services will likely benefit as a result.
“COVID has pushed adoption of solutions like ours. Everyone is talking about recession; technology is only going to be leaned on more and more by organizations that need to downsize their labor forces,” Eliyahu said. “Technology isn’t going to struggle; efficiency will prevail.”