Half of our modern economy is built on knowledge workers and knowledge-based work.
But “knowledge management” as a concept carries a lot of baggage. When people in companies bother to think of it at all, it brings to mind menial, thankless tasks like updating the company wiki.
And, wow. What a disservice that perception does to a company’s opportunities for success.
Thankfully, with the arrival of machine learning, natural language processing, and other artificial intelligence (AI) technologies, the concept of knowledge management is getting a lot more appealing.
In this piece, we’re going to dive into the specific ways artificial intelligence can help knowledge management at the companies who are forward-thinking enough to adopt these (surprisingly affordable and easy-to-implement) technologies.
Here’s a basic, indisputable challenge for every company:
As a company grows, the amount of knowledge that needs to be managed and distributed grows exponentially.
IT and HR teams roll out new policies and tools. Marketing teams gain new insights about prospective customers and the competitive landscape. Sales teams learn new strategies for overcoming objections. Engineers develop an increasingly-complex code base behind the product.
You get the idea. Companies are constantly generating new knowledge, which gets even more challenging to manage and leverage effectively.
In fact, an IDC study found that knowledge workers within a 1,000-person company waste about $5.7 million annually looking for information–without being able to find it.
There are two key components to these challenges:
On average, a knowledge worker spends one-third of their day looking for and organizing information spread across different internal systems. (How often do they find what they need? Only 56% of the time.)
AI technology implemented as a team intelligence layer breaks down a company’s knowledge silos, integrating all the information that’s available across a team’s many apps and teams.
It becomes the smart, one-stop-shop for information needs in the company. To that end, it ideally integrates into the company’s existing, widely-adopted workflows, whether that’s in Slack or over email, for example.
AI helps the system intelligently surface the right information at the right time–regardless of the app or team where the information originated. It intelligently interprets the intention behind employee queries, automatically surfacing relevant insights. Employees discover useful resources they didn’t even know existed.
Rather than knowledge workers digging and asking around for information, the system does the exploration and retrieval from across the company, allowing the knowledge workers to spend their time and energy on their core competencies. And on putting the gathered knowledge to impactful use.
Plus, thanks to its inherent ability to identify patterns, machine learning (ML) based AI can even point people to the right subject matter expert in the company for a given question.
Workplace requests are generally two-sided. That is, to get the information they need, workers usually have to ask a colleague for help. At least two people have to be involved.
When you consider all of the teams (like IT, HR, Finance, etc.) that handle requests every day–and how much of their job is just responding to simple questions–it’s clear that these interactions take up a lot of time, energy, and resources.
A study out of the University of California, Irvine, found that the average office worker is interrupted once every 11 minutes. And it takes them 25 minutes, on average, to return focus on what they were doing before.
So, workplace requests for information can be incredibly disruptive to productivity.
By providing an automated, always-on solution for handling workplace requests, an AI solution built as a Team Intelligence Layer doesn’t just make it easier for people to find and access the information they need. It also frees up their coworkers from disruptive interruptions.
With fewer distractions, people can get more done. (That’s just science.)
So AI technology developed as a team intelligence layer can break down knowledge silos, allow companies to act on knowledge more effectively and quickly, and dramatically reduce productivity disruptions around knowledge-seeking.
You can see why this model of knowledge management is a lot more compelling than a traditional knowledge base system like an intranet or wiki. Those systems lack intelligent search, are typically poorly-adopted by teams outside IT and HR, and frequently suffer from neglect and information rot.
We here at Spoke believe the early adopters of AI-powered knowledge management will enjoy enormous competitive advantages over the laggers. That’s why we’ve doubled-down on making this kind of technology simple and accessible.