Knowledge management—the efficient handling of information and resources within a commercial organization—is not a new concept in business. In fact, it has been around since the ’90s. But even as businesses are still trying to get a handle on knowledge management practices, the concepts and processes developed back then are aging to the point of becoming obsolete.
In other words, knowledge management as we know it is not destined to survive the digital age. The issue is that digital technology has, far from making management easier, contributed to the explosion of available information.
To survive the digital age, organizations will need to start thinking, instead, in terms of knowledge automation.
Proof that Knowledge Management Falters in the Digital Age
Knowledge management grew out of a realization that large organizations needed to organize their information in a more holistic manner. This meant capturing and retrieving needed information from a variety of sources: databases, documents, policies and procedures, and even the expertise and experience implicit in the practices of individual employees.
This sounds like just the sort of thing that should be easy with cloud technology and better integration tools. But consider:
- A typical employee is inundated with information; on average a worker receives the equivalent of 174 newspapers worth of information every single day.
- The average worker spends approximately 20% of their time locating information to help them do their jobs. Sales teams spend one-third of their time searching for information they need to sell; service teams take three times longer than needed to resolve issues because they are searching for information.
- That said, 40% of corporate users report that they can not find the information they need to do their jobs on their intranets.
- Only 11% of organizations have a formal process for capturing “best practice” and “lessons learned” content at the end of each project or engagement.
- Sales teams fail to use around 80% of the content created by marketing teams, even when that content is generated specifically for sales enablement.
- Fortune 500 companies lose roughly $31.5 billion a year by failing to share knowledge.
- One simulation determined that the time spent looking for (but not finding) information could cost a large organization a total of $6 million a year. That doesn’t include opportunity costs or the costs of reworking information that exists, but can’t be located.
Information is everywhere in organizations. But employees are spending an inordinate amount of time trying to find just the information they need, when they need it. As the information in an organization grows, this situation gets worse, and management of that information itself because an ever greater task.
In other words, knowledge management has led to better collection of, and access to, information…but it has failed to make the retrieval of pertinent knowledge faster or easier.
What Knowledge Management Promised
Still, the original needs that knowledge management was supposed to address will not go away. In an ideal world, proper knowledge management would enable things like:
- Rapid data-driven decision-making
- Fast dissemination of relevant information across siloes
- Minimization of duplicate efforts
- Broadcasting best practices and solutions in a timely manner
- Better utilization of existing knowledge assets, both formal and informal
- Better leveraging of SMEs’ knowledge
- Better use of SMEs’ time
- Standard and repeatable processes, procedures, techniques, and templates
- More accurate and timely information for sales teams
- More rapid response by customer service and support teams
Notice that these benefits are about speed and relevance as much as anything. But these are exactly the areas where traditional knowledge management has been slow to develop.
Even more disheartening is the cost burden that knowledge management has brought, without realizing return on those investments. Improper planning, design, support, and evaluation can easily lead to a lack of widespread contribution, which further erodes usefulness, relevance, and quality. And, even when moderately successful, using older knowledge management system is costly to maintain and difficult to scale because of their dependence on “top down” knowledge management rather than “self-building” knowledge.
From Knowledge Management to Automated Knowledge Curation
“Knowledge automation” is becoming a popular way to describe how machine learning and artificial intelligence (AI) can be used to automate more of the knowledge management process. (“Automated knowledge curation” is another, although less popular. It means much the same thing.)
Much of the knowledge within organizations is generated by the activities of your people. They email questions and answers back and forth. They use informal communication tools like Slack. They produce wikis and sales sheets and blog content. All of this knowledge is there in the organization—it just needs to be curated and made automatically available with the touch of a button.
This is the idea behind “self-building” knowledge: knowledge that is already present in an organization and that is continuously curated instead of “managed.” Knowledge automation creates access to self-building knowledge, rather than relying on “top-down” management.
After all, information itself is cheap; as the list above shows, it exists in many ways, and in many different forms. That information only becomes knowledge when the right slice of information can be applied in the right situation.
For example, suppose a prospect has a question about a particular feature on one of your newer products. Your sales team should be able to answer that question without wading through a pile of sales sheets and development wikis—or worse, waiting for an answer from a SME halfway around the world.
Another example: A customer has an issue and reaches out to your organization via social media. Your customer service team now has to query several separate systems in order to handle this: a case management system, an internal incident management system, a knowledge base, and several off-band communication channels. This would usually take a full day; imagine, though, if the relevant knowledge in these systems could be made available instantly, so that a reply could be made within the hour. (In fact. call center costs and volumes can decrease by as much as 30% when better search and automation tools are implemented.)
These are just a couple of simple examples—you can follow the links for more detailed use cases. Still, they are good examples of why traditional knowledge management is not surviving the digital age. They also show why we developed our app, Nimeyo, as a way to automate the gathering of information across channels. It is a knowledge automation solution that brings both speed and relevance to those who need the right information at just the right time.
Like Beanie Babies and dial-up internet, some things should be left in the ’90s. It’s time to update the way we access information in this digital age.