Institutional knowledge continues to be a casualty as boomer-retirements and pandemic-layoffs shelve decades of management know-how. According to Pew Research Center, nearly 30m boomers will leave the workforce this year. That’s a lot of knowledge walking out the door. Worse, it’s a lot of tacit-knowledge, sometimes called tribal knowledge or organizational wisdom.
Unlike explicit knowledge, which is objective, easy to codify and store, tacit knowledge is subjective and a result of “experiential learning.” High-performing veterans have learned through observing others – or by going through their own hard-knocks. As a result, they know what to do when faced with scenarios that depart from the norm.
Further, companies are also faced with the reality that younger generations are more likely to change jobs more often than their older colleagues. So even if there has been a passing of the proverbial knowledge baton between boomers and their heirs, the retention of knowledge is even more precarious in the future.
KM Back in the Spotlight
The pandemic has seemingly brought Knowledge Management (KM) more into favor this year. At least in some departments. The Technology & Services Industry Association (TSIA) annually conducts a survey on knowledge sharing. A couple of interesting trends were noticed.
1. Companies are still trying to squeeze greater productivity out of staffers.
This year’s survey shows 26 percent of respondents said that knowledge management had the potential for improving productivity 40 percent or more. For support that number was slightly higher, at 28 percent. But for sales, 36 percent of respondents said an improvement of 40 percent or more was possible, including a total of 29 percent saying 50 percent or more.
2. Professional Services teams are motivated to share knowledge.
Professional services teams are encouraging employees to submit lessons learned and best practices. This “encouragement” jumped up from 53 to 71 percent. The actual percentage of companies who hold a formal project review meeting at the end of each engagement to capture shareable knowledge remained flat. On the other hand, only 4 percent of companies say they don’t capture any knowledge to share – compared to over 26 percent a year ago.
This is a strong indicator that professional services are embracing a knowledge-sharing and collaborative culture, even if processes are yet to be formalized.
This shouldn’t be surprising, says my Coveo colleague Juanita Olguin, senior product marketing manager, Workplace. “Professional services are tied to revenue – and they have every incentive to make sure it all goes well,” she explains.
Of course a key factor in knowledge sharing, regardless of the department, is culture. KM programs are asking people to change behavior, i.e., document and share all their hard-earned knowledge. As such, a strong knowledge-sharing culture is “a critical driver for success” for most companies, according to TSIA.
Technological advancements in areas such as cloud computing and artificial intelligence (AI) are helping organizations design and deliver more effective KM programs to better prepare for the evolving workforce. Technology-backed KM programs also help companies work more seamlessly interact with customers, from one employee to the next.
AI Expands KM ‘in Ways Not Yet Imagined’
UW Healthcare, an academic, not-for-profit medical center, serves 600,000 patients in South Central Wisconsin. The goal at UW Health was to provide remarkable patient care to patients and families. Healthcare workers are routinely searching for the best procedure or efficacy of drugs. According to Noah Locke, manager of web development, whose team is in charge of that search, 80 percent of users are looking for 20 percent of the content.
“The other 20 percent of our users are looking for these random things that are spread across this other 80 percent of content,” he explains “So you get the kind of long-tail effect where you can’t predict what someone’s going to be searching for you can’t predict how they’re going to search for it.”
AI can help organizations leverage KM to improve performance and increase innovation, while also expanding perspectives for both the employer and the employee. In UW’s case, AI helps with automatic synonym detection and understanding the provider’s context so that the most relevant information appears highest in the search results.
The Marriage Between KM and AI
For other organizations, AI-powered tools connect and combine knowledge across different platforms for employees to access as needed. The right AI tools can also track data and remind organizations when the information needs to be updated, such as a new protocol or changing customer preferences.
KM and AI have a lot in common, says industry expert Anthony Rhem, Ph.D. with the KM Institute. “KM and AI at its core is about knowledge,” he writes. “AI provides mechanisms to enable machines to learn. AI allows machines to acquire, process, and use knowledge to perform tasks and to unlock knowledge that can be delivered to humans to improve the decision-making process.”
Adds Rhem: “I believe that AI and KM are two sides of the same coin. KM allows an understanding of knowledge to occur, while AI provides the capabilities to expand, use, and create knowledge in ways we have not yet imagined.”
Growing Dedication to KM
While many organizations still lag in their KM practices, there are signs of steady improvement. According to the TSIA, KM programs are maturing, with a rise in dedicated staffing and program management, and more executives including KM metrics in operational reviews.
The TSIA says search strategies are also becoming more sophisticated. There is a rise in unified search — everything from content repositories and product documentation to learning content and community discussions.
“Intelligent unified searching adds natural language processing, artificial intelligence, and machine learning to this, so search results become more accurate over time based on which search results are accessed, and results are based on the intent of search terms, not just actual search strings,” it says.
The Future of Knowledge Management
With the rapid growth of the digital economy, especially amid the pandemic, KM has evolved from Tom Davenport’s classic mid-90s definition of being “the process of capturing, distributing, and effectively using knowledge,” to a much more modern-day Gartner description of “a discipline that promotes an integrated approach to identifying, capturing, evaluating, retrieving, and sharing all of an enterprise’s information assets.”
According to Gartner, as cited in a Knowledge Management World article, these assets may include “databases, documents, policies, procedures, and previously un-captured expertise and experience in individual workers.”
While Gartner’s version is certainly more tech-savvy, KM is still, at its core, a critical element for organizations to successfully pass information from one employee to the next and provide consistent customer service and stability in operations.
According to the American Productivity & Quality Center (APQC), an authority in KM and process and performance improvement, the top four components of KM are people, process, content/IT, and strategy.
“Regardless of the industry, size, or knowledge needs of your organization, you always need people to lead, sponsor, and support knowledge sharing,” the APQC states. “You need defined processes to manage and measure knowledge flows. You need knowledge content and IT tools that connect the right people to the right content at the right time. And finally, you need a clear and documented strategy for using KM to meet the most important and urgent needs of the business.”
AI is essential for effective knowledge management—to discover where it can be applied and how, read 4 Features You Need to Power an Intelligent Workplace.
And to learn even more about how to apply AI to make the most of your knowledge resources, read Intelligent knowledge experiences.