Governing human and machine learning enterprise knowledge at KMWorld 2025
As the volume of enterprise knowledge continues to grow exponentially, traditional human tasks are increasingly being performed by AI, taxing the limits of KM. As a result, leaders and decision makers have far less visibility into how and where their organization’s knowledge is generated, along with its validity.
At KMWorld 2025, Art Murray, CEO, Applied Knowledge Sciences, Inc., held a workshop discussing “Building a Governance Model for Enterprise Knowledge.” This interactive workshop covered how to build a top-level governance model, along with a plan for implementation, including how to measure results and adjust along the way.
How many of your day-to-day decisions are automated? How many business rules does your organization have? How secure are they? What social amplification and other risks are inherent in your organization’s decision processes? If you can’t answer these questions, it’s a sign you need to start incorporating knowledge governance into your organization.
“We say, ‘enterprise knowledge’ and there’s a collective of human knowledge in there,” Murray said. “Generative AI is all the rage, but it’s just one piece of AI. It does not include other aspects of AI.”
The problem is there’s so many automated processes, it’s sucking in data from all different places within the organization, Murray explained. It’s all about the right data to give the answers that are needed.
“Part of the thought process is where do these rules/algorithms come from and how do we capture this knowledge,” Murray said. Not having clear answers opens companies to liabilities, risks, and missed opportunities. Making sure employees are sharing knowledge with each other is a big issue as well.
A mentoring system is one way to keep knowledge transfer between employees flowing, Murray noted. A universal notebook that is shared within the organization that has employees describe their jobs and their duties is another way to capture knowledge.
“Understanding and managing risk is the key to resilience,” Murray said.
When change is needed in an enterprise, the change cycle can be used, he explained. It is comprised of several steps including disruption, investigation, ideation, experimentation, affirmation, and automation.
“Understand what it is that’s driving this change,” Murray said. “How do we apply this to our organization? Let’s come up with some ideas.”
In decision processes it’s useful to work out an option outline, which maps out why a specific solution was chosen.
Achieve greater performance and value from KM by putting a formal governance model in place, reducing risk and uncertainty, and gaining greater value from the organization’s ever-expanding collection of human and machine knowledge assets, Murray said.
Good governance should consist of 8 attributes:
- Rule of Law
- Transparency
- Responsiveness
- Consensus-oriented
- Equity and inclusiveness
- Effectiveness and efficiency
- Accountability
- Participation
“You want to have a lot of minds in the room from a lot of disciplines,” Murray said. “Discourse is important.”
The 7 facets of knowledge governance include:
- Roles and responsibilities clearly defined
- Competencies required for each role are known, developed, and nurtured
- Process for knowledge vetting and assurance are in place, understood, and practiced
- Links to organizational performance are known and measured
- Training plan formulated and content developed
- Evangelize/socialization process in place
- Reward/recognition systems in place
“There are a hundred different ways to do things, meet people where they are,” Murray said.
KMWorld returned to the J.W. Marriott in Washington D.C. on November 17-20, with pre-conference workshops held on November 17.
KMWorld 2025 is a part of a unique program of five co-located conferences, which also includes Enterprise Search & Discovery, Enterprise AI World, Taxonomy Boot Camp, and Text Analytics Forum.