Application of AI for Knowledge Management
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Application of AI for Knowledge Management

Carla O'Dell, Chair of APQC and Tom Davenport, Distinguished Professor of Babson College
Carla O'Dell, Chair of APQC

Carla O'Dell, Chair of APQC

As the artificial intelligence arms race heats up, enterprise knowledge management (KM) is the beneficiary. Amazon, Google, Microsoft, and Facebook are vying to own the data, analytics, cloud, and machine learning ecosystems that determine how we work, travel, shop, and--most importantly for organizations creating and sharing knowledge—connect, communicate and search.

The goal of KM is to connect employees to the right information (and the right people) at the right time to be more productive and make better decisions in the flow of their daily work. To accomplish this today, most KM programs devote staff to categorize and curate documents to improve search results. They rely on employees to self-report and update their interests to identify experts and others with similar interests. 

  Three aspects of KM turn out to be a good target for analytics and AI: automating or augmenting behind-the-scenes KM tasks, enhancing the employee experience and enabling new capabilities​  

AI and machine learning offer a welcome way to automate some of these tasks and to augment what KM can do. It used to be that you had to sign a six-figure check to even experiment with something like IBM’s Watson. Now, thanks to cloud platforms from Microsoft, Google, IBM, and many others, KM programs can experiment with AI and machine learning tools at a low cost and launch proof-of-concept projects without making a huge up-front investment. 

Three aspects of KM turn out to be a good target for analytics and AI: automating or augmenting behind-the-scenes KM tasks, enhancing the employee experience and enabling new capabilities. 

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Top Knowledge Management Solution Companies

Automating Routine KM Tasks

The first way KM can leverage AI is to have software do even more of the lower-value routine analysis and categorization involved in harvesting, organizing, processing, and delivering relevant content. Most organizations have piles of project documentation and records that no person could ever hope to sift through. We’ve seen some great pilots for automating back-end KM processes, such as using autoclassification to assign metadata to files and natural language processing (NLP). NLP is software that enables computers to understand written or spoken human language, translate and detect patterns. Robotic process automation (RPA) can automatically translate between languages or to assign documents to various communities of practice. AI software can help clean up content repositories by flagging items that should be updated, retagged, or archived.  Tom Davenport, Distinguished Professor of Babson College

For a large organization, machines and coding will often be better and less expensive than having people do these routine, high-volume tasks. KM programs are chronically understaffed and not likely to fear the automation of routine jobs. This will allow the KM team to spend more time on the “high touch” and higher value-added work of bringing people together to solve strategic business issues and problems. 

Enhancing the User Experience

Employees want to have the same slick digital consumer experience at work they have come to expect at home. Fortunately, many AI applications focused on customers can do double-duty as KM enhancers. For example, a KM team at a large heavy equipment manufacturer embraced the lessons and software from a customer-facing chatbot and put it to work in their internal communities of practice. 

We all leave a trail of digital breadcrumbs as we compose reports and documents, search, send an email, and text/chat and collaborate online. Smart software can analyze this data to create summaries, identify relevant topics, sanitize proprietary and sensitive information, and pinpoint reusable knowledge nuggets. Programs such as Google Cloud Search can find documents across G Suite enterprise applications and, using NLP, advanced analytics and algorithms, deduce patterns about what and whom they know and what they are trying to accomplish making the search results far more useful. 

MITRE, a federally funded R&D center, has been using proprietary versions of data management and machine learning for years to connect people working on similar projects or wrestling with similar technical problems. MITRE also drives better search results based on digital knowledge of where, when and what people are working on. To enhance employee experience and learning, MITRE also anticipates what project managers and team members will need at each stage of a project and spoon-feeds them templates, documents or friendly nudges at the right time. As a result, new project managers get off to a better start and stay on track. 

Enabling New Capabilities and Revealing Hidden Potential 

AI software not only shows great promise in improving findability and suggesting resources based on a user’s context, but it also becomes really powerful when it connects employees with colleagues who are doing similar work in other units or locations. Innovation comes from the intersection of disciplines and problems. By bringing together people working on the same problems—and who might not have found each other otherwise-- organizations can avoid reinvention, surface-related efforts and certainly help make innovation more likely.

KM teams can also use NLP and analytics to spot hidden trends by analyzing employees’ discussions in enterprise chat systems and community sites—as well as employee/client interactions from call transcripts, customer service chat systems, and CRM systems—to pinpoint emerging knowledge needs. In doing so, the KM team can cultivate the most in-demand content and deliver knowledge at employees’ most teachable moments. 

How CIO’s can make the most of AI and KM 

Smart CIOs put KM-savvy experts on any team evaluating new technologies so the knowledge needs of employees can be considered along with customers. Grant Thornton’s KM team decided not to let a “good transformation” pass them by. The KM team joined the firm’s transformation team. They looked at the way consultants did their day to day jobs and figured out how, by adapting technologies being used for other purposes, they could reduce cycle time to find content and expertise.

Knowledge managers bring a lot of change management skills to the table when it comes to digital transformation. They know what knowledge workers need and have the change management expertise to get employees to actually use new digital approaches. 

Whether your organization is deep in the throes of AI-driven digital transformation or just dipping its toe into the water, KM should be part of the strategic conversation about AI’s role in the business. 

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