New and emerging computing technologies have taken artificial intelligence (AI) and machine learning (ML) from potential game changers to genuine powerhouses of innovation. While there’s certainly a lot of hype to wade through before getting to its true capabilities, putting AI and ML into practical use for knowledge management purposes promises to transform organizational efficiency and competitiveness.
How We Consume Knowledge
It wasn’t that long ago that printed materials were an organization’s primary source of information. Along with internal company reports, handbooks and other corporate documents, people relied heavily on magazine articles, trade journals and other industry content to stay current with trends. The stack of printed materials also helped decision making around new products and services.
Today knowledge management (KM) systems make it possible for those same organizations to easily find, capture and share information. In my work helping companies implement knowledge management systems, the benefits are clear: faster access to knowledge and information, improved decision-making, promotion of cultural change and innovation, improved efficiency and increased customer satisfaction via an enhanced customer experience. Backed with strong data analytics, ML is revolutionizing how businesses automate processes, gain insight through data analysis and engage with employees and customers.
The Positive Effects Of AI And ML On Knowledge Management
KM’s primary goal is to improve an organization’s efficiency and save knowledge within the company. With their combined ability to collect and process large volumes of data at a faster rate, AI and ML are accelerating digital transformations across a wide range of industries. And since organizations that have already adopted a digital-first approach are more likely to focus on new and emerging tools and opportunities, they’re also transforming themselves into market leaders.
Many people might not realize that AI got its start in the 1950s. Over the years, natural language processing (NLP), text and speech recognition, and robotics made their mark on the technology. Things really took off, though, when ML came on the scene to help AI “learn” for itself, creating a prediction engine for business applications that minimizes errors and maximizes accuracy.
Beneficiaries of this new technology pairing include applications used for everything from medical diagnostics to autonomous driving, image recognition and, of course, knowledge management.
Find the Information Needle in a Data Haystack
Machine learning relies on huge volumes of high-quality data. But the appeal of KM is its promise to provide the right information, to the right people, at the right time, which usually means now. This system for timely delivery of information has widespread implications for the systems required to support it. How does one determine the “right” information? How do you know who should receive it? How can preferences, context and information requirements be met?
The answer to these questions is knowledge management. There are different types of KM systems out there, but AI and ML helps each of them perform better and provide you with the information needed at the moment. AI and ML help KM in four key ways.
Simplify Knowledge Discovery
Over the past two decades, a number of tools such as cloud drives and wikis have been developed to simplify capturing and sharing knowledge. But as the amount of data grew, discovering knowledge became increasingly difficult. Solutions like knowledge base storage, detailed hierarchies and tagging data helped, but none offered a permanent solution.
AI and ML can help solve the problem by using the latest technologies to simplify knowledge discovery and make it easier for people to find the knowledge they’re looking for quickly and easily. ML can monitor and learn from what all employees search for before using that to predict and deliver the relevant information at the moment it’s needed.
Bring Data Together
Sales teams manage knowledge in a CRM while HR uses a secured intranet portal. Support teams capture and share knowledge in ticketing systems, while product teams use project management tools. Information silos create other problems, such as employees not knowing where to find the knowledge they need. AI and ML work together to connect and combine knowledge across multiple systems for everyone to access.
Keep Content Up to Date
Knowledge maintenance involves adding reams of new information to an organization’s knowledge base. Over time, some if not most of that information becomes outdated. Retaining outdated knowledge can have a negative effect, especially when errors are made because incorrect information was used to solve an issue. AI and ML solve this problem by generating regular reminders for users to regularly update saved knowledge.
Leverage Important Metrics
Organizations often want proof that their KM system is delivering on its promises. This has typically been done by surveys, but they aren’t always complete or accurate as people often remember things less clearly than they believe. AI and ML offer metric tracking capabilities that reveal exact, definitive performance data businesses can use to measure everything from participation rate to first call resolutions.
Machine learning and AI are helping to address modern KM challenges by making content more easily discoverable and shareable. Integrating AI and ML into a knowledge management system builds intelligent searches that result in greater productivity while improving how content is utilized. We’ve come a long way from referring to printed materials to answer questions and solve problems. AI and ML are two powerful tools that continue to evolve and affect knowledge management as well as a company’s success.
