Emerging strategies, sentiments, and solutions for KM and AI

Fundamentally, knowledge management maintains a core focus on connecting people with the right data when and where they need it. However, closing knowledge gaps—often induced by data silos, inaccurate and outdated data, as well as lack of user adoption and ease of use—continues to prove a significant challenge.

John Chmaj, senior director, KM strategy at Verint, and Colin Kennedy, COO and co-founder at Shelf, joined KMWorld’s webinar, Rethinking KM for Agility, Efficiency, and Innovation, to explore recent advancements in AI and related technologies that propel KM strategies toward their central goals.

“Like everyone else, we [Verint] have been impacted by AI and its capabilities,” noted Chmaj. “It’s a whole new world where there’s intelligence and automation in lots of new places.”

This new paradigm of infusing AI and ML into nearly everything calls for new KM strategies.

At Verint, new strategies maintain its core philosophy, where anything they do is to reduce effort, time, and cost required to meet increasing customer interactions and expectations. Verint’s CX automation delivers a variety of key capabilities that imbed intelligence into knowledge management, including:

• AI-infused search automation for contextual knowledge discovery
• Content ingestion
• Authoring automation
• Support task automation
• Self-service automation
• Continues improvement

Verint’s Knowledge Creation Bot—which powers several of the above automation features—enables enterprises to leverage generative AI (GenAI) to quickly summarize long, complex documents, shortening authoring time.

Additionally, Verint offers a Knowledge Suggestion Bot—which eliminates the need for manual knowledge search by delivering relevant, real-time knowledge into an agent’s workflow—and the Knowledge Answer Bot—which pairs GenAI with Verint Cognitive Search to provide the right answer across multiple documents.

Kennedy emphasized that “KM… [is the] backbone for being able to successfully implement generative AI,” adding that knowledge management is “more important than ever.”

This is due to data entropy—or the gradual decline in quality of organization's data and content. To deliver AI value through knowledge, data must be clean, accurate, and current at all times. Kennedy put it succinctly: If you put garbage into the AI, you’ll only ever get garbage out.

“If you have low-quality knowledge, you’re going to have business issues,” Kennedy added, including:

• Long answer times
• Wrong actions taken
• Unwarranted escalations
• Disillusioned employees
• Customer dissatisfaction
• Suffering brand reputation
• Factual inaccuracies
• Risk of toxic/biased outputs
• Privacy or compliance violations

Fundamentally, applying tried and true KM principles as an AI infrastructure improves AI inputs and outputs. Uniting content under a semantic layer, enterprises should then process that content with analytics, governance, automated maintenance, and content intelligence tools, before it ever touches the AI solutions in place—whether it be a copilot, agent assistant, or a bot.

Shelf provides that exact product suite, delivering next-gen KM strategies that enrich, enhance, and monitor unstructured data with the flexibility necessary to support current and culture knowledge-related initiatives, according to the company.

Chmaj and Kennedy then engaged in a discussion about re-thinking KM, examining resources and competencies, knowledge architectures, governance, analytics, and more.

To view the full, in-depth presentation on rethinking KM strategies, you can view an archived version of the webinar here.