AI in Action: Transforming Knowledge Capture and Retrieval

AI is poised to fix a core knowledge-management bottleneck: turning chaotic, hard-to-retrieve information into accurate, timely answers—without abandoning the human, process, and cultural foundations that make knowledge work. The central message is that generative and agentic AI can shift knowledge retrieval from keyword searching to natural-language question answering, while also accelerating knowledge capture through automated extraction, structuring, summarization, and content curation. The payoff matters most for organizations drowning in information overload, siloed knowledge, and the risk of losing critical know-how as experienced staff retire.

APQC’s knowledge-management priorities set the context: practitioners are prioritizing operational efficiency and process improvement, plus digital transformation and building an “intelligent enterprise.” AI is on the radar not as a standalone upgrade, but as a way to scale KM value—surfacing the right content when people need it, improving productivity, and addressing looming knowledge gaps from mass retirements. Within that framing, Badu Busouso’s story ties AI adoption directly to knowledge flow: people, processes, and technology create value, while culture acts as the undercurrent that determines whether knowledge systems actually stick.

Busouso lays out the “situation” KM faces today: too much information, siloed access, difficulty capturing tacit knowledge, limited availability of relevant information for decision-makers, and inconsistent or low-quality content. Traditional KM approaches often rely heavily on manual processes and keyword search, which becomes increasingly inadequate as content volume grows. That leads to a “search paradigm shift”—from keyword search to AI-driven retrieval using natural language processing and semantic/intelligent search.

The benefits he highlights are practical and operational. AI can mine data to find hidden patterns, automate content curation across the content lifecycle, and improve accuracy and consistency by consolidating and cross-checking information. For knowledge capture, AI can extract content from documents and emails, structure unstructured material, and support knowledge graphs that reveal relationships between topics. For retrieval, AI can summarize large knowledge bases, answer questions directly, filter for relevance and freshness, and tailor delivery to individual needs and learning styles.

But the emphasis stays on governance and readiness. AI is not a replacement for human judgment; it should support decision-making and critical thinking. Success depends on key performance factors such as accuracy, reliability, responsiveness, efficiency/productivity gains, findability, and—crucially—trust. Security and privacy require due diligence, and adoption needs change management from day one. Content quality is treated as the gatekeeper: “garbage in, garbage out” means organizations must manage content health (avoiding rot such as redundant, obsolete, or trivial material) through ongoing review and information architecture.

Busouso also argues that AI cannot work well without the scaffolding of information architecture—taxonomies, metadata, and knowledge/knowledge graphs that make relationships legible to AI systems. Prompt writing is framed as “secret sauce,” requiring training so users can ask for the right outputs and validate results with quality assurance.

The integration path is deliberately incremental: identify a knowledge gap, define use cases, prepare content, explore AI capabilities, pilot for quick wins, and expand based on feedback loops. The final guidance is blunt: don’t boil the ocean, don’t nibble around the edges, and don’t sit out the AI shift—learn it, pilot it thoughtfully, and keep content and human oversight at the center.