WHITE PAPER
Relevance Engineering
AI has raised the bar on relevance - organizations have more information, and more ways to surface it, than ever, and it is still hard to get people to what actually matters.
Discover how Relevance Engineering brings knowledge, context and intent together to improve decisions and create more effective AI-powered experiences.
- The five operating pillars of Relevance Engineering
- How AI exposes weaknesses in content, metadata, taxonomy and governance
- How to measure decision friction and knowledge effectiveness beyond clicks and views
- How an enterprise relevance layer connects fragmented systems and experiences
- Why relevance is becoming an operational leadership problem
- Shift from managing information to improving its relevance
- Align search, knowledge management, AI, customer experience and governance around shared outcomes
- Give AI stronger context through better taxonomy and metadata
- Evaluate whether people can complete tasks and make decisions, not just whether they click
- Connect fragmented systems to create more consistent AI and digital experiences
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