♦ Insights from previous scenarios to help interpret results and avoid repeating mistakes
♦ Standard practices and playbooks that drive consistent approaches
♦ Access to relevant expertise so they can validate insights and make better decisions (apqc.org/resource-library/resource/understanding-expertise-location-0)
When knowledge is embedded in these ways, employees are better equipped to draw insight from data and to use that insight to make better decisions.
AI Is Only as Effective as the Knowledge Behind It
Imagine a different scenario: An HR operations manager uses an AI assistant to interpret updated labor regulations related to overtime and scheduling. The system provides a clear recommendation, but it’s based on outdated guidance. Relying on that recommendation, the manager implements a new scheduling approach across the team.
The issue surfaces during an audit, where the organization discovers it is out of compliance. Correcting the problem means reconstructing records and adjusting payroll; the organization may even face financial penalties. Meanwhile, trust in the AI assistant evaporates across the team, undermining the value it was meant to deliver in the first place.
Scenarios such as this are far more likely when the content and knowledge supporting AI is incomplete, outdated, or poorly governed. Here are some of the common challenges within these environments:
♦ Concerns about whether AI-generated outputs can be trusted
♦ Inconsistent or incomplete data that limits the effectiveness of AI-generated insights
♦ Unclear use cases that make it difficult to apply AI in daily work
KM helps address these challenges by ensuring that the content and knowledge used by AI systems are curated, connected, and governed. It uses structured approaches to establish trusted knowledge sources, incorporate expert validation, maintain content quality across time, and retire outdated content.