Frequently Asked Questions
What exactly is “wisdom,” and why is it so hard to capture?
Wisdom is the highest form of applying tacit knowledge; the unwritten, experience-built know-how people carry. This includes things like shortcuts, signals, timing, and micro-adjustments that don’t fit neatly into documents. Most people never record it because articulating it is hard, they don’t realize they rely on it, or it functions as job security. The result: the parts of work that matter most rarely survive turnover.
Why are companies losing more institutional wisdom than ever before?
59% of today’s workforce will be retired, unemployed, or working elsewhere within five years. Median tenure is under four years. These two dynamics are creating the largest loss of institutional wisdom in modern history. When people leave, so does the operating logic that keeps companies running.
Why don’t traditional knowledge management tools solve this?
Knowledge platforms capture content, not context. They store documents, but they don’t capture how experts notice cues, weigh constraints, or adjust their approach under pressure. AI search improves retrieval, but it only works with what humans have taken the time to write down. Tacit expertise almost never makes it into the system.
What’s the “Wisdom Gap”?
It’s the space between what organizations store and what they depend on. Explicit knowledge is easy to capture; tacit wisdom requires high effort and high trust. Most tools operate on the low-value side, while real performance lives on the high-value side.
Why is wisdom so central to performance?
Because high performance is a judgment problem. Expert judgment draws on multiple layers of information from cues and exceptions to social modeling and reframing. Novices operate mostly in the lower layers (data, patterns). Experts rely on the upper layers that are invisible, unrecorded, and rarely transferred.
Can’t AI capture wisdom on its own?
Not yet. AI is strong at analyzing explicit knowledge, finding patterns, and reconfiguring information. But it struggles with more “human” tasks such as sensing cues, applying tacit memory, exception handling, and managing social dynamics. These elements depend on human experience. AI can scale wisdom only after the wisdom has been extracted and structured. Without that, AI just amplifies partial knowledge.
How do you capture wisdom in practice?
Organizations first identify the capability they want more people to masters such as negotiation incident response, executive engagement, etc. Then they identify the employees who already model that capability.
Once the focus and experts are clear, the process is simple and structured:
1. Identify
Choose the capability to deepen across the organization and select the employees who consistently demonstrate it.
2. Harvest
Run event-based interviews that surface the signals, decisions, exceptions, and reasoning experts use in real situations.
3. Reveal
Synthesize and test what’s transferable. Separate the individual quirks from the actionable, high-value judgment others can apply.
4. Package
Turn the validated wisdom into content that can be plugged into existing tools and programs (onboarding, playbooks, enablement modules, AI-ready libraries, etc.) so it can be applied at scale.
How is this different from communities of practice?
Communities depend on volunteer energy and eventually lose steam. Wisdom managment doesn’t rely on enthusiasm; it produces validated, permanent assets that survive turnover and don’t degrade over time.