Can AI learn how our best employees make decisions?
AI can help capture which evidence an experienced employee uses and why. Ask them to explain a recent hard case while the records are open, preserve exceptions and disagreement, then compare their reasoning with later results. An expert can still be wrong, and AI cannot recover a reason nobody recorded.
Illustrative example: A senior employee approves an unusual supplier. The record says who was approved, but the useful knowledge is why: which requirement the supplier missed, what evidence justified an exception, who accepted the risk and what condition would change the decision later. Ask the employee while the case is fresh; AI cannot recover a reason nobody recorded.
Learn from experienced employees while they are here
Choose a recent hard case and ask the employee to explain the records and rule they used, the alternatives they considered and what they were unsure about. Keep disagreement and exceptions rather than turning one person's habit into a company rule. Later results can challenge the explanation, including when the experienced employee was wrong.
A document library is still valuable. It holds policies, examples and shared procedures. A decision record adds the link between a particular case, the rule in force then, the person's judgment, the authorized action and the later result. Tacit knowledge is know-how someone may struggle to explain; interviews and mentoring can reveal some of it, but no system captures every unspoken thought.
How do we keep company knowledge when employees leave?
Keep selected decisions with their reasons, source dates, exceptions, unresolved work and the next owner. A folder of old documents may tell a successor which rule existed without showing how a difficult case was handled.
| Handover item | What the successor can check |
|---|---|
| Important decisions and reasons | Which evidence and rule supported an exception? |
| Source records and dates | What was known then, and what has changed? |
| Disagreement and open cases | Who must resolve a conflict or continue the work? |
| Later results and proposed lessons | Did the outcome support a reusable approach, or only suggest one? |
Ask the departing employee to walk through a few cases with the records open. Have the new owner try to explain the decision back, identify a missing fact and name the next check. That exercise exposes a gap sooner than an archive search months later. The Decision Traces glossary shows what an inspectable choice can retain.
Check a lesson before reusing it
A favorable outcome after an exception is evidence to examine, not proof that the exception caused success. Compare the original expectation with the later observation and ask whether the source, policy or population changed. A proposed lesson for another team needs its own review and tests. Company intelligence can improve without sharing customer data explains the difference between retained history and tested improvement.
The published Decision Traces study connects historical hiring and post-hire production records for 10,765 insurance agents at one Fortune 500 insurance carrier. It shows why linked outcomes can challenge a familiar hiring filter. It does not establish that the study recorded every interview, manager reason or override. The supplier handover above is illustrative; it does not describe fields in that study.
Nodes Engine connects permitted records, human judgment, decisions and later results in the company context graph. A Decision Trace makes the history inspectable. Named people still own consequential choices; routine steps may use permission already given within the current scope. Nodes Connector supplies tested access, and AI FDE helps configure the work. A retained record supports evaluation, but it does not automatically teach a better rule.
Start with one decision a successor would struggle to explain. Preserve its sources, reason, owner and open questions before trying to capture a whole team's knowledge.
Sources
Saad Bin Shafiq is the founder of Nodes, serving data-sensitive enterprises. Methodology: Decision Traces.