Proactive intelligence must do more than detect a threshold breach or unusual pattern. It must investigate the signal against relevant business context, test candidate explanations, surface supporting and conflicting evidence, and state what remains uncertain.
Without that step, an organisation risks automating the distribution of unexplained alerts. The result is more noise, not better decisions.
A worked example: rising order delays
Imagine an operations team sees an increase in delayed orders. Several explanations are possible: a supplier is dispatching late, a carrier is missing collection windows, stock is unavailable, or event data is arriving late.
The initial signal should trigger a bounded investigation, not an immediate accusation.
| Candidate explanation | Evidence to examine | Evidence that may weaken it |
|---|---|---|
| Supplier dispatch delay | PO dates, promised dates, dispatch records | Supplier dispatch occurred on time |
| Carrier disruption | Collection and transit scans, route events | Carrier timestamps are missing or stale |
| Inventory shortage | Available stock, allocations, inbound receipts | Stock was available at order release |
| Data freshness issue | Feed timestamps, missing events, refresh logs | Independent sources confirm the delay |
| Definition change | SLA rules, calendar and grace-period changes | Same definition applies across periods |
The investigation should report which explanations are supported, which are not, and which cannot be assessed because evidence is missing.
From trigger to evidence-backed investigation
- Start Trigger: delay rate crosses threshold
- Validate baseline and data freshness
- Identify affected orders and time window
- Gather supplier, inventory and carrier evidence
- Generate candidate explanations
- Check supporting and contradicting evidence
- Evidence sufficient for a conclusion?
- Yes Rank explanations and show provenance.
- No State the uncertainty and request review.
- Recommend a bounded next step
- Outcome Owner acts and records the outcome
It can establish that an order was delayed if the records support it. It should not claim a supplier caused the delay while the event history is incomplete.
The workflow needs an explicit boundary. It can establish that an order was delayed if the records support that conclusion. It may identify evidence consistent with a supplier delay. It should not claim that a supplier caused the delay if the event history is incomplete or another explanation remains plausible.
What a good investigation record contains
A useful record should preserve the trigger condition, time window, sources examined, entities linked, business definitions, evidence for and against each explanation, confidence or review status, and the next action. It should also make clear which sources were unavailable.
This record allows the business to challenge the conclusion and improve the process. If a domain expert rejects the proposed explanation, the correction should be captured in a controlled way rather than disappearing into a chat transcript.
Why cross-system context matters
The trigger may be visible in one system while the explanation sits elsewhere. Order data may show a delay, inventory data may explain a stock constraint, and carrier events may reveal a hand-off problem. Investigation quality depends on the ability to relate the records and compare their timestamps and definitions.
Qwry.AI’s platform is built around connecting data sources, resolving relationships and answering questions with traceable query evidence. That foundation is relevant to proactive investigations, but a production workflow also needs validated triggers, monitoring cadence, ownership, exception handling and permissions.
Next step: Select one recurring alert and document three plausible explanations, the evidence needed to test each, and the conditions that should force escalation. That is a more reliable starting point than automating the alert itself.
FAQ
Frequently asked questions.
AI can generate and compare candidate explanations, but the reliability of a root-cause conclusion depends on evidence coverage, temporal detail, business rules and alternative explanations. Some cases will require a human investigation.
An anomaly is a deviation from an expected pattern. A root cause is an explanation supported by evidence that accounts for the event. An anomaly alone does not establish its cause.
The system should state the limitation, reduce the strength of its conclusion or abstain. It should not silently treat missing evidence as proof that an event did not occur.
Use reviewed outcomes to improve definitions, thresholds and investigation paths, with versioning and governance. Do not automatically convert every previous recommendation into a permanent rule.