Data Analytics · Read + Write

Data → action plans & tickets

Augment Rep · High Reason · High

The problem

Insights die in dashboards. The analyst sees a problem, writes a Slack message, no one owns it, no one tracks it. The same finding shows up next week.

The AI approach

Findings → owned tickets. AI clusters anomalies, ranks by impact, drafts a fix-plan with hypothesis and acceptance criteria, and routes it to the right owner using a RACI map.

The outcome

~0 dropped

Findings become Jira tickets with owners and acceptance criteria. Closure rate goes from ~20% → ~80% within 30 days.

Try itInput → Process → Output

Input — analytics findings (last 24 hrs)
Weekly sales reviewauto-summarised from BI
⚠ severe store_07: revenue −22% WoW; fresh category down 38%, others flat. Coincides with new opening hours.
store_07 · fresh · −22%
watch online checkout: drop-off at payment step rose from 4% → 9% over 3 days. Affects ~₿340k/day.
web · checkout · −5pp
opportunity SKU CL-117: searches +180% with no stock in 4 stores. Margin 28%.
SKU · CL-117 · gap
Process — AI pipeline
1Cluster + rank by impactReadPredictive
2Draft hypothesis + planWriteGenerative
3Route via RACI mapRulesSymbolic
4Create ticket via APIWriteSymbolic
Output — Jira tickets ready for review
Click Run demo to turn the findings into routed action tickets.
SALES-2451 P1 · severe
Diagnose fresh-category drop at store_07
Hypothesis: new opening hours misaligned with morning fresh-shopper peak. Plan: (1) compare hourly fresh-revenue pre/post change, (2) interview store mgr on stocking schedule, (3) propose AM-shift adjustment. Acceptance: revenue gap closes <5% within 14 days.
OwnerPL ploy.sDue 14 MayImpact ₿1.2M/mo
WEB-882 P2 · watch
Investigate checkout drop-off spike
Hypothesis: new payment-page deploy 3 days ago. Plan: (1) funnel by browser/device, (2) check payment-gateway error rate, (3) rollback if confirmed. Acceptance: drop-off back to ≤5%.
OwnerTT tontonDue 6 MayImpact ₿340k/day
MERCH-712 P3 · opportunity
Reorder SKU CL-117 to 4 stores
Plan: 180% search lift, no stock. Replenish A,B,F,K stores; review next week. Acceptance: on-shelf in 4 stores by 10 May.
OwnerEH earthDue 10 MayMargin 28%

Three AI types in this use case

SymbolicRACI map (metric → owner); ticket schema (Jira API); de-duplication windows; SLA gates.
PredictiveCluster related findings; impact ranker (magnitude × baseline × decay); priority scoring.
GenerativeLLM drafts hypothesis + step-by-step plan + acceptance criteria + due date for each ticket.

The stack

  • Findings source · BI alert API + dbt tests
  • Ranker · impact = magnitude × baseline × decay
  • Drafter · LLM with RACI + style prompt
  • Sink · Jira / Asana / Linear API

When this works

  • Findings have structured metadata (entity, metric, delta)
  • RACI / ownership map exists for the data
  • Tickets land in a board with active SLAs
  • Owner reviews + edits — not auto-assigned without consent

When it fails

  • Same finding fires every week → ticket fatigue (need de-dup)
  • Owner ambiguous → ticket lands with team lead, who re-routes
  • Hypothesis treated as conclusion — culture problem, not AI
  • Tickets created on noise — false-positive rate matters