Task-Boundary Mode How task-instance boundaries are drawn from the event stream. Applies to every Task SoP, Step SoP, and Variants view.

Opportunity Lifecycle

1
Surfaced
2
Accepted
3
Remediating
4
Remediated
Status persists in your browser. In production, these actions notify team members, trigger workflows, and begin value-realization monitoring.
Calibrating — continuous monitoring active awaiting more data The pattern behind this finding has been detected, but the sample size within the 19-day pilot window is insufficient to produce a confident savings estimate. The agent continues to collect evidence — savings projections will appear as more data accumulates.

Description & Data Evidence

This variant cluster outperforms the current mined reference path for Generate Narrative (AI-assisted) by 20.0 adherence points across 31 instances. Consider updating the documented SoP to match — and aligning customer-provided SoPs accordingly.

Self-Evaluation Scores

The platform grades each finding on four dimensions (1–5 scale). Low scores flag findings that need more data or clearer remediation before acceptance.

Overall 3/5
Actionability 3/5
Specificity 3/5
Remediation Alignment 3/5
Source Task SoP: Narrative → Generate Narrative (AI-assisted) → See the full variant detail (sequence, narrative, AI explanation, application journey, user cohort) in context.

Key Findings

  • Observed sequence: Generate Narrative from Outline
  • Reference sequence: Generate Narrative from Outline → Open Details Section → Navigate to Cases → gpteal.merck.com → Microsoft Word → Select Patient → Microsoft Word
  • Genuine Complexity — this sequence differs from the reference path (1 sub-steps vs 7 in reference) but shows no strong pain-point signals: adherence score 82.5 vs reference 79.4 (delta +3.1). Likely a legitimate case-complexity path — not an automation candidate, but worth cataloging.

Remediation Ideas

Implementation Roadmap

Effort
Small
Timeline
4-8 weeks
Primary Owner
Engineering (UX)
Dependencies
  • Veeva Safety UI customization framework
  • User acceptance testing

How Risk-Adjusted Savings Is Calculated

The risk-adjusted number is the annual savings multiplied by a composite factor of four independent dimensions. Each dimension is rated High (1.0×), Medium (0.8×), or Low (0.5×). See full methodology.

Detection 40% weight
Medium
Confidence the agent-detected pattern is real
Feasibility 25% weight
High
Ease of building the remediation
Adoption 20% weight
Medium
Likelihood users change workflow
Compliance 15% weight
Medium
Simplicity of PV validation path
0 hrs × 0.85 = 0 hrs / year
At 1,000 users: 0 hrs / year · $0.0M