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Dashboard / AI Discovery / GenAI Narrative Drafting
Opportunity Surfaced
High Impact AI Agent

GenAI Narrative Drafting

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.

★ Savings Opportunity

Assumes $75/hr fully loaded cost. Pilot: 19 days. See methodology.
Pilot Period (19d)
1 hrs
Annual (17 users)
17 hrs
$1,305
Projected (1,000 users)
1,024 hrs
$76,762
Risk-Adjusted (17 users, annual)
15 hrs
0.84× composite · see breakdown ↓

Automation Readiness Score

47
Medium
Pattern Frequency 17 hrs/yr (17 users)
Decision Complexity Judgment-heavy, probabilistic
Data Structure Unstructured judgment
Cross-App Scope Single application scope

Description & Data Evidence

Users spend avg 15.0s per interaction on description fields in Veeva. Peak dwell 79.8s (SUSAR). 'Generate Narrative from Outline' exists but has 0ms dwell = background trigger, after which users heavily edit. ~1,024 hrs/yr at scale.

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 5/5
Actionability 5/5
Specificity 4/5
Remediation Alignment 5/5

Key Findings

  • Avg dwell on description fields: 15.0s per interaction
  • Peak dwell on SUSAR narratives: 79.8s
  • Total judgment-heavy hours (pilot): 1.3 hrs across 17 users
  • Annual projection (17 users): 17 hrs
  • Annual projection (1,000 users): 1,024 hrs
  • 'Generate Narrative' triggers exist but output requires extensive manual editing

Case Evidence

Specific case IDs pulled from the pilot data where this pattern is most pronounced. In production, clicking a case opens its full event timeline.

Case ID Signal Context
2317182 28 narrative events 12s focused dwell
2347840 19 narrative events 70s focused dwell
2281696 19 narrative events 15s focused dwell
2281543 16 narrative events 55s focused dwell
2347710 15 narrative events 2s focused dwell

Validation Questions

0 of 3 answered
Before accepting this opportunity, work through the questions below with the relevant subject-matter experts. Your answers lock in the acceptance criteria and — when you toggle Share with Pyze — inform how our agents surface similar patterns in the future.
1
What specific sections of the auto-generated narrative are users most frequently deleting or rewriting?
Identifies exactly where GenAI output falls short so we can target improvements.
2
Are users drafting narratives in external tools (e.g., MS Word) and then pasting them into Veeva?
Confirms whether the issue is the editor UI or the quality of AI-generated content.
3
Are there specific regulatory requirements for SUSAR narratives that prevent more aggressive auto-population of the field from structured data?
Determines the upper bound on automation before human review is mandatory.

Remediation Ideas

  • Deploy LLM-powered narrative drafting that pre-fills from structured case data
  • Auto-generate SUSAR/SAE narrative sections from source document entities
  • Provide AI-assisted editing with PV-specific language models
  • Integrate iterative refinement: draft -> review -> AI-revise cycle

Implementation Roadmap

Effort
Large
Timeline
4-6 months
Primary Owner
AI Platform + Regulatory
Dependencies
  • GenAI model selection + procurement
  • Regulatory validation plan for AI-generated content
  • Gold-standard narrative corpus for evaluation
  • User-facing review workflow design
Phased Delivery
  1. Model selection & validation plan (3-4 weeks)
  2. Prompt engineering + evaluation harness (4-6 weeks)
  3. Pilot with human-in-loop (6-8 weeks)
  4. Regulatory sign-off + broader rollout (4-6 weeks)

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
High
Confidence the agent-detected pattern is real
Feasibility 25% weight
Medium
Ease of building the remediation
Adoption 20% weight
Medium
Likelihood users change workflow
Compliance 15% weight
Low
Simplicity of PV validation path
17 hrs × 0.84 = 15 hrs / year
At 1,000 users: 860 hrs / year · $0.1M