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Dashboard / Rework / Acrobat-Word Narrative Drafting Loop
Opportunity Surfaced
High Impact Integration

Acrobat-Word Narrative Drafting Loop

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)
12 hrs
$938
Projected (1,000 users)
735 hrs
$55,148
Risk-Adjusted (17 users, annual)
12 hrs
0.97× composite · see breakdown ↓

Description & Data Evidence

Users switch between Acrobat and Word every 8-11s during narrative drafting, generating 427 rapid transitions across 104 cases in the pilot. This copy-paste-verify loop between source PDFs and Word narratives is a prime candidate for an integrated narrative drafting tool.

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

Key Findings

  • 832 total Acrobat<->Word transitions detected, 427 within 30s
  • Median gap between switches: 8.0s (avg 10.7s)
  • 104 cases and 7 users affected
  • Estimated 0.0 hours of dwell time in these ping-pong sessions

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
2353948 216 events Across 3 apps
2335834 198 events Across 3 apps
2349955 181 events Across 2 apps
2317182 180 events Across 1 apps
2355714 172 events Across 3 apps

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
Why are users drafting narratives in local Word instances instead of the built-in Veeva Narrative editor?
Editor limitation, formatting, or collaboration habit — each points to a different fix.
2
What specific data points are most commonly being 'looked up' in the Acrobat PDFs?
Determines what AI-assisted extraction would need to pre-populate into the narrative.
3
Is there a specific type of source document (e.g., CIOMS forms) that triggers the highest frequency of switching?
Lets us prioritize the AI extraction rollout by document type.

Remediation Ideas

  • Integrate PDF viewer within narrative editing interface
  • AI-assisted narrative drafting that extracts key data from source PDFs
  • Side-by-side panel in Veeva for PDF reference during narrative writing
  • Auto-populate narrative template fields from structured case data

Implementation Roadmap

Effort
Large
Timeline
3-5 months
Primary Owner
Engineering + AI
Dependencies
  • AI entity extraction model
  • Embedded PDF viewer in Veeva
  • Narrative template migration
Phased Delivery
  1. Model training (4-6 weeks)
  2. Side-by-side viewer (4 weeks)
  3. Integrated pilot + regulatory review (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
High
Ease of building the remediation
Adoption 20% weight
High
Likelihood users change workflow
Compliance 15% weight
Medium
Simplicity of PV validation path
12 hrs × 0.97 = 12 hrs / year
At 1,000 users: 713 hrs / year · $0.1M