Internal Copilot Pilot

Executive Summary

A mid-market financial services firm struggled with knowledge worker productivity bottlenecks—analysts spending hours searching internal documents, drafting reports, and synthesizing information from multiple systems. Leadership wanted to explore AI copilots but had concerns about data security, accuracy, and governance.

We designed and executed a structured pilot program that deployed internal AI copilots to 40+ knowledge workers with appropriate guardrails, governance, and measurement systems.

Key Results:

1. 30% reduction in average cycle time for core analyst workflows
2. 40+ active users across investment analysis and client services teams
3. Zero data leakage incidents during 90-day pilot
4. 85% user satisfaction rating
5. Clear ROI case for enterprise-wide rollout

The Challenge

Client Context

The client is a $2B AUM investment management firm with 200+ employees. Their competitive advantage depends on analyst speed and insight quality—the ability to synthesize market data, internal research, and client context faster than competitors.

The Problem

Knowledge workers were drowning in information:

1. Analysts spent 8-12 hours per week searching internal SharePoint repositories
2. Report generation required manual copy-paste from multiple systems
3. New hires took 6+ months to learn where critical information lived
4. Subject matter experts were constantly interrupted for "quick questions" that derailed deep work

Leadership saw public AI tools like ChatGPT but had valid concerns:

1. Data security: Could not risk proprietary research or client data leaving the organization
2. Accuracy: Financial analysis requires precision—hallucinations could be catastrophic
3. Compliance: Regulatory requirements meant every AI output needed auditability
4. Adoption: Previous technology rollouts had failed due to poor change management

What They Tried Before

The firm had attempted several solutions:

1. Traditional search improvements (failed—too many documents, poor metadata)
2. SharePoint modernization (failed—didn't address synthesis problem)
3. Knowledge management consultants (failed—created more process, not better tools)

They needed something fundamentally different.

Our Approach

Phase 1: Discovery & Design (Weeks 1-3)

We didn't start with technology. We started with workflow analysis.

Workflow Mapping We shadowed 12 users across different roles:

1. Investment analysts researching public companies
2. Client service managers preparing quarterly reviews
3. Compliance officers reviewing marketing materials

We identified three high-value, high-frequency workflows:

1. Company research synthesis (8-15 documents, 4-6 hours)
2. Client review preparation (10-20 documents, 3-5 hours)
3. Regulatory compliance checks (variable documents, 2-4 hours)

Risk Assessment For each workflow, we mapped:

1. Data sensitivity levels
2. Accuracy requirements
3. Decision stakes
4. Current error rates
5. Compliance implications

Governance Design We designed a three-tier governance model:

Tier 1 - Low Risk: General knowledge questions, publicly available information

1. AI can suggest without human review
2. Example: "Summarize the latest Fed meeting minutes"

Tier 2 - Medium Risk: Internal knowledge synthesis, draft generation

1. AI assists, human reviews and validates
2. Example: "Draft a company analysis based on these 10 documents"

Tier 3 - High Risk: Client-facing content, compliance decisions

1. AI provides research only, final decision requires senior approval
2. Example: "Is this marketing material compliant with SEC regulations?"

Phase 2: Technical Implementation (Weeks 4-6)

Architecture Decisions We deployed an internal copilot system with these key components:

1. Private LLM Endpoint

1. Azure OpenAI instance within client's tenant
2. No data leaves their environment
3. Dedicated capacity for consistent performance

2. RAG (Retrieval Augmented Generation) System

1. Indexed 15,000+ internal documents (research reports, memos, client files)
2. Semantic search with metadata filtering
3. Source citation for every AI-generated claim

3. Access Controls

1. Role-based permissions tied to existing AD groups
2. Information barriers between client portfolios
3. Audit logging for every query and response

4. Human-in-the-Loop Workflows

1. AI outputs flagged as "draft" in UI
2. One-click source verification
3. Required review checkpoints for high-stakes decisions

Technology Stack:

1. Azure OpenAI (GPT-4)
2. Azure AI Search (vector + keyword)
3. Custom Python middleware for governance rules
4. Microsoft Teams integration for user interface

Phase 3: Pilot Program (Weeks 7-18)

Pilot Cohort Selection We selected 40 users across three teams:

1. 20 investment analysts (high-frequency users)
2. 15 client service managers (medium-frequency users)
3. 5 compliance officers (low-frequency, high-stakes users)

Staged Rollout

1. Week 1-2: 10 "champion" users, daily feedback sessions
2. Week 3-6: Full 40-user cohort, weekly office hours
3. Week 7-12: Hands-off observation, biweekly surveys

Training Program

1. 2-hour initial training: system capabilities, governance model, how to evaluate AI outputs
2. Job aids: "How to write effective prompts" cheat sheet, governance decision tree
3. Open Slack channel for questions and tips

Measurement Framework We tracked four categories of metrics:

Efficiency Metrics:

1. Time to complete core workflows
2. Number of documents reviewed per task
3. Interruptions to subject matter experts

Quality Metrics:

1. Error rate in AI-assisted analysis
2. Supervisor review time
3. Client satisfaction scores

Adoption Metrics:

1. Active users per week
2. Queries per user
3. Feature utilization

Safety Metrics:

1. Data leakage incidents
2. Governance violations
3. Escalations to senior review