Building Organizational Intelligence
How MRC Houston's AI workforce creates lasting competitive advantage
Pre-AI Technology Stacks Fragmented Data and Intelligence in Silos
Business systems before the AI era evolved as isolated tools, each solving narrow problems while scattering data across disconnected platforms.
The Solution: Agentic AI as Organizational Nervous System
What's needed is an agentic AI layer that acts as the organization's nervous system: automatically connecting and synthesizing signals across all apps and platforms, proactively surfacing emerging risks and opportunities without waiting for manual queries, learning organizational context continuously, and bridging the gap between humans and systems to preserve and compound intelligence over time.
Copilots help you create
Agents help you execute
The 3-Layer Platform Architecture
Phase 1: Build the Brain
Unified knowledge layer that understands your organization
Not a data warehouse—a semantic knowledge layer integrated with every system (CRM, email, calendars, docs), accessible through natural language.
Vector Embeddings
Semantic understanding across all content
Knowledge Graphs
Relationship mapping across entities
Natural Language Access
Query anything in plain English
Example Query: "Project Avalon status"
The Brain returns: "Avalon is 67% complete, on schedule, BUT vendor delay yesterday creates renewal risk for Northern Industries (renewal in 6 weeks). Team addressing with [3 actions]. Similar projects averaged 2-day recovery."
Phase 2: Build the Workforce
Autonomous workers that monitor, decide, act, and learn
AI workers with specific roles that operate continuously, make decisions within boundaries, learn from corrections, and escalate when needed.
Status & Communication
Copilot
Project Setup
Copilot
Expediter Coordination
Agent
Provider Research
Agent
Authorization QA
Agent
Document Intake
Agent
Invoice Processing
Agent
Current Value: $600-680K annually
1,152 PM hours reclaimed monthly. Industry-leading 15-20 day turnaround (vs. 30-45 day standard). * Business case projections are estimates and not validated
Phase 3: Build the Nervous System
Proactive intelligence delivery—the right insight to the right person at the right time
Not dashboards humans have to check—intelligence that delivers proactively through the right channel (dashboard, Slack, email, SMS). It doesn't wait to be asked.
Multi-Channel Delivery
Dashboard, Slack, email, SMS routing
Role-Based Intelligence
COO vs PM vs Client get different views
Proactive Signals
Alerts before you need to ask
Example: Multi-Role Intelligence Flow
8:00am COO: Dashboard shows "3 at-risk renewals—pattern: Vendor X"
10:15am PM: Slack alert with draft email ready
2:00pm Client: Updated health score with resolution status
5:00pm COO: Summary with strategic recommendation
How AI Learns & Improves
Intelligence emerges from learning loops, not comprehensive upfront design. You don't build intelligence—you grow it through continuous feedback.
Real Queries
Reveal actual patterns and priorities users care about
Human Corrections
Teach judgment, tone, and organizational preferences
Real Outcomes
Show which approaches actually work in practice
Edge Cases
Refine boundaries, exceptions, and nuanced scenarios
Training in Action
Every draft you edit, every query you run, every pattern you confirm teaches the system your standards. The AI isn't following pre-programmed rules—it's learning your rules as you work.
The Compounding Effect
Value compounds. Intelligence built today creates advantage tomorrow.
Early Stage
Faster retrieval & search
Middle Stage
Workflow coordination
Advanced Stage
Pattern prediction
Mature Stage
Strategic intelligence
MRC Houston's Current Position
Between Middle and Advanced stages—AI workers monitor continuously, provide contextual insights, and pattern prediction capabilities are actively developing.
AI Workforce Portfolio
7 active workers delivering production value across medical records operations
Value Proposition
$600-680K Annual Value
Toward 10% margin improvement
* Business case projections are estimates and not validated
Industry Leadership
30-45 days → 15-20 days
Industry standard vs MRC turnaround
50%+ faster than industry average
Collaborative intelligence for PM decision-making
Status & Communication Copilot
Real-time insights & client-ready responses
ActiveClick to explore
Project Setup Copilot
Protocol validation & Salesforce accuracy
ActiveClick to explore
Autonomous workflow automation with human oversight
Expediter Orchestration Agent
20-person team coordination
ActiveProvider Research Agent
427 hrs/month automated
ActiveAuthorization QA Agent
600 monthly validations
ActiveDocument Intake Agent
80% electronic automation unlocked
ActiveInvoice Processing Agent
Compliance risk elimination
ActiveComplete System Overview
Status & Communication Worker
Project Setup Worker
Expediter Coordination Worker
Provider Research Worker
Authorization QA Worker
Document Processing Worker
Invoice Processing Worker
What can I help with?
Real-time insights across 70+ active litigation projects
Recent Queries
SLA Risk Analysis
SLA Risk Projects
Draft Response for Norton Rose Fulbright
Protocol Validation
AI-extracted fields with confidence scoring
Protocol Form Preview (Page 1 of 13)
KIRKLAND & ELLIS LLP
Medical Records Retrieval Protocol
AI-Extracted Fields
Expediter Orchestration Agent
20-person team coordination • $240K annual value
Team Capacity
Team Utilization: 95%
Scheduled Callbacks
47
Avg Daily Completion
32
Consolidated Provider Requests
Priority Callback List
Provider Research Agent
427 monthly hours automated • $154K annual savings
Processing Stats
Auto-Processed (85%+)
23
Human Review (70-84%)
4
Manual Research (<70%)
1
Avg Processing Time: 90 seconds (vs 8 minutes manual)
Review Queue
Authorization QA Agent
600 monthly validations • 60-80% hold reduction
Processing Stats
Form Types Supported: 7
Generic HIPAA, Walgreens, Kroger, CVS, CMS, Psychiatric, Social Security
Avg Processing: <1 min (vs 4 min manual) • Monthly Volume: 600 authorization sets
Validation Queue (Traffic Light System)
Document Intake Agent
80% electronic automation unlocked • 24-48 hour acceleration
Impact Stats
Monthly Hours Saved
120
Annual Value
$43K
Turnaround Improvement
24-48h
Electronic Intake
80%
Document Inbox
Invoice Processing Agent
100% statutory compliance • 40-50% payment acceleration
Compliance Stats
State Maximum Fee Validation: 100%
Payment Cycle Acceleration: 40-50%
Compliance Risk: Eliminated
Invoice Queue (Traffic Light System)
ROI Calculator
Model your AI workforce investment with corrected assumptions from verified business case
"The big one is Worker 3 (Expediter Coordination). Even if you brought that down by half, then I have to flip back to what's the initial investment cost to realize that value."
— Gretchen Watson, Meeting Transcript (Nov 7, 2025)
| Worker | Annual Value | Cost |
|---|
💡 Note: This is a decision support and visualization tool based on business case assumptions. All values and timelines are illustrative scenarios to guide strategic planning—not contractual commitments. Actual implementations, UI/UX, and results will be defined during project execution.
About the ROI Calculator
Understanding how to model AI worker investment and value
ROI Calculator
Purpose
The ROI Calculator helps you model the 3-year financial impact of deploying AI workers at MRC Houston. It shows total investment costs, annual value realization, payback period, and ROI multiple based on your specific worker selections and deployment timeline.
How to Use
- Adjust Input Controls: Use the sliders on the left to modify assumptions (PM count, labor rate, providers/month, etc.)
- Set Year 1 Timeline: Use the "Year 1 Live Months" slider (1-12 months) to model partial-year deployments
- Select Workers: Check/uncheck workers to see how different combinations affect ROI
- Optional IT Overhead: Enable the IT Overhead toggle ($500/month) if infrastructure costs apply
- Review Results: The middle panel shows 3-year ROI, payback period, and cumulative investment vs. value chart
- Analyze Breakdown: The right panel shows detailed cost breakdown by category
Key Assumptions
- Development Costs: $16K foundation + $32K per worker (one-time, Year 1 only)
- QA Costs: $5,400 (one-time, Year 1 only)
- Operations Support: $3K/month minimum OR (worker hours × $100/hr), whichever is higher
- IT Overhead: $500/month (optional, if infrastructure support is needed)
- Value Realization: 50% in Year 1, 100% in Years 2-3 (conservative ramp-up)
- Worker Hours: Each worker = 12 hours/month of operational support
Limitations & Caveats
⚠️ This tool makes several simplified assumptions:
- Static Costs: Development and operations costs are assumed constant across all workers
- Linear Scaling: Adding more workers increases ops costs linearly (12 hours each)
- Hard Cost Savings Only: Worker values show time/cost savings, not soft benefits like improved accuracy or customer satisfaction
- No Overlap Analysis: Doesn't account for worker dependencies or synergies (e.g., Worker 1 feeding data to Worker 3)
- Fixed Ramp-Up: 50% Year 1 realization applies to all workers equally—actual adoption may vary
- No Volume Sensitivity: Value calculations don't adjust for case volume fluctuations
When to Use This Tool
Use the ROI Calculator when:
- Evaluating the financial impact of specific worker combinations
- Presenting budget requirements to stakeholders
- Comparing different deployment timelines (3 months vs. 12 months live in Year 1)
- Understanding the relationship between investment and value realization
⚠️ Important Disclaimer
This ROI Calculator is a decision support and visualization tool based on business case assumptions from October 2025. All values, timelines, and metrics are illustrative scenarios to guide strategic planning—not contractual commitments.
Actual implementations, development costs, operational support requirements, worker performance, and value realization will be defined during project execution. Use this tool to explore possibilities and trade-offs, not as precise predictions.