Case Study Template
Standardized Format for Documenting Agentic AI Success Stories
Document ID: D5-CASE-STUDY-TEMPLATE
Version: 1.0
Category: P4 - Business/Strategy
Audience: Marketing, Sales, Customer Success, Partners
Template Overview
This template provides a standardized structure for documenting customer success stories with agentic AI implementations. Consistent case studies support sales enablement, marketing campaigns, and customer reference programs.
Case Study Structure
Section 1: Executive Summary (150-200 words)
Template:
[COMPANY NAME] is a [industry/description] company with [size metrics]. Facing [primary challenge], they implemented [CODITECT solution/paradigm] to [primary objective].
Results:
- [Quantified result 1]
- [Quantified result 2]
- [Quantified result 3]
Key Quote: "[Compelling customer quote about transformation]" — [Name], [Title], [Company]
Example:
Meridian Financial Services is a regional bank with $12B in assets and 2,500 employees. Facing a 40% increase in loan applications with flat headcount, they implemented CODITECT's VE (Verifiable Executor) paradigm to automate loan processing workflows.
Results:
- 85% reduction in loan processing time (5 days → 18 hours)
- 99.2% decision consistency across loan officers
- $2.4M annual savings in operational costs
Key Quote: "CODITECT didn't just automate our process—it transformed how we think about serving customers." — Sarah Chen, SVP Operations, Meridian Financial
Section 2: Company Profile
Template:
| Attribute | Details |
|---|---|
| Company | [Full legal name] |
| Industry | [Primary industry vertical] |
| Size | [Revenue range / employee count / other] |
| Headquarters | [City, Country] |
| Operations | [Geographic scope] |
| Key Challenge | [One-sentence summary] |
Background (100-150 words):
[Provide context about the company, their market position, and relevant business context that led to the AI initiative.]
Section 3: The Challenge
Template (250-350 words):
Business Problem
[Describe the specific business problem in concrete terms. What processes were broken? What were the symptoms?]
Pain Points
- [Pain Point 1]: [Description with quantification if possible]
- [Pain Point 2]: [Description with quantification if possible]
- [Pain Point 3]: [Description with quantification if possible]
Impact Before Implementation
| Metric | Before State | Business Impact |
|---|---|---|
| [Metric 1] | [Value] | [Impact description] |
| [Metric 2] | [Value] | [Impact description] |
| [Metric 3] | [Value] | [Impact description] |
Previous Attempts
[What had the company tried before? Why did those approaches fall short?]
Example:
Business Problem
Meridian's loan processing team was drowning in applications. The 2021 housing boom increased applications by 40%, but budget constraints prevented hiring. Loan officers spent 60% of their time on data gathering and documentation rather than decision-making and customer interaction.
Pain Points
- Manual Data Collection: Officers manually gathered credit reports, income verification, and property data from 8+ systems, taking 2-3 hours per application
- Inconsistent Decisions: Same-profile applicants received different decisions based on which officer reviewed them
- Compliance Risk: Manual documentation led to a 12% error rate in compliance audits
Impact Before Implementation
| Metric | Before State | Business Impact |
|---|---|---|
| Processing time | 5 business days | Lost customers to faster competitors |
| Decision consistency | 72% | Regulatory scrutiny, customer complaints |
| Compliance errors | 12% | Audit findings, remediation costs |
Previous Attempts
Meridian had implemented an RPA solution in 2020, but it only handled data extraction. Decision support and documentation still required manual work, limiting impact to 15% time savings.
Section 4: The Solution
Template (300-400 words):
Solution Selection
[Why did they choose CODITECT? What alternatives were considered?]
Paradigm Selection
| Use Case | Paradigm | Rationale |
|---|---|---|
| [Use case 1] | [LSR/GS/EP/VE] | [Why this paradigm] |
| [Use case 2] | [LSR/GS/EP/VE] | [Why this paradigm] |
Implementation Approach
[Describe the implementation methodology, phases, and timeline]
Phase 1: [Name] (Duration)
- [Key activity 1]
- [Key activity 2]
Phase 2: [Name] (Duration)
- [Key activity 1]
- [Key activity 2]
Technical Architecture
[High-level description of how the solution was architected]
Integration Points
| System | Integration Type | Purpose |
|---|---|---|
| [System 1] | [API/File/etc.] | [Purpose] |
| [System 2] | [API/File/etc.] | [Purpose] |
Change Management
[How was organizational change managed? Training approach, communication strategy?]
Section 5: The Results
Template (250-350 words):
Quantified Outcomes
| Metric | Before | After | Improvement |
|---|---|---|---|
| [Primary KPI] | [Value] | [Value] | [% or absolute change] |
| [Secondary KPI] | [Value] | [Value] | [% or absolute change] |
| [Tertiary KPI] | [Value] | [Value] | [% or absolute change] |
Financial Impact
- Cost Savings: $[amount] [annually/one-time]
- Revenue Impact: $[amount] [from faster processing, new capacity, etc.]
- ROI: [X]% over [timeframe]
- Payback Period: [X] months
Operational Impact
[Describe qualitative improvements—employee satisfaction, customer experience, risk reduction]
Timeline to Value
| Milestone | Timeline |
|---|---|
| Go-live | [Date/Week X] |
| First measurable impact | [Date/Week X] |
| Full ROI realization | [Date/Week X] |
Customer Testimonial
"[Detailed quote about the transformation, specific enough to be credible, positive enough to be compelling]"
— [Name], [Title], [Company]
Section 6: Lessons Learned
Template (150-200 words):
What Worked Well
- [Success factor 1]
- [Success factor 2]
- [Success factor 3]
Challenges Overcome
- [Challenge 1]: [How it was addressed]
- [Challenge 2]: [How it was addressed]
Advice for Others
"[Quote from customer offering advice to peers considering similar initiative]"
— [Name], [Title]
Section 7: What's Next
Template (100-150 words):
[Describe planned expansion of the AI initiative—new use cases, broader deployment, deeper integration]
Future Plans
- [Planned initiative 1]
- [Planned initiative 2]
- [Planned initiative 3]
Visual Elements Checklist
- Company logo (with permission)
- Before/after comparison chart
- Architecture diagram (simplified)
- Timeline graphic
- Key metrics callout boxes
- Customer photo (if available and permitted)
Data Collection Questionnaire
For Customer Interviews
Background:
- Describe your company and your role.
- What was the business situation that led to this initiative?
- What had you tried before? Why didn't it work?
Challenge: 4. What were the specific pain points you were experiencing? 5. How did you quantify the problem? What metrics were you tracking? 6. What was the impact on your team, customers, and business?
Solution: 7. Why did you choose CODITECT? 8. How would you describe the implementation process? 9. What was the timeline from decision to go-live? 10. How did you manage change with your team?
Results: 11. What results have you achieved? (Specific metrics) 12. How long did it take to see results? 13. What has been the financial impact? 14. How has this changed how your team works?
Reflection: 15. What surprised you about this implementation? 16. What advice would you give to others considering this? 17. What's next for your AI journey?
Quotable: 18. If you had to summarize this experience in one sentence, what would you say? 19. Would you recommend CODITECT to peers? Why?
Approval Workflow
| Step | Owner | Deliverable | Timeline |
|---|---|---|---|
| Draft | Marketing | Initial case study | Day 1-5 |
| Internal review | Product/Sales | Accuracy check | Day 6-7 |
| Customer review | Customer Success | Customer approval | Day 8-14 |
| Legal review | Legal | Final approval | Day 15-17 |
| Design | Creative | Final formatted version | Day 18-21 |
| Publication | Marketing | Live on website | Day 22 |
Distribution Channels
| Channel | Format | Audience | Timing |
|---|---|---|---|
| Website | Full PDF + landing page | All prospects | Ongoing |
| Sales collateral | 2-page summary | Active deals | As needed |
| Email campaign | Key metrics snippet | Vertical-specific list | Launch week |
| Infographic | Professional network | Launch + ongoing | |
| Webinar | Customer presentation | Registered attendees | 4-6 weeks post |
| Conference | Slide deck excerpt | Event attendees | Event schedule |
Sample Case Study: Complete Example
Meridian Financial Services
85% Faster Loan Processing with Agentic AI
Executive Summary
Meridian Financial Services is a regional bank with $12B in assets serving the Pacific Northwest. Facing a 40% increase in loan applications with flat headcount, they implemented CODITECT's Verifiable Executor (VE) paradigm to automate loan processing workflows.
Results:
- 85% reduction in loan processing time (5 days → 18 hours)
- 99.2% decision consistency across loan officers
- $2.4M annual savings in operational costs
- 98.5% compliance audit pass rate (up from 88%)
Key Quote: "CODITECT transformed our loan operations from a bottleneck into a competitive advantage. We're now the fastest lender in our market." — Sarah Chen, SVP Operations, Meridian Financial
Company Profile
| Attribute | Details |
|---|---|
| Company | Meridian Financial Services |
| Industry | Regional Banking |
| Size | $12B assets, 2,500 employees |
| Headquarters | Seattle, WA |
| Operations | Pacific Northwest (WA, OR, ID) |
| Key Challenge | Scale loan processing without adding headcount |
Meridian Financial has served the Pacific Northwest since 1952, building a reputation for personal service and community banking. As the region's housing market boomed, Meridian faced unprecedented demand for mortgages and home equity loans while larger competitors moved into their market with faster, more automated processes.
The Challenge
Business Problem
Meridian's 45-person loan processing team was overwhelmed. Applications had increased 40% year-over-year, but budget constraints prevented hiring. Loan officers spent 60% of their time on data gathering and documentation rather than decision-making and customer interaction. Processing times stretched to 5+ business days, causing customers to abandon applications for faster competitors.
Pain Points
- Manual Data Collection: Officers manually gathered credit reports, income verification, and property data from 8+ systems, taking 2-3 hours per application
- Inconsistent Decisions: Same-profile applicants received different decisions based on which officer reviewed them, leading to customer complaints and regulatory scrutiny
- Compliance Risk: Manual documentation led to a 12% error rate in compliance audits, resulting in costly remediation
Impact Before Implementation
| Metric | Before State | Business Impact |
|---|---|---|
| Processing time | 5 business days | Lost 23% of applicants to competitors |
| Decision consistency | 72% | 15 regulatory findings in last audit |
| Compliance errors | 12% | $340K in remediation costs |
| Officer utilization | 40% on decisions | Low job satisfaction, 28% turnover |
Previous Attempts
Meridian implemented an RPA solution in 2020 for data extraction, achieving 15% time savings. However, the solution couldn't handle decision logic or generate compliant documentation, leaving most manual work unchanged.
The Solution
Solution Selection
After evaluating five vendors, Meridian selected CODITECT for its paradigm-based approach that could handle both structured compliance requirements (VE) and evidence-based credit analysis (GS). The ability to maintain full audit trails while automating decisions was critical for their regulated environment.
Paradigm Selection
| Use Case | Paradigm | Rationale |
|---|---|---|
| Data gathering | GS | Multi-source retrieval with verification |
| Credit decision | VE | Protocol-driven with full audit trail |
| Document generation | VE | Compliance-required formatting |
| Exception handling | GS + Human | Evidence-based recommendation |
Implementation Approach
Phase 1: Foundation (Weeks 1-4)
- Mapped existing loan processing workflows
- Defined credit decision protocols in VE format
- Integrated with core banking, credit bureaus, and property databases
Phase 2: Pilot (Weeks 5-8)
- Deployed with 5-person pilot team on home equity loans
- Daily monitoring and feedback sessions
- Refined protocols based on edge cases
Phase 3: Rollout (Weeks 9-12)
- Extended to full mortgage team
- Trained all 45 loan officers
- Established quality assurance protocols
Change Management
Meridian's HR team partnered with CODITECT to redesign loan officer roles. Officers transitioned from data gatherers to "Credit Advisors" focused on customer relationships and complex exception handling. Training included 16 hours of classroom instruction plus 2 weeks of supervised operation.
The Results
Quantified Outcomes
| Metric | Before | After | Improvement |
|---|---|---|---|
| Processing time | 5 days | 18 hours | 85% faster |
| Decision consistency | 72% | 99.2% | +27 percentage points |
| Compliance audit pass rate | 88% | 98.5% | +10.5 percentage points |
| Applications per officer | 8/week | 35/week | 4.4x increase |
| Customer abandonment | 23% | 6% | -17 percentage points |
Financial Impact
- Cost Savings: $2.4M annually (avoided hiring + reduced errors)
- Revenue Impact: $4.1M from retained customers who would have abandoned
- ROI: 340% in first year
- Payback Period: 3.2 months
Operational Impact
Loan officer satisfaction increased from 3.1/5 to 4.4/5, and turnover dropped from 28% to 11%. Officers report feeling like "real bankers again" rather than "data entry clerks." Customer NPS improved from 32 to 58.
Customer Testimonial
"We were skeptical about AI making credit decisions, but CODITECT's approach gave us confidence. Every decision is traceable, every document is compliant, and our officers are happier than they've been in years. We've gone from the slowest lender in our market to the fastest."
— Sarah Chen, SVP Operations, Meridian Financial
Lessons Learned
What Worked Well
- Starting with a defined, high-volume process (home equity loans)
- Heavy investment in change management and role redesign
- Daily feedback loops during pilot phase
Challenges Overcome
- Integration Complexity: Legacy core banking system required custom adapters; CODITECT's professional services team built connectors in 3 weeks
- Officer Skepticism: Initial resistance overcome through transparent AI decision explanations and pilot success stories
Advice for Others
"Don't underestimate change management. The technology is the easy part—getting your team to trust and adopt it takes real investment. But it's worth it."
— Sarah Chen, SVP Operations
What's Next
Meridian plans to extend CODITECT to commercial lending (Q3) and customer service automation (Q4). They're also exploring the Emergent Planner (EP) paradigm for complex commercial deal structuring.
Case study published with permission of Meridian Financial Services. Results may vary.
Template maintained by CODITECT Marketing Team. Feedback: marketing@coditect.com