The 90-Day Challenge: When Your Legacy System Can't Wait for Vendor Roadmaps

The Setup: Legacy Meets Reality

The Enterprise Dilemma

Our client—a large enterprise with complex operational workflows—found themselves in a familiar but painful position. After 15 years of building custom business logic into a homegrown scheduling platform, they needed to migrate to a modern vendor solution.

The problem? The vendor's platform was robust and scalable, but it couldn't handle the enterprise's unique workflow requirements without extensive customization.

The vendor's timeline? 12-18 months for custom development, plus uncertainty about whether some requirements were even commercially viable for them to build.

The business reality? Operations needed to continue seamlessly, without an incredible uplift in resources.

Why "Rip and Replace" Wasn't an Option

Custom Business Logic Complexity:

• 15 years of evolved workflow refinements
• Industry-specific requirements that don't exist in standard software
• Integration dependencies with multiple internal systems
• Operational knowledge embedded in process, not documentation

Real-World Constraints:

• Revenue operations couldn't be interrupted
• Staff retraining would be extensive and costly
• Existing customer commitments required specific workflow capabilities
• Compliance and audit requirements tied to current processes

The Challenge: Interpreting Unstructured Business Instructions

The Real Challenge

The heart of the integration challenge was both technical AND human. Our client had built highly specific technology around a workflow that enabled their operations staff to process relatively unstructured instructions from account executives and translate them into precise system commands at scale.

The client's 15-year-old custom platform wasn't just software—it was a finely-tuned machine built around their unique operational requirements. The vendor's platform, while modern and robust, couldn't replicate this specialized workflow without extensive customization that wasn't commercially attractive for them to build.

Example Instruction Types:

• "Run the Johnson campaign on all downtown locations from 3/15 to 3/22, avoiding peak traffic hours"
• "Emergency change: swap out the automotive content with the retail version on Location Group Alpha starting tomorrow morning"
• "Update rotation schedule for March promotion - increase frequency during high-traffic periods, standard rotation otherwise"

The Translation Challenge

These natural language instructions contained:

• Temporal requirements (dates, times, durations)
• Geographic specifications (location groups, individual assets)
• Business rules (priority levels, conflict resolution)
• Resource allocation (content assignments, capacity management)

The Manual Process Reality:

• Operations staff spending 40% of their time interpreting instructions
• Frequent back-and-forth clarification cycles
• Human error in translation leading to costly mistakes
• Inconsistent interpretation across different staff members

The Aggressive Timeline Constraint

Why 90 Days?

Business Drivers:

• It became clear that the vendor (a great partner) had limited commercial upside for modeling the company's unique and specific workflows
• Competitive pressure requiring operational efficiency gains
• Neither company had 12-18 months to go into a custom build cycle for a proof-of-concept
• Budget cycle constraints requiring demonstrable results

Technical Constraints:

• Legacy system integration points were fragile
• New vendor platform was being customized in parallel
• Limited development resources available
• Production system couldn't be disrupted for extended testing

The Strategic Decision

Rather than wait for the vendor's custom development roadmap, we decided to build an AI interpretation layer that could:

• Bridge the gap between human instructions and system requirements
• Maintain operational continuity during the transition period
• Provide immediate ROI through efficiency gains
• Serve as a prototype for long-term integration architecture

The AI-First Solution Strategy

Core Concept: The Intelligent Interpreter

Instead of forcing humans to learn new system syntax, we built an AI layer that speaks human and outputs machine-readable instructions.

The Architecture Decision:

• Input: Natural language instructions from business users
• Processing: LLM-powered interpretation and structuring
• Output: Validated JSON commands for system APIs
• Oversight: Human approval loop with chat-based correction capability

Why This Approach Made Sense

Immediate Benefits:

• No retraining required for operations staff
• Maintains existing business processes
• Reduces interpretation errors and time
• Provides audit trail for all decisions

Long-term Strategic Value:

• Serves as integration prototype for vendor development
• Creates reusable AI interpretation patterns
• Generates training data for future automation
• Demonstrates ROI for broader AI initiatives

The Technical Stack Decision

• Frontend: Replit for rapid prototyping and quick iterations
• AI Processing: Multiple LLM evaluation (OpenAI, Claude, others)
• Workflow Orchestration: N8N and Workato evaluation for complex business rules
• Database: PostgreSQL for structured data management
• Integration: RESTful APIs for vendor platform communication
• Data Flow: Salesforce → AI Processing → Vendor Platform → Validation

The 90-Day Challenge: From Prototype to Production

Current Status: Rapid Prototype Phase

We've built a working prototype that demonstrates the core concept: AI interpretation of business instructions with human oversight. The prototype proves the approach works, but now comes the real challenge—can we go from prototype to full production deployment in 90 days?

The 90-Day Sprint to Production

Weeks 1-3: Foundation and Architecture

• LLM Selection: Complete performance comparison and select primary AI engine
• Workflow Architecture: Finalize N8N vs Workato for production orchestration
• Database Design: Set up PostgreSQL schema for instruction tracking and audit trails
• Integration Hardening: Build robust API connections with error handling and retries

Weeks 4-7: Core Development

• Production Prompt Engineering: Scale prototype prompts for production reliability
• Business Rules Engine: Implement complex workflow logic and exception handling
• User Interface Development: Build production-ready approval and correction interface
• Security Implementation: Add authentication, authorization, and data protection

Weeks 8-11: Testing and Validation

• Parallel Operations: Run AI system alongside existing manual processes
• Accuracy Benchmarking: Measure interpretation quality against human baseline
• Performance Testing: Ensure system can handle production volume and speed
• User Acceptance: Train operations staff and refine based on feedback

Weeks 12-13: Production Deployment

• Phased Rollout: Start with low-risk scenarios, expand to full operations
• Monitoring Dashboard: Real-time tracking of accuracy, performance, and errors
• Success Metrics: Document efficiency gains and operational improvements
• Lessons Learned: Capture insights for future AI implementation projects

The "Why Now" for Enterprise AI

The Market Reality Check

Traditional Approach: Wait for vendors to build custom solutions

• Timeline: 12-18 months minimum, possibly longer
• Cost: High development and integration fees
• Risk: Vendor priorities may not align with your timeline
• Control: Limited influence over feature development

AI-Bridge Approach: Build intelligent integration layers

• Timeline: 90-day prototype-to-production challenge
• Cost: Fraction of custom development costs
• Risk: Controllable scope with rapid iteration
• Control: Full ownership of business logic and integration

Strategic Advantages

• Immediate ROI while longer-term solutions develop
• Proof-of-concept for broader AI adoption initiatives
• Competitive advantage through operational efficiency
• Risk mitigation by reducing vendor dependency
• Knowledge building for future AI implementations

The Real Questions We're Facing

As we write this, we're deep in the prototype-to-production phase. Here are the honest challenges we're wrestling with:

Technical Uncertainties:

• Will our LLM selection hold up under production load?
• Can we maintain 95%+ accuracy across all instruction types?
• How do we handle edge cases that weren't in our training data?

Operational Concerns:

• Will operations staff trust the AI recommendations?
• How quickly can we iterate when we find problems?
• What happens if the system goes down during peak operations?

Business Realities:

• Are we building something that's truly scalable?
• Will this approach work for other similar enterprises?
• Can we document this process well enough to replicate it?

Coming Up: The Technical Deep-Dive

We're currently in the prototype phase with promising early results, but the real test is ahead of us. In our next article, we'll dive into the technical decisions that will make or break our 90-day challenge:

• LLM Selection: OpenAI vs Claude vs others—performance comparison on real business instructions
• Workflow Orchestration: N8N vs Workato for production-grade business logic
• Prompt Engineering: How we're building reliability into AI interpretation
• Integration Architecture: Connecting Salesforce, AI processing, and vendor APIs