The Aha Moment Matters Most

The situation: Order processing has grown organically over years. What started as a simple process now spans multiple disconnected systems. Nobody can explain the whole workflow—different people own different pieces. Orders take hours to process. Errors are common. New hires take months to get up to speed.

What we discovered: When we mapped the workflow, the operations director's reaction was immediate: "I never realized we touch 7 different systems for one order." The workflow map showed 50+ manual handoffs across 3 teams with multiple people doing the same validation steps in different systems.

What changed: Instead of optimizing 7 systems, we designed a single workflow with AI-assisted routing. Order comes in → AI validates inventory, flags exceptions, routes to the right team → Human reviews only flagged items → System updates across platforms automatically.

What we built: Sprint 1 delivered an Order Validation Agent—monitoring incoming orders, extracting key fields, validating against inventory. By week 4, the agent handled 70% of routine validation. Sprint 2 added Cross-System Sync with exception handling and audit trails.

The outcome: Processing time dropped from 4 hours to 45 minutes per order. Error rate dropped because the system catches inconsistencies humans missed. New hire ramp time cut in half.

The situation: Customer service response times are slow. Everyone agrees it's a problem. Nobody can pinpoint why. The team works hard. Individual response times look reasonable. But customers complain about delays.

What we discovered: When we mapped the workflow and measured time at each step, the surprise wasn't in the work—it was in the waiting. 80% of elapsed time was spent in "waiting for approval" state. Complex inquiries required manager sign-off. Managers were in meetings. Responses sat in queues.

What changed: When we mapped the outcome that mattered, we learned the real job was "Resolve customer inquiry accurately." Not "get manager approval"—that was a workflow artifact. The transformation introduced AI pre-screening: low-risk inquiries skip approval, medium-risk get async review, high-risk still require real-time approval.

What we built: Sprint 1 delivered an Inquiry Classification Agent that classifies by complexity and risk, pulls customer history, and drafts responses for routine inquiries. By week 4, 60% of inquiries were classified and pre-drafted. Managers now review 40% instead of 100%.

The outcome: Response time for routine inquiries dropped from 3 days to 4 hours. Most inquiries resolve same-day. Customer satisfaction improved because responses are faster without sacrificing accuracy.

The situation: Senior staff "just know" how to handle complex cases. They've developed intuition over years. New hires watch and learn—eventually. But there's no documentation, no training materials, no consistent approach. When an expert leaves, their knowledge leaves with them.

What we discovered: When we mapped personas, we found 4 senior experts handling complex cases. When we mapped their workflows, we found 4 different approaches to the same job. None were wrong—each had developed heuristics that worked. But they were different, and nobody knew it.

What changed: We brought the experts together and synthesized their approaches into a decision framework: What signals indicate each type of case? What information do you need? What are the decision criteria? This became documented judgment that AI could apply and humans could refine.

What we built: Sprint 1 delivered a Case Assessment Agent using the synthesized framework—performs initial assessment, recommends approach, surfaces relevant precedents. Human experts review every recommendation. Sprint 2 added a learning loop where confidence scores improve from expert adjustments. By week 8, 70% of recommendations accepted without changes.

The outcome: New hire ramp time dropped from 6 months to 6 weeks. Consistency improved because everyone follows the same framework. Expert time is spent on truly novel situations. When an expert leaves, their judgment stays documented in the system.