AI that reduces Opex, accelerates TTM, and expands TAM.
Most AI initiatives stall at the pilot stage because they were funded as innovation projects rather than operating-discipline projects. PEXIVA's AI engagement methodology starts with a quantified Opex / TTM / TAM hypothesis, identifies the day-to-day operational workflows where AI changes the unit economics, and ships production-grade automation in 12-16 week cycles — not multi-year research programs.
Methodology, not fabricated case studies.
PEXIVA publishes engagement methodology rather than client case studies with invented metrics. We do this for two reasons: most of our work is governed by NDAs that don't permit public reference, and we believe a real CIO or operating partner gets more value from interrogating the methodology we'd actually use than from reading sanitized success stories with implausibly clean numbers.
The framework below is what we'd run on your engagement. Phases, durations, deliverables, principles, anti-patterns. Defensible because every step is what we'd actually do — not retrofitted to a marketing narrative.
When organizations call us about ai & automation in day-to-day operations.
A mid-market or enterprise operator has tried GenAI pilots — usually some combination of an internal copilot, a customer-service experiment, and a document-automation prototype. Some demoed well. None reached production. The CFO is asking when AI will show up in a real KPI. The CIO is asking what "production-grade" actually means. The COO has identified D2D operations where AI could change the cost structure but doesn't know where to start. Sometimes a vendor has sold an "AI platform" that hasn't shipped a working use case.
Common signals
Recognize any of these? They're the patterns that drive ai & automation in day-to-day operations engagements:
- Previous initiatives produced disappointing results
- Leadership is not aligned on what success looks like
- Vendor-led roadmap is being mistaken for architecture
- Compliance and security are afterthoughts in the plan
- No instrumented KPI tied to the work yet
Four outcome categories we measure against.
Every engagement scoped against these four. If we can't articulate the target in all four — we don't take the engagement.
Quantified reduction in the targeted operational cost line — typically 15-40% in the addressed workflow within 6-9 months of go-live
Cycle-time reduction in customer-facing or product-facing process — onboarding, pricing, document turnaround, claims, etc.
AI-enabled capability addressing a customer segment or use case that was previously uneconomic to serve
Model evaluation, monitoring, and incident response practices in place — AI in production responsibly, not chaotically
Four phases. Real durations. Real deliverables.
This is the actual rhythm of a PEXIVA ai & automation in day-to-day operations engagement. Phase 1 typically funds Phase 2. Each phase ends with a decision gate — continue, adjust, or stop.
Use Case & Hypothesis · 2-3 weeks
Activities: D2D workflow audit. Quantified hypothesis development for Opex / TTM / TAM impact. Use case prioritization (value × feasibility × data readiness). Compliance and risk classification per use case.
Deliverables: Use case backlog (ranked). Quantified value hypothesis per top-3. Risk classification. Data readiness assessment.
Architecture & Foundation · 3-4 weeks
Activities: AI architecture decisions: model choice (Anthropic Claude, AWS Bedrock, Azure OpenAI, on-prem), RAG pattern, agent design, evaluation harness, monitoring stack. Data foundation gap analysis. Compliance and security boundaries.
Deliverables: AI architecture document. Model selection memo. Eval harness (running). Compliance posture for each use case.
Production Build · 8-12 weeks
Activities: First production use case build. Integration into the operational workflow. Eval-driven development. Human-in-the-loop where required. Change management for affected operations team.
Deliverables: Production AI capability (deployed). Eval reports. Human-in-the-loop UI (where applicable). Trained operations team.
Operate & Expand · Ongoing
Activities: Production monitoring (drift, hallucination rate, business KPI). Continuous eval. Use case 2 and 3 development. Governance forum stand-up. Knowledge transfer.
Deliverables: Production monitoring dashboard. Quarterly model review. Use case 2 charter. Governance operating model.
Five non-negotiables across every phase.
- Every use case tied to a quantified Opex, TTM, or TAM hypothesis before development starts
- Evaluation harnesses built before production code — eval-driven development is non-negotiable
- Human-in-the-loop where the cost of error is high; full automation only where the eval economics justify it
- Production monitoring (hallucination rate, drift, KPI) shipped with every model — not after
- Governance is an operating practice, not a one-time policy document
Anti-patterns we refuse
Patterns that produce the failures we've seen too often. We won't run an engagement structured this way, even if a client asks for it:
- GenAI pilots that demo well but never ship to production
- "AI platforms" purchased before a single use case is in production
- Models deployed without evaluation harnesses or production monitoring
- Full automation in workflows where the cost of an error exceeds the savings 10x over
Tell us about your ai & automation in day-to-day operations trigger event.
A 1-hour working session with our senior team. We'll walk you through the methodology applied to your specific situation. You leave with a prioritized next-step plan whether we engage or not.
Start the Conversation