90-Day AI Execution Plan: From Idea to Production (Without Pilot Theatre) | Omovera
Executive takeaway: In 90 days, you can take one AI workflow to production if you enforce five non-negotiables: a named business owner, 3 measurable KPIs, production-grade engineering, governance controls, and adoption-led rollout.

What “pilot theatre” looks like (and why boards hate it)

Symptoms

  • POCs that never touch real workflows
  • No baseline metrics, no ROI tracking
  • Model accuracy demos without adoption
  • No monitoring, audit logs, or rollback
  • “We’ll productionize later” as a plan

Root causes

  • Unclear business ownership
  • Use case too broad (not workflow-scoped)
  • Data readiness ignored until late
  • Security/compliance treated as an afterthought
  • No change management and incentives

The CXO KPI set: measure what boards trust

AI should be measured like any other operating improvement program. Define three business KPIs and three guardrail KPIs before work begins.

Business KPI #1
Cycle time (TAT)
Time from intake to decision/closure
Business KPI #2
Cost-to-serve
Unit cost per ticket/document/case
Business KPI #3
Throughput / FTE
Volume handled per analyst per day
Guardrail KPI #1
Accuracy / Quality
First-pass accuracy, error rate, rework rate
Guardrail KPI #2
Escalations
% cases escalated, complaint rate, overrides
Guardrail KPI #3
Safety & Drift
Drift signals, incident counts, policy violations
Rule: If a use case cannot be measured with baselines and targets, it is not ready for a 90-day execution plan.

The Omovera 90-day structure (board-ready gating)

This plan is designed for one workflow (one business unit, one process path, one production release). You can expand after proving value. The goal is to ship measurable impact, not “AI capability.”

Gate Timeline Outcome Board/CXO proof
Gate 0: Use case selection Days 1–5 One workflow scoped; owners assigned Named sponsor + KPI baselines + feasibility
Gate 1: Diagnostic & blueprint Weeks 1–2 Execution plan + ROI model + controls Measurement plan + risk controls + architecture
Gate 2: Build & pilot in real workflow Weeks 3–8 Working system with users & data Early KPI movement + adoption + stability
Gate 3: Hardening & production go-live Weeks 9–12 Production release + monitoring + SOPs Security, audit logs, rollback, governance cadence

Week-by-week: the 90-day AI execution playbook

Days 1–5 • Use Case Selection (Stop guessing)

Most programs fail by picking the wrong first use case. The best first use case is high impact, measurable, and workflow-bounded.

  1. Pick one workflow with clear ownership and measurable outcomes Example: “invoice exception triage” beats “AI for finance.”
  2. Set 3 KPI targets and capture baselines immediately TAT, cost per case, throughput/FTE (plus guardrails).
  3. Define “production” upfront Users, volume, SLAs, audit logs, monitoring, rollback.
  4. Decide control model: automate vs assist vs approve Human-in-loop thresholds and escalation rules.
Weeks 1–2 • Diagnostic & Blueprint (Make it executable)
  1. Map the workflow: before/after process, exceptions, decision points Identify bottlenecks that drive cost, rework, or backlog.
  2. Data readiness review (fast, pragmatic) Availability, quality, privacy clearance, labeling needs, edge cases.
  3. Architecture blueprint (production-first) Integrations, identity/access, logging, eval harness, monitoring, cost controls.
  4. ROI model and measurement instrumentation Define how KPI movement will be measured weekly.
  5. Governance checklist Policies, approvals, evidence trails, red-teaming (where relevant), and compliance sign-offs.
Weeks 3–4 • Build the “thin slice” (Real workflow, real users)
  1. Ship a thin slice into the workflow One document type, one queue, one team—end-to-end, not a demo.
  2. Create an evaluation harness Test cases, acceptance thresholds, and regression checks.
  3. Implement guardrails from day one Confidence thresholds, citations/evidence links, refusal policies, and escalation paths.
  4. Instrument KPIs and usage analytics Adoption is a first-class metric, not an afterthought.
Weeks 5–6 • Expand coverage + design exception handling (Where ROI hides)
  1. Increase coverage to adjacent doc types / variations Target the 60–80% volume segment first.
  2. Build exception taxonomy Define the top 10 exception classes that consume the most time.
  3. Design human-in-loop workflow Queueing, reviewer UI, suggested actions, and audit trails.
  4. Refine prompts/rules/models based on measured failures Fix what breaks in the real workflow, not what looks good in a lab.
Weeks 7–8 • Prove KPI movement + stabilize operations
  1. Publish weekly KPI movement to the steering group TAT, cost-to-serve, throughput, error/rework, escalations, adoption.
  2. Run a controlled rollout Gradual ramp: 10% → 30% → 60% traffic with monitoring.
  3. Implement cost controls Rate limits, caching, batching, and fallbacks to control inference spend.
  4. Security review and audit pack draft Logs, access control, retention, incident response, vendor contracts.
Weeks 9–10 • Production hardening (Board-grade reliability)
  1. Finalize monitoring and alerting Quality drift, failure spikes, latency, cost anomalies, escalation rate.
  2. Finalize SOPs and playbooks How teams operate when AI is down or uncertain.
  3. Define rollback and kill-switch rules Clear criteria to revert to manual or reduced automation safely.
  4. Training and change management Enable the frontline teams; adoption is the ROI multiplier.
Weeks 11–12 • Go-live + scale plan (Avoid “one-off AI”)
  1. Go-live with production SLA Operational ownership assigned; incident response defined.
  2. Board-ready results pack KPI movement, ROI estimate, risk controls, and next 2 use cases.
  3. Scale blueprint Reusable components: document intake, extraction, eval harness, monitoring, governance cadence.

What Omovera delivers (authority by design)

Omovera’s approach is practitioner-led and execution-first. We do not sell “AI strategy decks” without shipping outcomes. In 90 days, our deliverables are structured to satisfy three stakeholders: the board (control + ROI), the business (adoption + throughput), and IT/security (production readiness).

Delivery artifacts (CXO-ready)

  • Use case charter + KPI baselines & targets
  • ROI model (best/base/worst) + unit economics
  • Workflow maps: before/after + exception taxonomy
  • Production architecture + integration plan
  • Governance pack: logs, policies, audit trails

Production outcomes

  • Live workflow in production (not a demo)
  • Monitoring + alerting + drift detection
  • Human-in-loop controls and escalation queues
  • SOPs, training, and adoption dashboards
  • Scale plan: next 2–3 use cases prioritized

Sample ROI examples (how boards evaluate success)

ROI varies by workflow and volume. The most reliable early gains usually come from cycle-time reduction, fewer manual touches, and fewer rework loops. Below are conservative “directional” ranges commonly seen in document- and ticket-heavy operations.

Workflow type Typical KPI wins in 90 days Primary value driver Board narrative
Document intake + routing TAT ↓ 20–40% • Misroutes ↓ 50–80% Less waiting + fewer handoffs Faster throughput without adding headcount
Extraction + validation Cost/case ↓ 30–60% • STP 40–80% Lower unit cost and rework Unit economics improve; scalable operations
Exception triage + copilot Review time ↓ 25–50% • Escalations ↓ 15–35% More throughput for same team Backlog control and consistent decisions
Important: “Accuracy” alone is not ROI. The board cares about KPI movement in operations: cycle time, unit cost, throughput, and risk outcomes—measured weekly and tied to baselines.

Want a 90-day execution plan tailored to your business?

Omovera can run a short diagnostic to identify your fastest AI win, quantify ROI, and deliver a production go-live plan with governance-ready controls.

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FAQ

What’s the best first AI use case to take to production?

Pick a workflow with high volume or high value-at-risk, stable inputs, measurable KPIs, and a named business owner. Document-heavy workflows (intake, extraction, exception handling) often provide the fastest measurable impact.

What governance controls are essential for production AI?

Logging, versioning, access controls, monitoring and drift detection, human-in-loop thresholds, incident response procedures, and rollback/kill-switch rules. These controls enable speed by reducing operational and reputational risk.

How do you avoid vendor lock-in while moving fast?

Use modular architecture, keep your evaluation harness and logs portable, separate orchestration from model providers, and maintain clear data ownership terms. Move fast, but design for optionality.