The AI Automation Field Guide: From Pilot to Production in 90 Days

The execution gap: pilots that never scale

Most enterprises have an AI automation pilot somewhere. Few make it to durable production. The gap is rarely about models; it is about operating constraints: data access, controls, service levels, and who owns the runbook. “Pilot to production” fails when scope is fuzzy, governance is bolted on late, and KPIs measure novelty instead of throughput and risk.

The cost is tangible: stranded opportunity, compliance exposure from shadow workflows, and teams that learn the wrong lessons (“AI doesn’t work here”). The remedy is a production-first plan with clear decision gates, security-by-default, and measurable unit economics from day one.

Why now: AI economics, platforms, and governance have matured

Three shifts make this the moment to industrialize automation:

 
• Economics: Token and inference costs continue to drop (varies by context). Combining retrieval with compact models brings predictable unit cost per task.
 
• Platforms: Category tools—IDP engines for documents, RPA platforms for legacy apps, agent frameworks for orchestration—are stable enough to compose without bespoke glue code.
 
• Governance: Model risk management, data residency controls, and LLM observability have playbooks. You can meet financial-grade requirements with standard patterns instead of custom policies.

Result: you can ship faster without trading away compliance. Treat automation like a product, not a lab experiment.

Reference architecture: secure, compliant, production-grade AI automation

Start with a simple, defendable north star architecture and evolve. Keep blast radius and auditability in focus.

Core layers

 
• Ingress and identity: SSO, conditional access, service accounts, and per-tenant keys. Enforce least privilege.
 
• Data plane: Read-only connectors to systems of record; policy-based PII redaction and masking; regional storage for data residency.
 
• Knowledge layer: Retrieval index (vector database + metadata) with explicit provenance; TTL for embeddings aligned to data SLAs.
 
• Reasoning layer: Prompt templates, tool-use policies, and function routing. Prefer constrained tools over free-form generation for high-risk steps.
 
• Automation layer: Human-in-the-loop queues, RPA/IPA tasks, and workflow state machine with idempotency and retries.
 
• Guardrails: Input/output filtering, prompt injection defenses, jailbreak detection, and content policy enforcement.
 
• Observability and risk: Tracing, evaluations, red-team harnesses, model cards, and change logs mapped to model risk tiers.

Security and compliance essentials

 
• PII redaction pipeline before model calls; per-field policies logged for audit.
 
• Data residency controls by region; clear cross-border data transfer register.
 
• SOC 2/ISO 27001-aligned processes (access review, change management, incident response).
 
• EU AI Act readiness: purpose specification, human oversight, and transparency for users.

90-day plan: from pilot to production

An operator-grade plan with decision gates ensures momentum and safety.

Days 0–14: Frame the thin slice

 
• Use case: Select a single workflow with high volume, clear SLAs, and bounded data (e.g., onboarding document triage, invoice exception handling).
 
• Success criteria: Define target KPIs (automation rate, cycle time reduction, error rate, human acceptance).
 
• Data contracts: Identify systems of record, fields, and masking rules. Write the data handling spec.
 
• Risk tiering: Assign model risk level, oversight steps, and rollback triggers.

Days 15–30: Build the secure sandbox

 
• Stand up the core stack: identity, logging, retrieval index, evaluation harness, and human-in-the-loop review queue.
 
• Implement PII redaction, content filters, and prompt injection defenses.
 
• Assemble a golden dataset (50–200 items) with ground truth for offline and shadow testing.
 
• Define playbooks: incident management, model change control, and data deletion.

Days 31–60: Ship the thin slice

 
• Wire connectors to one production source; run shadow mode for 1–2 weeks.
 
• Instrument evaluations: accuracy, latency, cost per task, hallucination rate.
 
• Tune prompts/tools; add deterministic steps for high-risk transitions.
 
• Launch limited production with human oversight; record acceptance and rework.

Days 61–90: Harden, scale, decide

 
• Expand to second data source or downstream system (e.g., ticketing, CRM) via workflow engine.
 
• Finalize runbooks, SLAs, and on-call rotation; document control evidence.
 
• Conduct a red-team exercise; remediate findings; lock the release checklist.
 
• Executive decision: proceed to scale, extend scope, or pause. Use KPIs and unit economics to decide.

Deliverables at day 90: production workload with guardrails, KPI dashboard, model risk dossier, and a backlog for the next two increments. This is your 90‑day AI automation roadmap in action.

KPIs and ROI model

Measure outcomes that map to operations and risk, not just model cleverness.

Simple ROI frame

Net impact = (Baseline cost − New cost) × Volume − Fixed run cost (varies by context).

Example: If baseline handling is $6/task and the automated flow is $2.80/task inclusive of oversight, at 30k tasks/quarter the gross impact is ~$96k/quarter. Subtract platform and support to get net. Use this same frame to compare alternatives (e.g., IDP-first vs. RPA-first).

Risks and guardrails: security, privacy, and EU AI Act readiness

Design controls into the workflow, not as a bolt-on.

Mini-case: onboarding automation for a fintech operations team

Scope: automate “Know Your Business” document intake and verification for SMB onboarding. Constraints: mixed document quality, sensitive PII, tight SLAs, and regulator scrutiny.

Approach

Outcomes

This is the pattern for “how to move from AI pilot to production in 90 days” without vendor lock-in or bespoke code sprawl.

Next steps

If you have a stranded pilot, start with the thin slice and a production-first guardrail set. Tie each iteration to business KPIs, not demos. Prioritize workflows where compliance and throughput both matter—financial onboarding, claims triage, invoice exceptions, policy Q&A—so the win compounds.

Recommended move: run a two-week 90-day blueprint to lock scope, KPIs, controls, and the first release. From there, execute the plan above with a small, cross-functional squad and an explicit operations owner. When in doubt, simplify the scope, tighten the guardrails, and measure cost per task.

When you are ready, request a production readiness review focused on data handling, model risk, and observability. In most environments, this is the fastest path to a compliant, English-first, enterprise-grade deployment that scales.