[Enterprise AI](/ai-integration-for-enterprises) Roadmap: A 90-Day Plan from Pilot to Production

Executive summary

Enterprises don't need sprawling, multi-year AI programs to create value. With tight scope and disciplined execution, you can ship a production-grade AI capability in 90 days—measured, compliant, and aligned to business outcomes. This roadmap provides a pragmatic plan for CIOs and COOs to move from ideas to an operating pilot, then to a controlled production rollout. The emphasis is on measurable ROI, security-by-design, and change management so adoption sticks.

Why 90 days works

- Focus forces prioritization. Constraining to 90 days eliminates nice-to-have features that dilute value. - Momentum reduces risk. Shipping a small, well-governed pilot surfaces real-world constraints early. - Confidence enables funding. Clear outcomes and baselines establish credibility for the next tranche of investment.

Guiding principles

- Business-outcome first: Tie every requirement to a KPI that a business owner cares about. - Security and compliance by default: Bake in controls on day one. Don't retrofit. - Human-in-the-loop where it matters: Achieve speed without ceding oversight. - Measure before and after: Establish baselines up front to prove impact. - Keep the surface area small: Prioritize one or two high-leverage use cases. - Plan for handover: Document runbooks, train users, and set clear ownership.

Day 0 prerequisites

Before Day 1, confirm: - Executive sponsor and business owner: A VP/Director accountable for the outcome and adoption. - Cross-functional team staffed: Product, engineering, data/ML, security, legal/privacy, and operations. - Budget and access: Environment access, sandbox datasets, procurement guardrails, and a contingency reserve. - Risk and compliance alignment: Initial policy decisions on data residency, retention, and acceptable use. - Success criteria defined: Target metrics, measurement methods, and decision thresholds for go/no-go.

Phase 1 (Days 1–30): Discover and de-risk

Goal: Select high-ROI use cases, confirm data readiness, design the solution and guardrails, and build a thin proof of viability.

1) Pick 1–2 high-impact use cases

Use a quick scoring model across value, feasibility, and risk: - Customer support deflection: Automated answers with agent assist; measured by deflection rate and handle time. - Contract review triage: Clause extraction and risk flags; measured by review throughput and variance in cycle time. - Sales enablement: Drafting emails and summarizing calls; measured by cycle time and pipeline velocity. - Document processing: Invoice/PDF extraction; measured by straight-through processing (STP) and exception rate.

Selection criteria: - Clear owner and process fit - Contained data scope with manageable sensitivity - Achievable latency, accuracy, and cost targets - Integration path that avoids re-architecture

Deliverable: Use case one-pagers with success metrics, scope, and stakeholders.

2) Establish baselines and target outcomes

Define how you'll prove value: - Operational: AHT, deflection, cycle time, first contact resolution - Quality: Accuracy, recall/precision on key fields, user satisfaction - Financial: Cost per ticket/document, savings per transaction, time saved per FTE - Risk: Error severity distribution, override rates, exception volume

Deliverable: Baseline report and target thresholds (e.g., "Reduce invoice processing time from 36h to 8h with ≤2% critical errors, achieve $0.18 marginal cost per document").

3) Data readiness and access

- Inventory and classify data: Sources, PII, sensitivity, ownership - Create a governed sandbox: Mask PII as appropriate, log access - Sampling and annotation: Build small, representative datasets with ground truth labels and rubrics - Retention and residency decisions: Define what is stored, for how long, and where

Deliverable: Data readiness memo, sampling plan, glossary, and quality rubrics.

4) Architecture and vendor fit

Make decisions early to avoid churn: - Model strategy: General-purpose LLM vs domain-tuned; open vs hosted; fallback models for resilience - Retrieval (if needed): Choose vector store and ingestion approach; define chunking/indexing rules - Inference placement: Edge vs region vs on-prem based on latency, privacy, and cost - Integration patterns: Event-driven vs synchronous; how to log, monitor, and route exceptions - Procurement: Shortlist vendors; align on SLAs, pricing, and data handling

Deliverable: High-level architecture diagram, vendor shortlist, and cost model with unit economics.

5) Guardrails and governance design

- Security: Secret management, network boundaries, dependency scanning - Privacy: Data minimization, prompt/response redaction, DLP - Safety: Prompt injection defenses, allowed sources, response filters - Accountability: Human-in-the-loop thresholds, review queues, override and escalation paths - Auditability: Event and inference logging, correlation IDs, immutable audit trails

Deliverable: AI policy addendum, risk register, and control checklist.

6) Thin proof of viability

Build a narrow spike to de-risk the riskiest element (e.g., retrieval quality or field extraction accuracy) using a handful of examples and a simple UI or CLI.

Deliverable: Findings report with precision/recall and latency on the sampled dataset; decision to proceed to build.

Phase 2 (Days 31–60): Build and validate

Goal: Ship a minimal lovable pilot (MLP) to a controlled group, with end-to-end quality, safety, and observability.

1) Implement the MLP

- Core capability: The smallest set of features that deliver end-to-end value (e.g., draft answer, show sources, one-click escalate) - UX that builds trust: Show citations, confidence indicators, and an easy path to correction - Feedback capture: In-line thumbs up/down with reason codes; capture agent edits as training signals

2) Technical architecture

- Data ingestion: Deterministic pipelines with deduplication, PII handling, and content chunking - Retrieval (if applicable): Embedding choice, vector DB, hybrid search, and freshness strategy - Orchestration: Stateless server-side actions that call models; idempotent retries and timeouts - Observability: Tracing across UI/inference/integrations; structured logs with request IDs; dashboards for latency, cost, and quality - Caching and cost control: Response caching where safe, structured prompts, prompt compression, and model routing based on context

3) Evaluation and red teaming

- Offline evaluation: Holdout set with labeled outcomes; measure precision, recall, and hallucination rate - Online evaluation: Shadow/live tests with small cohort; track task success, time saved, escalation rate - Red teaming: Prompt injection, jailbreak attempts, data exfiltration probes; adversarial content testing

4) Security and compliance checkpoints

- DPIA/PIA as needed - Access controls: SSO, RBAC, least privilege; service-to-service auth - Data handling: Encryption at rest/in transit; explicit retention and deletion policies - Audit logs: Immutable storage for sensitive actions; reviewer identity attached to overrides

5) Change management and enablement

- Training: Short task-based videos and scripts; clear do/don't list - Playbooks: When to trust AI, when to escalate; error taxonomy and remediation steps - Communication: Set expectations—AI assists, humans decide; publish metrics and wins to build momentum

6) Pilot launch (limited cohort)

- Scope: One team or region; 10–50 users for 2–4 weeks - Support: Slack/Teams channel with rapid response; office hours; on-call rotation - Feedback cadence: Weekly review of metrics and user feedback; fast iteration on prompts and UX

Deliverables by Day 60: - Pilot live with controlled cohort - Metrics dashboard and risk report - Runbook for support, incidents, and rollback - Updated financial model and go/no-go criteria for productionization

Phase 3 (Days 61–90): Productionize and scale

Goal: Harden the pilot for production, roll out safely, and prove business impact.

1) Reliability, SLOs, and resilience

- SLOs: p95 latency, availability, and error budgets defined per capability - Resilience patterns: Fallback models/providers, timeouts and retries, circuit breakers, and graceful degradation - Kill switches: Feature flags to disable high-risk features instantly

2) Cost governance and unit economics

- Per-capability unit cost: Tokens per request, cache hit rate, and expected volume - Controls: Token and request caps, auto-downgrade on budget breach, batch and streaming where applicable - Optimization levers: Prompt and response compression, selective retrieval, model routing by task complexity

3) Performance and UX tuning

- Latency targets by step; prefetching and streaming partial responses where appropriate - A/B testing of UI patterns that influence trust and adoption (citations, confidence indicators, action layouts) - Accessibility and internationalization if rolling out globally

4) Governance and lifecycle

- Model versioning: Semantic version tags, rollout plan, and rollback procedure - Data lifecycle: Retention policies enforced in code; automated redaction for logs - Compliance: Update policy docs and training; schedule periodic audits and red-team exercises

5) Rollout and operations

- Canary strategy: 5% to 25% to 50% with gate checks at each stage - Regional considerations: Data residency, latency, and language support - Support readiness: Tier-1/Tier-2 playbooks, escalation paths, and on-call coverage - Vendor management: SLAs in place, usage alerts, and quarterly business reviews focused on cost and quality

6) Prove value

- Executive readout: Before/after metrics, savings realized, risk outcomes, and user adoption - Decision: Graduate to "business-as-usual" with a funded roadmap, or iterate further with the pilot cohort

Deliverables by Day 90: - Production rollout to 25–50% of target population with guardrails - Signed-off SLOs, runbooks, and governance docs - ROI report with baselines, deltas, and confidence intervals - Next-90-day roadmap with two to three additional capabilities

Common pitfalls and how to avoid them

- Fuzzy success criteria: Fix by defining baselines and targets in Week 1. - Over-scoped pilots: Ship one capability well; expand later. - Ignoring the last mile: Invest in UX trust signals and training; adoption drives ROI. - Weak observability: Without tracing and structured logs, you can't debug or prove value. - Compliance afterthoughts: Engage privacy and legal from Day 1; document decisions. - Cost surprises: Track unit economics early; implement caps and model routing before GA.

After 90 days: Institutionalize, don't just scale

- Operating model: Assign product ownership, establish an AI review board, and schedule model evaluations. - Platformization: Reuse retrieval, prompting, logging, and guardrail components to accelerate new use cases. - Portfolio planning: Maintain a prioritized backlog with expected ROI and risk profile per capability. - Continuous improvement: Feed edits and feedback into evaluation pipelines; iterate monthly.

Conclusion

A 90-day AI program is enough time to move from slideware to measurable impact—if you narrow scope, build the right guardrails, and obsess over outcomes and adoption. Start small, prove value, and scale deliberately. The enterprises that succeed treat AI as an operating capability with governance and accountability, not a one-off experiment.