[Entreprise IA](/IA-Intégration-pour-enterprises) Feuille de route: un/une 90-Day Plan de Pilot à Production

Executive summary

Enterprises don't need sprawling, multi-year IA programs à create value. avec tight scope et disciplined execution, you can ship un/une production-grade IA capability dans 90 days—measured, compliant, et aligned à Entreprise outcomes. ce/cette Feuille de route provides un/une pragmatic plan pour CIOs et COOs à move de ideas à un/une operating pilot, then à un/une controlled production rollout. le/la/les emphasis is sur measurable Retour sur Investissement, Sécurité-par-design, et change Gestion so adoption sticks.

Why 90 days works

- Focus forces prioritization. Constraining à 90 days eliminates nice-à-have features ce/cette dilute value. - Momentum reduces risk. Shipping un/une small, well-governed pilot surfaces real-world constraints early. - Confidence enables funding. Clear outcomes et baselines establish credibility pour le/la/les next tranche of investment.

Guiding principles

- Entreprise-outcome first: Tie every requirement à un/une KPI ce/cette un/une Entreprise owner cares about. - Sécurité et Conformité par default: Bake dans controls sur day one. Don't retrofit. - Human-dans-le/la/les-loop where it matters: Achieve speed without ceding oversight. - Measure before et after: Establish baselines up front à prove impact. - Keep le/la/les surface area small: Prioritize one or two high-leverage use cases. - Plan pour handover: Document runbooks, train users, et set clear ownership.

Day 0 prerequisites

Before Day 1, confirm: - Executive sponsor et Entreprise owner: un/une VP/Director accountable pour le/la/les outcome et adoption. - Cross-functional team staffed: Product, engineering, data/ML, Sécurité, Juridique/Confidentialité, et Opérations. - Budget et access: Environment access, sandbox datasets, procurement guardrails, et un/une contingency reserve. - Risk et Conformité alignment: Initial policy decisions sur data residency, retention, et acceptable use. - Success criteria defined: Target Métriques, measurement methods, et decision thresholds pour go/no-go.

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

Goal: Select high-Retour sur Investissement use cases, confirm data readiness, design le/la/les Solution et guardrails, et build un/une thin proof of viability.

1) Pick 1–2 high-impact use cases

Use un/une quick scoring model across value, feasibility, et risk: - Customer support deflection: Automated answers avec agent assist; measured par deflection rate et handle time. - Contrat review triage: Clause extraction et risk flags; measured par review throughput et variance dans cycle time. - Sales enablement: Drafting emails et summarizing calls; measured par cycle time et pipeline velocity. - Document processing: Invoice/PDF extraction; measured par straight-through processing (STP) et exception rate.

Selection criteria: - Clear owner et process fit - Contained data scope avec manageable sensitivity - Achievable latency, accuracy, et cost targets - Intégration path ce/cette avoids re-Architecture

Deliverable: Use Dossier one-pagers avec success Métriques, scope, et stakeholders.

2) Establish baselines et target outcomes

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

Deliverable: Baseline report et target thresholds (e.g., "Reduce invoice processing time de 36h à 8h avec ≤2% critical errors, achieve $0.18 marginal cost per Document").

3) Data readiness et access

- Inventory et classify data: Sources, PII, sensitivity, ownership - Create un/une governed sandbox: Mask PII as appropriate, log access - Sampling et annotation: Build small, representative datasets avec ground truth labels et rubrics - Retention et residency decisions: Define what is stored, pour how long, et where

Deliverable: Data readiness memo, sampling plan, glossary, et Qualité rubrics.

4) Architecture et vendor fit

Make decisions early à avoid churn: - Model Stratégie: General-purpose LLM vs domain-tuned; open vs hosted; fallback models pour Résilience - Retrieval (if needed): Choose vector store et ingestion approach; define chunking/indexing rules - Inference placement: Edge vs region vs sur-prem based sur latency, Confidentialité, et cost - Intégration patterns: Event-driven vs synchronous; how à log, monitor, et route exceptions - Procurement: Shortlist vendors; align sur SLAs, pricing, et data handling

Deliverable: High-level Architecture diagram, vendor shortlist, et cost model avec unit economics.

5) Guardrails et Gouvernance design

- Sécurité: Secret Gestion, Réseau boundaries, dependency scanning - Confidentialité: Data minimization, prompt/response redaction, DLP - Safety: Prompt injection defenses, allowed sources, response filters - Accountability: Human-dans-le/la/les-loop thresholds, review queues, override et escalation paths - Auditability: Event et inference logging, correlation IDs, immutable Audit trails

Deliverable: IA policy addendum, risk register, et control checklist.

6) Thin proof of viability

Build un/une narrow spike à de-risk le/la/les riskiest element (e.g., retrieval Qualité or field extraction accuracy) using un/une handful of examples et un/une simple UI or CLI.

Deliverable: Findings report avec precision/recall et latency sur le/la/les sampled dataset; decision à proceed à build.

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

Goal: Ship un/une minimal lovable pilot (MLP) à un/une controlled group, avec end-à-end Qualité, safety, et observability.

1) Implement le/la/les MLP

- Core capability: le/la/les smallest set of features ce/cette deliver end-à-end value (e.g., draft answer, show sources, one-click escalate) - UX ce/cette builds trust: Show citations, confidence indicators, et un/une easy path à correction - Feedback capture: dans-line thumbs up/down avec reason codes; capture agent edits as training signals

2) Technical Architecture

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

3) Evaluation et red teaming

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

4) Sécurité et Conformité checkpoints

- DPIA/PIA as needed - Access controls: SSO, RBAC, least privilege; Service-à-Service auth - Data handling: Encryption à REST/dans transit; explicit retention et deletion policies - Audit logs: Immutable storage pour sensitive actions; reviewer identity attached à overrides

5) Change Gestion et enablement

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

6) Pilot launch (limited cohort)

- Scope: One team or region; 10–50 users pour 2–4 weeks - Support: Slack/Teams channel avec rapid response; office hours; sur-call rotation - Feedback cadence: Weekly review of Métriques et user feedback; fast iteration sur prompts et UX

Deliverables par Day 60: - Pilot live avec controlled cohort - Métriques dashboard et risk report - Runbook pour support, incidents, et rollback - Updated Financier model et go/no-go criteria pour productionization

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

Goal: Harden le/la/les pilot pour production, roll out safely, et prove Entreprise impact.

1) Fiabilité, SLOs, et Résilience

- SLOs: p95 latency, Disponibilité, et error budgets defined per capability - Résilience patterns: Fallback models/providers, timeouts et retries, circuit breakers, et graceful degradation - Kill switches: Feature flags à disable high-risk features instantly

2) Cost Gouvernance et unit economics

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

3) Performance et UX tuning

- Latency targets par step; prefetching et streaming partial responses where appropriate - un/une/B testing of UI patterns ce/cette influence trust et adoption (citations, confidence indicators, action layouts) - Accessibility et internationalization if rolling out globally

4) Gouvernance et lifecycle

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

5) Rollout et Opérations

- Canary Stratégie: 5% à 25% à 50% avec gate checks à each stage - Regional considerations: Data residency, latency, et language support - Support readiness: Tier-1/Tier-2 playbooks, escalation paths, et sur-call coverage - Vendor Gestion: SLAs dans place, usage alerts, et quarterly Entreprise reviews focused sur cost et Qualité

6) Prove value

- Executive readout: Before/after Métriques, savings realized, risk outcomes, et user adoption - Decision: Graduate à "Entreprise-as-usual" avec un/une funded Feuille de route, or iterate further avec le/la/les pilot cohort

Deliverables par Day 90: - Production rollout à 25–50% of target population avec guardrails - Signed-off SLOs, runbooks, et Gouvernance docs - Retour sur Investissement report avec baselines, deltas, et confidence intervals - Next-90-day Feuille de route avec two à three additional capabilities

Common pitfalls et how à avoid them

- Fuzzy success criteria: Fix par defining baselines et targets dans Week 1. - Over-scoped pilots: Ship one capability well; expand later. - Ignoring le/la/les last mile: Invest dans UX trust signals et training; adoption drives Retour sur Investissement. - Weak observability: Without tracing et structured logs, you can't debug or prove value. - Conformité afterthoughts: Engage Confidentialité et Juridique de Day 1; Document decisions. - Cost surprises: Track unit economics early; implement caps et model routing before GA.

After 90 days: Institutionalize, don't just scale

- Operating model: Assign product ownership, establish un/une IA review board, et schedule model evaluations. - Platformization: Reuse retrieval, prompting, logging, et guardrail components à accelerate new use cases. - Portfolio planning: Maintain un/une prioritized backlog avec expected Retour sur Investissement et risk profile per capability. - Continuous improvement: Feed edits et feedback into evaluation pipelines; iterate monthly.

Conclusion

un/une 90-day IA program is enough time à move de slideware à measurable impact—if you narrow scope, build le/la/les right guardrails, et obsess over outcomes et adoption. Start small, prove value, et scale deliberately. le/la/les enterprises ce/cette succeed treat IA as un/une operating capability avec Gouvernance et accountability, not un/une one-off experiment.