Procure AI without locking into the wrong stack

AI vendors move fast and contract templates lag. Without AI-specific terms, your data may train vendor models, logs may be unavailable for audit, and silent model updates may break compliance workflows overnight.

Procurement prevents duplicate spend — business units buying overlapping copilots while enterprise agreements exist.

Regulated buyers need demonstrable vendor oversight for third-party risk programmes. Procurement artifacts feed those reviews.

Good procurement accelerates sanctioned deployment. Clear security gates and standard clauses shorten cycle time after selection.

Renewal season exposes weak original terms — auto-renew on unfavourable inference pricing, missing deletion clauses, vendor model changes without notice. Procurement sets defaults that protect year two.

Align evaluation committee calendars with security architecture review slots — misaligned schedules silently add quarters between vendor selection and production go-live.

Programme elements: (1) Requirements linked to strategy and readiness — not generic AI wish lists. (2) RFP sections on integration, logging, residency, SLAs, support. (3) Evaluation rubrics shared with business, IT, security. (4) Proof-of-concept contracts with data handling addenda. (5) Master agreement clauses — training opt-out, deletion, audit, liability, change notification for models. (6) Onboarding checklist — SSO, DPIA if needed, enablement plan.

Involve /solutions/* owners when buying domain workflow tools — finance, HR, legal — so requirements reflect operational reality.

Align with /products/* when Arcloops products are in scope to avoid redundant third-party buys for the same workflow.

Maintain a clause playbook for AI buys: training prohibition, incident notification SLAs, subprocessors, benchmarking rights, and service credits for availability — adapted by tier, not reinvented per deal.

Translate components into a RACI snippet: who owns each element, who approves exceptions, and which forum reviews metrics. Without names and dates, components remain abstract bullets nobody executes.

Copying SaaS RFPs without AI annexes leaves data and model gaps.

Letting vendors drive POC scope without kill criteria extends trials indefinitely.

Procurement decoupled from security — signed deals blocked at architecture review.

Ignoring total inference cost — per-seat plus consumption overages blow budgets post-signature.

Treating professional services SOWs as afterthought while license gets signed — implementation failure often lives in services scope, data access, and acceptance tests.

Teams often repeat these mistakes after reorgs or vendor changes — keep a short incident log so new managers inherit lessons instead of rediscovering the same failure modes.

Auto-renewing AI contracts without benchmarking inference pricing against current usage — finance discovers overages only after the renewal window closes.

Skipping embedded SaaS AI in renewal reviews — toggles activate on enterprise data without procurement or security quorum.

We help procurement teams write AI-literate RFPs and red-line priorities based on readiness and governance tiering. Technical evaluators score proofs; procurement converts results into contract language.

We flag when buying is premature — data or policy gaps should close first — and when build or Arcloops delivery paths are more appropriate than vendor shelfware.

Handoff includes onboarding runbooks for security and enablement so purchased tools reach production, not shelfware.

We join evaluation sessions as technical scorers, not resellers — findings flow into procurement red-lines with testable acceptance criteria tied to your workflows.

Engagements exit with a handover checklist tied to this guide — owners, dashboards, and policy links — so your team can operate without consultant dependency after hypercare ends.

Align procurement milestones with security architecture review slots — calendar friction silently adds quarters to sanctioned deployments.

Intake — business sponsor and procurement owner confirm requirements tie to strategy and readiness evidence, not generic AI wish lists. Security, legal, and IT named on evaluation committee with quorum rules before RFP release; embedded SaaS AI features in renewals routed through same path.

RFP — include integration scenarios, logging, residency, model-change notification, training opt-out, subprocessors, inference pricing, and support SLAs. Evaluation rubric shared with scorers before vendor demos to reduce halo bias; proof scripts reflect your workflows.

POC — legal approves data samples; kill criteria and max duration documented in writing. Technical team scores proofs against workflow scripts and security tests, not slide decks; findings feed contract red-lines immediately.

Contract — clause playbook covers deletion, audit rights, incident notification, consumption caps, and exit assistance. Finance models run-cost including inference overages, not seat count alone; renewal dates entered in vendor registry.

Onboarding — SSO, DPIA where required, enablement plan, and security architecture sign-off before wide rollout. Named product owner accepts handover; 90-day shelfware review triggers remediation or termination with documented rationale. Legal confirms training opt-out and deletion clauses match tenant settings verified by IT admin.

Phase 1 (weeks 1–2) — sponsor and process owner agree scope, baseline metrics, and prohibited automations; security confirms data classes and logging defaults; legal confirms jurisdiction and retention. Phase 2 (weeks 3–8) — pilot on one queue or entity with hypercare office hours; champions named per site; override sampling weekly. Phase 3 (month 3+) — steering reviews expand/stop/fix with evidence; only then fund multi-entity rollout. Skipping Phase 1 produces demos that fail audit; skipping Phase 2 produces shelfware after launch email.