Pillar overview: This concise guide helps non‑technical leaders run focused AI pilots that deliver measurable outcomes. Use a Pillar + Cluster approach: one comprehensive pillar post that explains the strategy and links to short cluster posts that dive into specific use cases, playbooks, and templates—improving internal linking and SEO while making adoption practical.

What you’ll gain: clear, outcome‑focused pilots (eg. reduced handle time, higher email open rates, fewer maintenance incidents), simple evaluation plans, and governance steps to scale safely.

Core pilot playbook:

• Start small: pick one high‑value, repeatable process owned by a domain lead.
• Measure first: define 2–3 KPIs (time, cost, quality) and a control vs. treatment plan.
• Cross‑functional team: include a domain owner, ops lead, and delivery partner.
• Iterate & scale: fix data or edge cases, reuse pipelines, and document monitoring.

Governance & monitoring: run a DPIA, log inputs/outputs/versions, add periodic bias checks, and require human oversight for material decisions. Track business KPIs and technical metrics (accuracy, calibration, latency) with dashboards and alerts.

Cluster posts (short, linkable resources):

• Personalization pilot (email subject lines, A/B tests) — /clusters/personalization
• Predictive maintenance (single‑machine sensor pilot) — /clusters/predictive‑maintenance
• Contract review assistant (one contract class) — /clusters/contract‑automation
• Data readiness checklist & labeling playbook — /clusters/data‑readiness
• Governance checklist & monitoring templates — /clusters/governance

Evidence & vetting: validate vendor claims with reproducible tests, benchmark against published case studies (note date, sample size, metrics), and run micro‑replications before scaling.

Next steps: run a 4–8 week pilot with clear success thresholds, capture lessons learned in a short memo, and publish cluster posts that document the how‑to so other teams can reuse your work.