Good question — business leaders don’t need to become ML engineers, but they do need a clear, practical understanding of what AI can and cannot do, how to evaluate risk and value, and how to lead an organization that uses AI responsibly. Below is a compact, actionable learning playbook you can use yourself or adapt for your leadership team.

1. Mindset and goals (why learn)

• Purpose: move from “AI is hype” to “AI is a tool we can use to achieve specific business outcomes.”
• Focus on outcomes (revenue, cost, risk, customer experience), not on specific models or vendors.
• Treat learning as iterative: small experiments that prove value and build capabilities.

2. Core concepts every leader should know (non-technical)

• What AI/ML is and isn’t: supervised vs. unsupervised learning, generative AI, fine-tuning, model inference.
• Data basics: quality, bias, lineage, governance, privacy.
• System behavior: hallucinations, brittleness, distribution shift, model drift.
• Operational needs: compute, data pipelines, MLOps, monitoring.
• Legal/ethical/regulatory considerations: IP, privacy laws, fairness, explainability.

3. Practical ways to learn (fast, role-appropriate)

• Short executive courses (1–8 hours): choose one that focuses on business applications and governance. (Look for vendor-neutral, instructor-led or well-reviewed online programs.)
• Readable primers: concise books and whitepapers about AI strategy, risks, and governance.
• Podcasts and newsletters: for continual, bite-sized updates and perspectives.
• Internal briefings: ask your data/ML teams for 30–60 minute demos showing real use cases and failure modes.
• External briefings: vendor demos only after you define the problem and metrics — use them for validation, not education.
• Peer learning: talk to other leaders in your industry about what worked and failed.

4. A 30/60/90-day leadership learning plan

• 0–30 days (awareness)
  • Read 2–3 high-level primers and one governance paper.
  • Attend 1 executive course or webinar.
  • Ask your teams for short demos of existing data assets and any ML pilots.
• 31–60 days (evaluation)
  • Map 3–5 business processes that could benefit from AI; prioritize by impact and feasibility.
  • Run a lightweight workshop with stakeholders (product, legal, IT, security) to identify risks and data needs.
  • Commission a rapid discovery/proof-of-concept (POC) for the top priority use case (4–8 weeks).
• 61–90 days (action)
  • Use POC results to decide: scale, iterate, or stop.
  • Put basic governance guardrails in place (data controls, logging, human oversight).
  • Define budget, team structure, KPIs, and a hiring/partnering plan.

5. How to evaluate use cases

• Value: measurable business metric improvement (e.g., conversion lift, cost per contact).
• Feasibility: available quality data, engineering effort, latency/compliance constraints.
• Risk: reputational, regulatory, safety, privacy.
• Speed to learn: choose at least one “quick win” pilot that can prove value in 6–12 weeks.

6. Governance and risk controls leaders must enforce

• Data governance: provenance, retention policy, access controls.
• Model lifecycle governance: versioning, testing, validation, deployment approvals.
• Monitoring: performance, fairness, drift detection, and automated alerts.
• Human-in-the-loop: decisions that materially affect customers/employees should have human oversight and appealability.
• Documentation: decision logs, model cards, data sheets for datasets.
• Legal/compliance: involve privacy, security, and legal early; maintain audit trails.

7. Building capabilities: who and how

• Core team: product leader, data engineer, ML engineer or vendor partner, ethical/compliance lead, and UX/designer.
• Hire vs. partner: start with partners for speed, hire for core IP and long-term ownership.
• Upskilling: run internal workshops, sponsor key leaders for executive AI programs, and rotate people through hands-on projects.

8. Metrics to track (examples)

• Business KPIs: revenue lift, cost savings, conversion rate, retention.
• Model KPIs: accuracy/precision/recall where relevant, latency, uptime.
• Operational KPIs: time to deploy, mean time to detect/resolve issues, data pipeline freshness.
• Safety/KPI: number of incidents, complaint rate, fairness metrics.

9. Hands-on experimentation checklist for a pilot

• Define a single, measurable objective and baseline metric.
• Agree success criteria (numeric threshold + qualitative review).
• Identify required data and confirm legal/consent constraints.
• Build a minimal model or prototype (can be prompt-based for generative cases).
• Run a small, controlled test with monitoring and human oversight.
• Evaluate results against baseline, measure harms, and document learnings.

10. Ongoing learning resources (types to look for)

• Executive short courses (business-focused AI strategy and governance).
• Industry reports from reputable consultancies and standards bodies.
• Books aimed at leaders (strategy + case studies).
• Podcasts/newsletters for current developments.
• Vendor-neutral communities and conferences to hear real deployments and failures.

11. Common pitfalls to avoid

• Chasing technology instead of business value.
• Starting without the right data or governance (leads to costly failures).
• Overreliance on vendor demos without POCs.
• Ignoring ethical, legal, and reputational risk until after deployment.
• Expecting immediate full automation — many early wins are augmentation, not replacement.

12. Quick starter resource types (examples to search for)

• Executive AI strategy courses (look for university or reputable provider executive programs).
• Practical books on AI for managers and ethics/governance whitepapers.
• Case studies focused on your industry for realistic expectations. (If you want, I can look up current, specific courses, books, podcasts, or industry case studies tailored to your sector and time availability.)

If you want, tell me your industry (e.g., banking, healthcare, retail, manufacturing), the size and maturity of your data team, and whether you want a 3‑ or 6‑month adoption plan — I’ll produce a tailored, prioritized roadmap and a short list of concrete learning resources.