Training Staff on AI:

A simple, four‑module curriculum

Module 1 – Foundations & vocabulary. Demystify how modern AI works (prediction, generation, retrieval), what it’s good at, and what it isn’t. Translate jargon into plain English so people can ask better questions. Anchor this in your business goals—sales conversion, faster turnaround, higher NPS.

Module 2 – Prompting & workflow design. Teach a “C‑I‑C” prompt structure: Context (who/what), Instruction (task with constraints), Critique (how to self‑check). Pair prompting with workflow basics: triggers → actions → reviews. Encourage teams to save good prompts as re‑usable templates.

Module 3 – Data privacy & safe usage. Cover sensitive data handling, redaction, approved providers/endpoints, and what not to paste into a model. Introduce the OWASP LLM Top 10 at a high level (prompt injection, insecure output handling) so staff see security as part of their craft, not a blocker.  

Module 4 – Evaluation & measurement. Show how to test prompts (edge cases, tone, bias checks) and how to measure impact (hours saved, cycle time, error rates). Use “AI drafts, human approves” for customer‑facing work until quality trends are solid.

Programme design that sticks

• Start with roles, not tools. Sales, marketing, operations, and finance each get tailored labs with their real apps and data.
• Short sprints. Deliver content in four 90‑minute sessions over four weeks, with hands‑on micro‑projects between sessions.
• Champions network. Nominate an “AI champion” per team who gets extra coaching and becomes the local first‑line helper.
• Sandbox safely. Provide a secure environment with approved models and logging so people can explore without risk.
• Show, don’t tell. Demos of before/after workflows trump theory—record short Looms so wins spread.

OECD research on AI in the workplace shows that adoption succeeds when organisations pair practical training with process change and clear guardrails. Build skills around the work, not in isolation.  

What to teach each function (starter ideas)

• Sales & BD. Lead research brief from CRM + web; objection‑handling library; personalised follow‑ups drafted for review.
• Marketing. Audience‑specific outlines; on‑brand rewrites; content repurposing (webinar → posts → email).
• Operations. SOP drafts; meeting summaries with action tracking; QA checklists generated from policies.
• Customer support. Triage macros; knowledge‑base answer drafts with citations; sentiment tagging.
• Finance. Variance explanations; contract clause extraction; invoice note standardisation.

Each example should be delivered as a tiny playbook: when this happens → do this → here’s the prompt → here’s where to be careful → here’s how we measure it.

Assessment & certification (keep it lightweight)

• Demonstration. Each learner submits a 3–5 minute screen recording of an AI‑assisted workflow they built.
• Rubric. Score clarity of prompt, safe data handling, outcome quality, and measurement.
• Badge. Issue an internal certificate (and a LinkedIn badge) when the playbook is adopted by the team.

Who teaches (and how managers help it stick)

• Internal + external blend. Pair a vendor‑neutral trainer with your own domain experts so examples are authentic. Your people know the edge cases; the trainer supplies the frameworks and prompting patterns.
• Manager enablement. Give managers a short guide on coaching AI use: how to ask for a working demo, where to insist on human review, and how to escalate security questions. Provide weekly check‑ins that focus on outcomes, not tool geekery.

A pragmatic starter toolkit

• Core model access via an approved enterprise endpoint.
• A safe workspace (e.g., a team knowledge base) to store prompts and playbooks.
• An orchestrator (such as Zapier) for simple automations tied to the apps people already use.
• Lightweight evaluation templates to sanity‑check outputs before they reach customers.
• A measurement sheet with the three metrics you picked—kept visible to everyone.

This isn’t about perfection on day one. It’s about lowering the barrier to experimentation while keeping data safe. The result is a team that learns by shipping real improvements, one small win at a time.

Change management: making it normal

• Leadership stance. Model the behaviour—execs share their own prompts and wins.
• Policy clarity. Publish a simple “green/amber/red” guide for data and tools; update quarterly.
• Time to practise. Ring‑fence 1–2 hours per week for experimentation during the programme.
• Recognition. Celebrate shipped automations in all‑hands; tie improvements to team OKRs.

The UK’s AI Opportunities Action Plan emphasises building capability alongside deployment—your programme should do the same: upskill while you implement.  

Measuring ROI (and proving it to finance)

Pick three metrics that matter and instrument them from the start:

1. Cycle time on a core task (e.g., proposal turnaround).
2. Volume handled per person (e.g., leads followed up within 24 hours).
3. Quality proxies (e.g., error rate, CSAT).

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