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PM scopes the programme based on your team roles, existing workflows, and the specific business outcomes you want from AI adoption. The AI Enablement Specialist runs a discovery session with team leads from each department to identify the 3-5 highest-value use cases per role not generic AI capabilities but specific tasks your team does today that AI can handle or accelerate. The specialist designs a curriculum where every exercise uses your actual documents, customers, and data rather than hypothetical examples. Teams that train on real work tasks adopt AI 3-4x faster than teams that train on generic content because they immediately see the relevance to their own job.

Hands-on. Every session is 30% concept introduction and 70% live practice. Participants use AI tools on their actual work tasks not watching a demo or doing a hypothetical exercise. By the end of a 4-hour workshop, every participant has: used an LLM to generate a first draft of something they need to write this week, learned 3-5 tested prompt patterns for their specific role, experienced the quality difference between a generic prompt and a structured prompt on their own content, and identified the 2-3 tasks they will change their workflow to use AI for starting Monday. This is what drives adoption: people see the value on their actual work before the session ends.

Yes customisation is standard. A training session for a legal team looks completely different from one for a software development team or marketing team, even learning the same AI tool. For a legal team: contract review prompts, clause extraction from complex documents, regulatory research workflows, and specific AI limitations in legal contexts. For a development team: code review prompts, test generation, documentation drafting, debugging assistance, and AI coding assistance integration into your PR workflow. For a marketing team: copy generation with brand voice controls, competitive research synthesis, SEO content workflows, and campaign brief drafting. The prompts, exercises, and examples are all built specifically for your industry and workflow.

PM designs measurement before training starts. Pre-training baseline: survey participants on 5-10 specific weekly tasks asking how long each takes and whether they currently use AI. Post-training 2-week check-in: repeat the survey asking which tasks now use AI assistance and the estimated time saving. Tool analytics: review weekly active users vs. licences to identify which team members have adopted vs. which need additional support. Outcome metrics tied to your business goals: content output volume, support ticket handle time, code review throughput, or any metric reflecting the specific productivity improvement targeted. PM delivers a 30-day adoption report and identifies users who need additional one-on-one support.

Self-study (giving employees access to AI tools and documentation) results in approximately 15-20% adoption rates after 30 days most employees try the tool once, do not get impressive results without knowing how to prompt effectively, and stop using it. Structured hands-on training with role-specific prompts and exercises results in 60-80% active adoption after 30 days, with adopting employees reporting 2-4 hours per week in time savings. At a 50-person team with average salary cost of $60 per hour, 60% adoption (30 people) saving 3 hours per week equals 90 hours saved per week approximately $5,400 per week in labour cost recaptured. The training investment is typically recovered in the first 2-3 weeks.

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