Originally published on LinkedIn on November 18, 2025. Revised in September 2026 to explain a few terms along the way.
AI won't fix a broken workflow or a bad habit on its own — it tends to make whatever is already happening happen faster, for better or worse. If a team hasn't been trained, adding AI on top just speeds up the mistakes, data leaks, and risk that were already there. Training people before — and during — AI adoption tends to produce safer, higher-quality results. That starts with governance (the rules a company sets for how AI can be used), role-based training, and clear guardrails for safe use.
What taking AI training seriously actually looks like
1) Training is the multiplier
Microsoft's 2025 Work Trend Index makes the case that leaders need to invest in reskilling their people and provide ongoing coaching — not one-off webinars — to help people rebuild how they do their daily work around AI. Enablement (getting people ready and able to use a new tool well) should be designed like a product: built around specific tasks, taught through hands-on practice, and improved continuously rather than delivered once and left alone.
2) Start with policy and practice, not tools
McKinsey's 2025 survey found that many companies are still stuck between small pilot projects and AI actually being used at scale, because it was never built into how the business runs day to day. Policy has to become practice: what can and can't be shared with an AI system, how outputs get reviewed, and when something needs to be escalated to a person. These should be built into the workflow as checklists, not left as a document nobody reads.
3) Make it continuous
Adoption goes up when employees are given structured guidance and real time to practice inside their actual work, not a separate training environment. A network of internal champions, regular office hours, and role-specific “prompt clinics” — short working sessions on how to get better results from AI — organized by department (sales, support, finance, engineering) tend to work well.
4) Train people to manage AI, not just use it
Microsoft's Work Trend Index describes this moment as one that requires honest conversations, intentional communication, and a real investment in reskilling. Enablement should be reframed around managing AI “teammates” — AI systems, sometimes called agents, that can carry out multi-step tasks with less direct supervision. That means teaching people when to hand a task off to AI, how to check and override what it produces, and how to handle the exceptions it can't. Microsoft's framework describes this as a progression — AI as an assistant, then as something closer to a digital colleague, then running whole processes on its own — and that progression is a useful way to set expectations and design training exercises matched to wherever a company actually is on that path.
If nobody's taking charge of this, take charge of it yourself
Book a room. Invite one leader from each part of the business. Pick one high-value workflow per team. Decide together on the outcome, the guardrails, the method, and who owns it. Review it again in two weeks. AI will only move as fast as the people using it are trained to move. Train first, deploy second, and the result tends to be safer, faster, and better.
That's the work we do at AI Catalyst Institute: hands-on AI training built for businesses, designed to make training-first adoption easier.
Interested in learning more? Contact: rob@aicatalystinstitute.com