Why "Train and Leave" Doesn't Work for AI

Every month, Microsoft releases new Copilot features. Every quarter, the underlying AI models change. Every year, entirely new capabilities emerge that didn't exist before. And yet, most organisations still treat AI training as a one-off event.

It's a pattern we see constantly: a company invests in Copilot training, runs a workshop or two, and then moves on. Six months later, their teams are using about 20% of what they learned — if that — while a raft of new features sits completely untouched.

This isn't a training problem. It's a model problem. The "train and leave" approach that works for static software simply doesn't work for AI.

The Pace of Change Is Relentless

Consider what's happened with Microsoft Copilot in just the past few months. In October 2025, GPT-5 started rolling out as the default model in Copilot Chat, bringing an intelligent router that dynamically selects between a fast chat model and a deeper reasoning model depending on what you're asking. That same month, Agent Mode arrived in Word — transforming document creation into an interactive, conversational experience.

Copilot Studio now supports MCP (Model Context Protocol) connections with just a few clicks, opening up integration possibilities that didn't exist six months ago. Computer use capabilities are in public preview, letting agents operate apps and websites directly. File upload support for custom agents. Automated agent evaluation tools. Session persistence. The list goes on.

And this is just one quarter. Every month brings new capabilities that could genuinely transform how your team works — but only if they know about them and understand how to apply them.

The Training Gap Is Real

Research consistently shows the same pattern. According to McKinsey, 48% of employees rank training as the most important factor for AI adoption — yet nearly half feel they're receiving moderate or less support. An Asana report found that 82% of workers say their organisations still haven't provided any training on generative AI.

Gallup research reveals that only 28% of employees in organisations implementing AI strongly agree their manager actively supports their team's use of the technology. The top barrier to adoption? An unclear use case or value proposition (16%), followed by concerns about legal or privacy risks (15%), and lack of training (11%).

The result is predictable. AI adoption starts strong with early enthusiasts, then plateaus. Tools get underused. Investments don't deliver their promised returns. A 2024 report found that 70-80% of AI projects fail to deliver expected benefits — often due to lack of user adoption rather than technical issues.

Why Traditional Training Falls Short

Traditional training models were designed for stable software. Learn Excel in 2020, and most of what you learned still applies in 2025. The core features don't fundamentally change.

AI tools are different. They operate on a completely different update cadence:

Model changes: The underlying AI models themselves evolve. GPT-4.1 replaced GPT-4o as the default in Copilot Studio in October 2025. GPT-5 started rolling out in November. Each model shift brings different capabilities, different strengths, and sometimes different behaviours.

Feature releases: Microsoft ships new Copilot features monthly. Not annually. Not quarterly. Monthly. Each release can introduce capabilities that change what's possible — from Agent Mode in Word to on-canvas slide translation in PowerPoint.

Integration expansion: The ecosystem keeps growing. MCP support, custom agents, computer use, new data connectors — the integration possibilities multiply faster than any single training session can cover.

A one-off workshop simply can't account for this velocity of change.

What Actually Works: Ongoing Partnership

The organisations getting the most from AI aren't treating training as an event. They're treating it as an ongoing capability-building exercise.

This shows up in several ways:

AI Champions programmes: Companies like ServiceNow have identified 1,000 high performers who are avid AI users and enlisted them to train their colleagues. International Motors found that traditional training didn't work — "you think this would sell itself, but it doesn't" — so they helped early adopters become internal influencers instead.

Regular check-ins: Rather than annual refreshers, forward-thinking organisations schedule monthly or quarterly sessions to review new features and identify new opportunities. This isn't about re-training; it's about staying current.

Practical application focus: The most effective AI adoption happens when people immediately apply what they learn to real work. Generic training on "what AI can do" rarely sticks. Training on "how AI can help you specifically with that report you produce every week" does.

Measurement and adjustment: Frontier Firms — organisations Microsoft identifies as leading in AI adoption — don't just deploy AI and hope for the best. They measure adoption, identify gaps, and adjust their approach. Only 24% of companies have deployed AI organisation-wide; the ones that succeed treat it as an ongoing initiative, not a project with an end date.

The Business Case for Ongoing Support

The difference in outcomes is stark. Microsoft's 2025 Work Trend Index found that 71% of leaders at Frontier Firms say their company is thriving, compared to just 39% of workers globally. These firms report being able to take on more work (55% vs 25% globally) and finding more meaningful work opportunities (90% vs 77%).

What sets them apart isn't just that they adopted AI first. It's that they've built ongoing capability around it: continuous training, active support structures, regular reassessment of how AI fits into their workflows.

The firms that "train and leave" don't get these results. They get a spike in usage immediately after training, followed by a gradual decline as people forget what they learned and never discover what's new.

What This Means for Your Organisation

If you're about to invest in AI training, ask yourself: what happens after the workshop ends?

If the answer is "we move on to other priorities," you're likely to see limited long-term return on that investment. AI capabilities are evolving too fast for a one-and-done approach.

Instead, consider building in ongoing support from the start:

• Schedule regular touchpoints — even quarterly check-ins to review new features and identify opportunities can make a substantial difference
• Identify and support internal champions — people who are naturally curious about AI and willing to help colleagues
• Connect training to real workflows — generic AI knowledge fades; specific applications to actual work stick
• Keep your training partner close — work with someone who stays current with the technology and can help you stay ahead

The organisations that will thrive with AI aren't the ones with the best initial training. They're the ones that build ongoing capability — that treat AI adoption as a journey rather than a destination.

The technology isn't slowing down. Your approach to adopting it shouldn't either.

At IQ IT, we work as ongoing AI implementation partners — not just trainers who deliver a session and disappear. If you want to explore what that could look like for your organisation, get in touch.