The Great Adaptation: Why 2026 Is the Make-or-Break Year for AI Upskilling

Randstad's CEO dropped a phrase earlier this month that stuck with me: 2026 is "the year of the great adaptation." Not the great replacement. Not the great disruption. The great adaptation.

I think that framing is exactly right. The question isn't whether AI is changing work — it obviously is. The question is whether people and organizations are adapting fast enough.

And based on what I'm seeing? Most aren't. Not because they don't want to, but because they're going about it wrong.

Team Learning Effective upskilling is hands-on and role-specific, not generic training modules

The Numbers That Should Wake You Up

Mercer surveyed 12,000 people across 17 countries for their 2026 Global Talent Trends report. A few numbers stood out:

97% of investors said funding decisions would be negatively impacted by firms that fail to systematically upskill workers on AI
Over 75% of investors are more likely to invest in companies that provide AI education
40% of employees fear losing their job to AI (up from 28% in 2024)
But only 23% of companies have a structured AI upskilling program

That gap between the 97% investor expectation and the 23% company execution is where the real story is. Everyone agrees upskilling matters. Almost nobody is doing it well.

Why Most AI Training Programs Fail

I've sat through my share of corporate AI training. Most of it falls into one of these traps:

The "Everyone Watch This Webinar" Approach

A 90-minute presentation about "the future of AI" with glossy slides about machine learning, neural networks, and transformation. Everyone nods politely. Nobody changes their actual work behavior.

This fails because it's too abstract. People don't need to understand how transformers work. They need to understand how AI can help them do their specific job better.

The "Here's a Tool, Figure It Out" Approach

Company buys ChatGPT Enterprise licenses for everyone. Sends an email saying "AI is now available!" and considers the job done.

Three months later, 15% of people are using it regularly. The rest tried it once, got a mediocre result, and went back to their old workflow.

This fails because tool access isn't the same as tool fluency. Giving someone a chainsaw doesn't make them a lumberjack.

The "Mandatory E-Learning Module" Approach

A series of multiple-choice quizzes about AI terminology. What does "LLM" stand for? What is "prompt engineering"? Check the box, get the certificate.

This fails because knowing vocabulary isn't the same as building capability. Nobody ever improved their work by learning to define "retrieval-augmented generation."

The "Send the Tech Team to a Conference" Approach

Only the IT department gets serious AI training. Everyone else gets the webinar from approach #1.

This fails because AI isn't just a technology initiative. It's a business capability that needs to be embedded across every function.

What Actually Works

I've seen successful upskilling programs, and they share some common patterns:

1. Start with Workflows, Not Technology

Don't begin by teaching people about AI. Begin by mapping their actual daily workflows and identifying where AI can make a difference.

For a marketing manager, that might be:

Drafting initial copy for campaigns
Analyzing campaign performance data
Generating variations for A/B testing
Researching competitor positioning

For an accountant:

Reconciling transactions
Identifying anomalies in expense reports
Generating financial summaries
Answering audit queries

Each role has different workflows, so each role needs different AI training. One-size-fits-all doesn't work.

2. Pair Training with Immediate Practice

The most effective programs I've seen follow this structure:

Week 1: Identify 3 tasks in your role that take the most time
Week 2: Learn to use AI for the first task (with guided examples)
Week 3: Practice daily, document what works and doesn't
Week 4: Learn to use AI for the second task
Week 5: Practice both, share learnings with your team
Week 6: Learn to use AI for the third task
Week 7-8: Refine your workflow, measure time saved

The key is that people are applying what they learn to their real work immediately, not studying theoretical concepts.

3. Create Champions, Not Just Trainees

In every team, there's someone who's naturally curious about new tools. Find that person, invest heavily in their AI skills, and make them the team's AI champion. Their job is to:

Try new tools and features first
Develop team-specific prompts and workflows
Help colleagues who get stuck
Share wins and lessons learned

One champion per 8-10 people works well. It scales better than trying to make everyone an expert simultaneously.

4. Measure Outcomes, Not Completion

Don't track how many people finished the training module. Track:

How many people are actively using AI tools weekly?
What's the average time saved per person per week?
What new capabilities has the team developed?
How has output quality or volume changed?

If people completed the training but nothing changed in their work, the training failed — regardless of completion rates.

5. Address the Fear Directly

This is the one most companies skip, and it's the most important.

People are scared. 40% of employees think AI might take their job. If you launch an upskilling program without addressing that fear, people will resist it — consciously or unconsciously. Why would someone enthusiastically learn the tool they think is replacing them?

Be direct: "We're investing in AI to make our team more productive, not smaller. We're investing in your AI skills because we believe you'll be more valuable with these capabilities. Here's our commitment to you." And then follow through on that commitment.

The Leadership Problem

Here's something that doesn't get discussed enough: many leaders are just as confused about AI as their teams, but they're expected to set the strategy.

I've had conversations with executives who:

Don't personally use AI tools but mandate their adoption
Can't articulate what "AI-ready" means for their organization
Set arbitrary targets ("increase AI adoption by 50%") without understanding what that looks like
Delegate the entire AI strategy to IT

This doesn't work. Leaders need to build their own AI fluency first. Not deep technical knowledge — practical fluency. They need to understand what AI can and can't do, have realistic expectations, and make informed decisions about where to invest.

What Good Leadership Looks Like

The best AI leaders I've worked with:

Use AI themselves daily. They have direct experience with the tools, so they make better decisions about adoption and can empathize with their team's learning curve.

Set specific, measurable goals. Not "adopt AI" but "reduce customer response time by 30% using AI-assisted support tools by Q3."

Allocate real time for learning. Not "learn AI on your own time" but "4 hours per week are blocked for AI skill development." If it's important enough to mandate, it's important enough to make time for.

Celebrate early wins publicly. When someone uses AI to solve a problem or save time, highlight it. This normalizes AI use and reduces the stigma some people feel about "cheating" by using AI.

Are honest about what they don't know. AI is evolving so fast that nobody has all the answers. Leaders who admit uncertainty and learn alongside their teams build more trust than those who pretend to have it figured out.

Building an AI-Ready Culture

Beyond individual training, the organizational culture matters enormously.

Experimentation Must Be Safe

If someone tries an AI tool on a project and it doesn't work well, the response should be "What did we learn?" not "Why did you waste time on that?" Innovation requires permission to fail.

Set up a channel (Slack, Teams, whatever your team uses) dedicated to AI tips, tricks, and learnings. Encourage people to share prompts that worked, tools they found useful, and mistakes they made. Make the collective learning visible.

Policies Need to Be Clear

People need to know:

What data can and can't be put into AI tools?
What tasks require human review of AI output?
Who's responsible when AI makes an error?
What tools are approved for use?

Ambiguity causes paralysis. Clear guardrails enable confident experimentation.

Quality Standards Must Evolve

AI makes it easy to produce more. That doesn't mean more is always better. Quality standards need to account for AI-generated content. "We used AI" isn't an excuse for sloppy output — it should mean better output, produced more efficiently.

What Individuals Can Do Without Waiting

If your organization isn't providing structured upskilling (and statistically, it probably isn't), here's what you can do on your own:

This Week

Pick one AI tool (ChatGPT, Claude, Gemini — any of them) and use it for a real work task
Spend 30 minutes exploring what it can do for your specific role
Ask it to help with something you find tedious

This Month

Develop 3-5 prompts that you use regularly for your work
Share your experience with a colleague
Read one article about AI in your specific industry

This Quarter

Build AI into at least one regular workflow
Measure the time or quality impact
Teach someone else what you've learned

The barrier to getting started is lower than most people think. You don't need a training program or a certification. You need curiosity and 30 minutes.

The Bottom Line

The great adaptation isn't a single event. It's an ongoing process that started a couple of years ago and will continue for years to come. The organizations and individuals who take it seriously — who invest real time and resources, not just lip service — will come out ahead.

And here's the part nobody wants to hear: there's no shortcut. AI upskilling takes effort, practice, and sustained commitment. A one-day workshop won't do it. A tool license won't do it. Only consistent, practical, role-specific skill-building will do it.

The good news is that the payoff is real. The people and teams who've done this work are measurably more productive, more confident, and — counterintuitively — less anxious about AI. Knowledge is the best antidote to fear.

Resources

Need help building an AI upskilling program for your team? CODERCOPS has done this — from executive workshops to hands-on team training, we focus on practical skills that change how people actually work.