Executive summary
People change how they work only when they are both willing and able. Most AI upskilling programs build neither. They provide access to tools and a course, then wonder why use stalls.
- Canadian CEOs expect 53% of their workforce to need upskilling for their current roles by 2028, and 29% to need reskilling for new roles.1
- 64% of workers used AI at work in the past year, up ten points, while the share with the learning resources they need fell from 59% to 51%.2
- 63% of employers name skill gaps as the biggest barrier to transforming their business.3
Leaders who build an AI-ready workforce work on both ingredients at once:
- Willing: they answer the "what does this mean for me" question, show proof from peers and make it safe not to know yet.
- Able: they train people on their own work, make time to learn and put managers ahead of their teams.
Every change needs people who are willing and able
When I teach change, I come back to one idea. For people to change how they work, they have to be willing and they have to be able. Willing means they believe the change is worth it and safe for them. Able means they have the skills, the time and the tools to do it.
If people are willing but not able, you get frustration. They try, struggle and go back to the old way. If they are able but not willing, you get compliance on the surface and workarounds underneath. AI tests both at once. It asks people to learn a new skill on top of a full job, and it raises a question many won't say out loud.
My companion paper, AI Adoption Is a Leadership Problem, covers where to aim AI and how to measure the return. This paper covers the people who decide whether any of it happens.
AI use is rising faster than the support behind it
PwC's 2026 Hopes and Fears survey of more than 49,000 workers found AI use at work up ten points in a year, while access to the learning people need fell eight points.2 Use is going up and support is going down, the opposite of what any leader would design.
The size of the prize is well measured. A study of customer support agents found an AI assistant raised productivity by 14% on average and by 34% for newer, less experienced workers.4 The tool helped most where skills were thinnest. That only happens when people are trained and trusted to use it.
Fear decides willingness, and most programs never address it
Stanford researchers found the average U.S. worker believes there is a 20% chance of losing their job to generative AI within two years, whether or not they use it at work.5 People who believe that will not volunteer to automate their own tasks.
When a leader announces an AI program, the questions asked in the room tend to be about logistics. The questions that decide whether people are willing get asked afterwards, between colleagues. Will my role change? Will I still be needed? Who decides? When leaders don't answer, people answer for themselves, usually with the worst case.
AI upskilling breaks down in six places
Three breakdowns hit willingness. Three hit ability.
01. The "what about me" question goes unanswered. Without a clear account of how roles will change and how people will be supported, fear wins.
02. There is no proof from people like me. Executive memos about AI's promise don't persuade a payroll specialist. A colleague showing a real time saving does.
03. It isn't safe to say "I don't know how." If admitting you're behind feels risky, people hide it and avoid the tools.
04. Training is generic and one-off. A lunch-and-learn on writing prompts doesn't help an underwriter, a store manager or a planner. People need practice on their own tasks.
05. There is no time to learn. Change is additive. People learn AI on top of a full job, and if nothing comes off the plate, learning is the first thing to go. Of the 751 organizations Emergn surveyed, 42% of respondents said they didn't get enough training during their last change.6
06. Managers are learning alongside their teams. Front-line managers carry most of the change. If they aren't confident, their teams won't be either.
Build willingness and ability together
Make people willing. Say plainly what AI will and won't be used for, how roles are expected to change and what support people will get. Put early users in front of their peers. Make "I'm still learning this" an acceptable thing to say in a team meeting, starting with leaders saying it themselves. This fixes breakdowns 01 to 03.
Make people able. Train in short sessions on each team's real work, with practice built in. Free up time by stopping something, so learning has room. Train managers first so they can coach. Then check whether people are still using the tools a month later. This fixes 04 to 06.
The workforce readiness test
Six questions, one for each breakdown.
- Have leaders told people, in writing and in person, how AI will change roles and what support they'll get?
- Have peers who use the tools shown their colleagues what worked?
- Would someone on your team feel safe saying they don't know how to use the tools yet?
- Is AI training built around each team's actual tasks?
- What have you stopped or paused so people have time to learn?
- Were your managers trained before their teams?
What organizations with AI-ready workforces do in practice
1. They publish the deal. A short, plain statement of what AI is for, what it isn't for and how people will be supported, repeated by every leader.
2. They run peer demos. Five minutes at a regular team meeting where a colleague shows a real task done faster with AI.
3. They lead with their own learning. Senior leaders share what they're still figuring out, which makes it normal for everyone else.
4. They train on the work. Every session uses the team's own documents, data and tasks, and people leave with something they can use that afternoon.
5. They protect learning time. A report, a meeting series or a deadline moves to make room, and that decision is visible.
6. They train managers first and measure use. Managers get a head start and coach their teams, and the organization tracks weekly use a month after training.
Bring The Next Big Thing to your event
The Next Big Thing is my keynote on AI and digital transformation that actually moves the needle. I build a plain-language foundation in the room so everyone starts from the same place, then show leaders how to make people willing and able to use AI, and where it pays back.
I deliver it as a 45- to 90-minute keynote, in person or virtual, tailored to your industry and audience, with a custom tip sheet your attendees can use after the event. It works for leadership conferences, HR and learning events, and all-staff kickoffs where people want to know what AI means for them.
Conclusion: people decide whether AI works
Leaders choose the AI tools. People decide whether those tools change anything. With more than half of Canadian workforces needing new skills by 2028, the organizations that pull ahead will be the ones that build willingness and ability with the same care they put into choosing the technology.
None of it is expensive next to the cost of tools nobody uses.
Read next: AI Adoption Is a Leadership Problem
Frequently Asked Questions
What is AI upskilling?
AI upskilling is helping people build the skills to use AI well in the job they already have. Reskilling prepares people for a different role. IBM found Canadian CEOs expect 53% of their workforce to need upskilling and 29% to need reskilling by 2028.
What does "willing and able" mean in change management?
It is Suzanne Knight's way of describing the two things people need to change how they work. Willing means they believe the change is worth it and safe for them. Able means they have the skills, time and tools. Most programs build one and forget the other.
How do you reduce employee fear of AI?
Be specific about what AI will and won't be used for, how roles will change and what support people will get. Let peers who use the tools show what they found, and make it acceptable to say you're still learning.
Why does AI training often fail?
It is usually generic, one-off and squeezed on top of a full workload, and managers are learning at the same time as their teams. Training sticks when it uses each team's real work, time is freed up to learn and managers are trained first.
Who benefits most from AI tools at work?
Research on customer support agents found an AI assistant raised productivity 14% on average and 34% for newer, less experienced workers. Done well, AI can lift the people who are still building their skills.
Who is this keynote for?
Leadership teams, HR and learning leaders, and organizations rolling out AI to their workforce. It suits leadership conferences, HR events, kickoffs and all-staff sessions, and each talk is tailored.
About Suzanne Knight
Suzanne Knight, MBA, MA, is a keynote speaker and the CEO of mBolden, a Toronto-based management consulting firm (mbolden.co). She has led transformation in the Ontario government, at Deloitte for nearly eight years, as Director of Organizational Effectiveness at Philip Morris International in Canada, and as VP, Transformation Services at Walmart Canada.
She has advised more than 80 organizations in 14 countries, including Fortune 500 companies and governments, and has spoken on more than 65 stages in 18 months. Her TEDx talk is The myth of doing it all. Her keynotes are The Next Big Thing, Never Waste a Good Crisis, The Trust Advantage and Create More Time.
Sources and references
- IBM Canada. (2026, June 22). Can Canadian organizations deliver in the next era of business AI? Source ↗
- PwC. (2026). Global Workforce Hopes and Fears Survey 2026 (49,364 workers in 48 countries and territories). Reported by Benefits and Pensions Monitor. Source ↗
- World Economic Forum. (2025, January). Future of Jobs Report 2025. Source ↗
- Brynjolfsson, E., Li, D., & Raymond, L. (2023). Generative AI at Work. NBER Working Paper 31161. Source ↗
- Hartley, J. S., Jolevski, F., Melo, V., & Moore, B. (2026, August). Job Loss Fears in the First Years of Generative Artificial Intelligence. Stanford Institute for Economic Policy Research. Source ↗
- Emergn. (2025). The Global Intelligent Delusion (751 organizations). Reported by Consultancy.uk. Source ↗