AI adoption is often assumed to belong to younger or more digitally confident employees. But successful AI adoption across teams depends on more than speed or enthusiasm. In the next article in our series on preparing for the AI shift, Patrick Walsh, our Chief Commercial Officer, looks at why organisations need to combine technological curiosity with business experience.
There is a quiet assumption inside many organisations that AI is a young person’s game, and it’s easy to see why.
Younger employees may be faster to experiment with new tools. They may be more comfortable asking questions of ChatGPT, testing prompts, or trying new platforms. They may be less attached to established ways of working. They may appear more naturally fluent in the technology.
But the assumption is incomplete.
AI adoption does not succeed because the youngest employees move fastest.
It succeeds when technological curiosity is combined with business experience. That distinction matters because AI is not just about knowing how to use a tool. It is about knowing where the tool should be used, what risks it creates, what context it needs, and what business outcome it should improve.
That is often where experienced people matter most.
Successful AI adoption across teams depends on bringing those perspectives together.
AI rewards experimentation. People who try things learn faster. They discover use cases. They build confidence. They show others what is possible. This is why digitally confident employees can be powerful catalysts. They can help create momentum. They can reduce fear. They can show that AI is not as intimidating as it may seem.
But curiosity alone does not create business value.
A person may be excellent at using AI tools but still not understand which workflows matter most. They may create impressive outputs that do not solve important problems, automate a task without understanding the wider process or miss risks that experienced operators would spot immediately.
Curiosity opens the door. Experience helps decide which door is worth opening.
Domain expertise is essential
In most organisations, the people with deep experience know things that are not written down. They know:
– where processes break
– which exceptions matter
– why a workflow exists in its current form
– which customers are difficult
– where judgement is required
– where the business loses time, money, trust, or control
That knowledge is critical to AI adoption. Without it, organisations risk applying AI to the wrong problems. This is why AI should not be left only to the most enthusiastic users or the most technical teams. It needs the people who understand the work.
AI capability building has to include domain expertise, not just tool confidence.
When employees resist AI, leaders often interpret that resistance as fear, laziness, or lack of curiosity. Sometimes that is true. But often, resistance is a signal that something has not been explained, designed, or supported properly.
Employees may resist because they do not understand how AI applies to their role. They may fear job displacement, worry about making mistakes, be unsure what data they can share, or feel that leadership is imposing a change without understanding the reality of their work.
These concerns should not be dismissed. They should be investigated, as resistance often points to gaps in communication, training, governance, or workflow design.
This is where AI change management becomes essential. A strong AI adoption approach does not simply tell people to get on board. It helps them understand what is changing, why it matters, how to use the tools, and where their judgement remains essential.
The best model: curiosity plus experience
AI adoption works best when curiosity meets experience. That means bringing together people who are willing to experiment with people who understand the business deeply.
The best AI work happens at the intersection of different perspectives. A tech-savvy employee knows what a tool can do. An experienced operator knows where it actually fits. A specialist knows how to connect it safely. A manager knows how it shifts the team dynamics. And a leader knows which ultimate goal matters most.
This is exactly why AI cannot be forced through a single department. It demands a cross-functional effort that loops in business owners, tech, risk, legal, HR, and front lines.
Most importantly: the people closest to the work need a voice in shaping how AI is used. Otherwise, adoption becomes something done to them, not with them.
Leaders can take several practical steps to avoid the “young person’s game” trap.
1. Identify AI champions across levels
Do not only choose the youngest or most technical employees.
Look for people who are curious, respected, practical, and close to important workflows. A strong AI champion may be a mid-career manager who understands the business deeply and is willing to experiment.
2. Pair digital confidence with domain expertise
Create small teams that combine tool fluency with operational knowledge.
For example, pair someone comfortable with AI tools with someone who owns the process being improved.
3. Make learning role-based
Experienced employees do not need generic hype. They need to see how AI applies to their actual work. Show them relevant examples. Give them practical exercises. Help them connect AI to real tasks.
Role-based AI training is important because different people need different levels of confidence, context and support.
4. Treat resistance as useful data
When teams resist, ask why. Is the concern about job security? Accuracy? Risk? Lack of time? Lack of relevance? Poor communication? Each requires a different response.
5. Celebrate practical use cases, not just flashy demos
Highlight examples where AI improves real work. Reducing repeatable work, speeding up reporting, improving customer response, summarising technical information, or helping teams make better decisions may be more valuable than an impressive but irrelevant demo.
This is how AI training for employees becomes practical: by connecting learning to real work, real use cases and real business outcomes.
To understand whether the organisation is building AI capability across experience levels, leaders should ask:
✓ Who is currently experimenting with AI?
✓ Who is not experimenting, and why?
✓ Are experienced operators involved in identifying use cases?
✓ Are managers confident enough to redesign workflows?
✓ Are digitally confident employees helping others learn?
✓ Are we treating resistance as a change management signal?
✓ Are we pairing tool fluency with domain expertise?
✓ Are we making AI relevant to different roles?
These questions help leaders see whether AI adoption is broadening or staying trapped in small pockets of enthusiasm.
They also reveal whether AI skills training is reaching the people who understand the work, not just the people most likely to experiment first.
AI is not a young person’s game. It is not an old person’s game either. It is a learning game.
The organisations that succeed will be the ones that combine curiosity, experience, judgement, and practical capability. They will not assume that the fastest adopters know the most valuable use cases. They will not assume that experienced employees are blockers. They will bring both groups together.
Because AI creates the most value when people who understand the technology work with people who understand the business. That is how organisations move from experimentation to adoption, and from adoption to impact.
At Decoded, we help organisations build that shared capability across roles, levels, and teams.
Support AI adoption across teams by building shared capability, confidence and practical skills. Take a look at our AI courses.
If you’d like to learn more about Decoded and how we can help transform your
organisation, we’d love to hear from you.
