What AI Enablement Means for a Marketing Team

For marketing leaders who have rolled out AI tools and are still waiting for people to use them.

Earlier in my career, at BBDO Levant, I ran team trainings in press relations, media training, digital activation, CRM and crisis management, and I trained as a media “train the trainer.” The subjects were different, but what helps a new skill stick is much the same: people learn by doing it on their own work, with someone close by when they get stuck.

What is AI enablement?

AI enablement is the training, coaching, rules and support that help people use AI tools well in their daily work. It sits between buying a tool and getting value from it, and it’s mostly about people and process. In a marketing team, it means each role knows which of its tasks AI can help with, has practiced on real work, knows the rules and has someone to ask.

Rolling out tools vs AI enablement

	Rolling out tools only	AI enablement
Training	One general session for everyone	Training by role, on real tasks
Practice	Left to each person	Built into this week’s work
Support	A help page or a ticket queue	Champions on each team
Rules	Unclear, or a long document nobody reads	A short list everyone knows
The job question	Avoided	Discussed openly by leaders
Measuring progress	License counts	Who uses AI, for what and what changed

Why buying the tools isn’t enough

The numbers on training are sobering. In BCG’s survey of more than 10,600 workers in 11 countries, 72% said they use AI regularly, but only 36% felt adequately trained, and only 25% of frontline employees said their leaders gave them enough guidance. Regular use among frontline employees sat at 51%, a figure BCG described as stagnant (BCG, June 2025).

Using AI is common. Feeling trained to use it is not.

Employees who use AI regularly

72%

Frontline employees who use AI regularly

51%

Employees who feel adequately trained

36%

Frontline employees who say leaders give enough AI guidance

25%

The same research found that employees who received five or more hours of training, especially in person and with coaching, were significantly more likely to become regular users.

McKinsey heard something similar from employees directly. In the US, 48% said formal generative AI training from their organization would do the most to increase their daily use. Leaders also underestimated what was already happening: they guessed that 4% of employees used generative AI for at least 30% of their daily work, while employees reported 13% (McKinsey, January 2025).

People want help, and many are already experimenting on their own. Enablement gives that energy a direction.

What AI enablement looks like in a marketing team

• Training by role. A copywriter, a designer, a media planner and an account lead use AI for different things. One general session for everyone tends to leave each of them with half of what they need.
• Practice on live work. People learn faster on this week’s brief than on a made-up exercise, and the time they save shows up right away.
• Champions. One or two people per team who are a step ahead and happy to help. They answer the small questions that would otherwise stop someone cold.
• Simple rules. What people may use AI for, what they must never put into it and who checks the output before it goes out. I covered this in a generative AI policy for marketing teams.
• A way to see progress. Who is using it, for which tasks and what has changed in time and quality. This doesn’t need to be elaborate, but it needs to exist.

How to start an enablement program, step by step

1. Map how the team works so training can focus on the steps where AI helps most.
2. Pick one or two roles to start with, not the whole department.
3. Run hands-on sessions on real tasks from those roles.
4. Name a champion in each team and give them time for it.
5. Share the short list of rules before people start.
6. Check in after two weeks: what’s working, what isn’t, what people still avoid.
7. Extend to the next roles, using what the first group learned.

The part that decides whether it sticks

My background in clinical psychology shapes how I look at this. Resistance usually carries information. Someone who is worried about what AI means for their job won’t take much in from a training session until that worry has been addressed honestly.

McKinsey’s research found that 41% of employees are more apprehensive about AI and will need additional support. Its 2026 survey shows why: 39% of respondents expect AI to reduce their organization’s headcount over the next year (McKinsey, August 2026).

My own view is that AI should add to what capable marketers can do, so the same team spends more of its week on the work that needs them. Teams can tell quickly whether their leaders mean that. When they believe it, adoption gets much easier.

Common mistakes to avoid

• One training for everyone. People leave with general ideas and no clear use for their own tasks.
• Training once and moving on. Tools change and questions come up after the session, not during it.
• Choosing champions by title. The best champions are the people others already ask for help.
• Measuring licenses instead of use. A seat that’s paid for isn’t a skill that’s been learned.
• Staying silent about jobs. If leaders don’t talk about it, people fill the silence with their own fears.

Where I can help

I design and run upskilling and change programs for marketing teams and agencies: training by role, on your own work, with champions and simple rules in place. If you’d like to know more about how I see people and AI, I wrote about it in what I believe.

Final thought

Pick one role on your team and sit with them for an hour on a real task. You’ll learn more about what training they need than any survey will tell you. Then build the first session around what you saw, using their real work as the example.

Frequently asked questions

Sources

1. Boston Consulting Group, “AI at Work 2025: Momentum Builds, But Gaps Remain,” survey findings press release, June 26, 2025. Read on bcg.com
2. McKinsey & Company, “Superagency in the workplace,” January 28, 2025. Read on mckinsey.com
3. McKinsey & Company, “The state of AI in 2026: On the road to ROI,” August 25, 2026. Read on mckinsey.com

Related articles