Category: Bringing the team along

For marketing leaders and agency owners whose teams are hesitant about AI, and who want to know what actually moves people.

The day the numbers turned

In 2014 I was at BBDO Gulf, in a department of 60 people handling communications for large clients across the Middle East. Influencer marketing was gaining ground, and our media team kept noticing that digital campaigns were bringing a better return. One day we sat down and compared media budgets with results, channel by channel.

Digital was delivering the highest return. Traditional media was still doing important work for reputation, but it wasn’t bringing in business the way digital placements were. Audiences had moved, and their habits with them.

After a long day of brainstorming, we agreed on what had to change. Client servicing would need to brief the creative team differently, and the creative team would need to start from digital-first ideas and adapt them to traditional media afterward, the reverse of how we worked.

Not everyone on the team understood digital well. Our creatives still thought of the TV commercial and the newspaper ad first, and digital was seen as an adaptation, a smaller version of the real idea. There was a lot of confusion about where to start, and I could see we had to shift what people believed before we could change the work itself.

What is AI change management?

AI change management is the work of helping people change how they work so a team actually adopts AI, rather than just having access to it. It covers the reasons people hesitate, the evidence that persuades them, the training and incentives that build new habits and the roles leaders play along the way.

The digital shift vs the AI shift

	The digital shift (from 2014)	The AI shift (today)
How skeptics saw it	A trend that wouldn’t stick	A threat to their jobs
Biggest fear	The work becoming smaller and cheaper	Being replaced, or being left behind
Brand worry	The big idea losing its stature	The brand losing its voice
Certainty	Many doubted digital would last	Very few doubt AI is coming
Who knew more at first	Junior people who lived on social media	Almost everyone is still discovering
Senior people	Behind the juniors on tools and trends	Possibly better placed, because it starts with process and business judgment

What moved the team

I didn’t start with a speech. I started with the math. Our media planning director and I gathered the data, facts and figures, so the case for change rested on results rather than on my opinion.

Then I teamed up with one of our youngest creatives, who was handling social media execution at the time. Together we went out into the streets around the office to collect vox pops, quick public opinion samples: the bakery downstairs that everyone knew, the parking attendant and his teenage son, the waiters at the café across the street. It gave the team something the numbers couldn’t, the voices of the people we were trying to reach. Influencers we had worked with before also let us dig into their results anonymously.

We built all of it into a large interactive deck, and around it a learning program with an incentive. Each module was one idea, and each module earned points that people could exchange for vacation days or other benefits. We also told the team we would start only with the brands that sell to young audiences, as a trial. Nobody’s entire portfolio was changing overnight.

The resistance came from the top

The junior team got excited quickly. The resistance came from the heads of department, mainly our creative director.

His view was that digital was a trend that wouldn’t stick, and that limiting the creative team to digital ads was cheap. We think bigger, he said in effect, and we are bigger. He had been with the company for decades and held one of its most senior positions. My own read, which he never said out loud, was that it was hard for him to see a junior creative know more than he did about the tools, the trends and the influencers.

So we had an excited young team and a leader above them who didn’t believe the ideas were worth it. He also had the final sign-off on creative work and client presentations, which meant his doubts could stop everything.

We tried incentives and many conversations. None of it worked, and looking back I think I understand why: every argument asked him to give something up. What finally helped was giving him something instead. With approval from head office, we sent him to a digital training program in New York. We told him that when he came back, he would lead the change with the team, and then join a regional team training other offices.

It worked, not without a few bumps, over roughly the next two to three years.

What sealed it was the client

The moment I remember most came at a client’s yearly strategy presentation. The client was a telecom company operating across the region, and our plan already included a lot of digital because we were well into the change.

At the end, the client stood up and pushed back. The content was great, they said, but they didn’t want to keep paying for expensive TV commercials seen mostly by older viewers, or for newspaper ads. The business made its money by upselling services, and its most important audience was getting younger. They wanted digital first.

Our creative director answered that we were a very advanced agency, fully up to date on every trend, and that we would keep the concept and push it further into digital. The same person who had called digital a trend was now defending it in front of the client. That was the point where the change stopped being an internal project and became simply how we worked.

What feels the same with AI

When I talk with marketing teams about AI today, I recognize the same fear and the same resistance, and three worries come up again and again.

The first is that AI is there to replace them. Many people haven’t yet seen what it can do for their own work, or that people who use it well become harder to replace.

The second is related. They don’t yet see that handing over repetitive day-to-day tasks gives them more time for the work that needs a person: thinking, research, creative brainstorming and building relationships.

The third is the brand. People worry it will lose its voice and stop sounding like itself. It’s the same concern our creative director had about the big idea, in a new form. Clear rules help here, and I wrote about them in a generative AI policy for marketing teams.

Underneath all three is the fear of being left behind. That fear isn’t imagined. In McKinsey’s survey of US employees, 35% named workforce displacement as a concern about gen AI (McKinsey, January 2025).

What feels different this time

The big difference is that the change feels inevitable. With digital, plenty of smart people doubted it would last. With AI, I don’t meet many people who think it’s going away. The question has shifted from whether to how.

It’s also new for almost everyone, so a team starts closer to the same point. And I think senior people may be better equipped this time, because building AI into a team’s work starts with process and business understanding. Deciding where AI belongs in a workflow, what a person must keep and what a client will accept takes the kind of judgment that comes with experience.

Familiarity with the tools is a different matter. McKinsey found that 62% of employees aged 35 to 44 report extensive familiarity with gen AI, compared with 22% of those over 65. It also found that employees are more ready for AI than their leaders assume, and that leadership, more than employee readiness, is the biggest barrier to scaling it (McKinsey, January 2025). So experience is an advantage only if senior people get their hands on the tools too.

How to bring a hesitant team along, step by step

1. Start with your own numbers: where time, money and rework go today. A simple map of how your team works is enough.
2. Add evidence from outside your own opinion: client feedback, audience behavior, results from peers.
3. Put the people who already use AI well at the center, whatever their title.
4. Run a small, bounded trial on one part of the business before changing everything.
5. Make learning worth something, with recognition or rewards people actually value.
6. Find your most senior skeptic a real role, such as leading the training for other teams.
7. Bring client or customer expectations into the room early.

Common mistakes to avoid

• Arguing with senior skeptics. If every conversation asks them to give something up, expect them to hold on harder.
• Ignoring the person with final sign-off. Whoever approves the work can quietly undo the change.
• Treating AI as an add-on. Digital didn’t work as a smaller version of the TV idea, and AI won’t work bolted onto the old process.
• Leaving the job question unspoken. People fill the silence with their own fears.

Where I can help

I help marketing teams and agencies with the people side of AI adoption: training by role, incentives that fit the team and a plan for the people who are less sure about it. You can see the six areas I work on in What I Bring, and read more about training in what AI enablement means for a marketing team.

Final thought

Think of the most senior skeptic on your team, and ask what role in the change would give them something rather than take something away. That conversation may matter more than your next training session.

Common questions about bringing a team along to AI

Sources

1. McKinsey & Company, “Superagency in the workplace: Empowering people to unlock AI’s full potential,” January 28, 2025 (checked October 20, 2026). Read on mckinsey.com

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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

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