AI Can Do the Work. But Can Your Team Question It? Why Critical Thinking Matters More Than Ever in Marketing

Two separate conversations last week, one with a GM of Marketing and another with a CMO, landed on exactly the same two words.

Critical thinking.

Neither conversation was about whether their teams were using AI. They were. The question was what happened next.

Both conversations touched on coordinators and junior marketers using AI to create reports or produce work, then sharing the output without really stopping to question whether it was right.

And that got me thinking about what this means for the next generation of marketing talent, the people leading them, and the skills that will matter when it comes to building a career in marketing.

Because being able to use AI is one thing. Knowing when to question it is another.

AI is changing the work, but judgement still matters

I'm having more conversations with marketing leaders about AI and what it means for their teams. The discussion is moving beyond the tools themselves and towards how people are actually using them.

AI can help marketers get to a first draft faster, summarise information, build reports and explore ideas they might not have considered. Used well, it can be incredibly useful.

But producing something quickly doesn't automatically make it good. And just because a report looks polished, or an answer sounds convincing, doesn't mean the information is accurate, or the thinking behind it is sound.

This is where I think we need to pay closer attention.

When someone generates a report using AI and sends it straight on to their manager, there are a few questions I'd want them to ask first.

Does the information make sense? Are the figures accurate? Does the output reflect what we know about our customers? Is it consistent with our brand and the brief? And, perhaps most importantly, what might be missing?

These aren't new skills. Good marketers have always needed to think critically about the information in front of them.

What's changing is the environment in which they're expected to apply those skills. When AI can produce a credible-looking piece of work in minutes, it's tempting to accept the output and move on.

The ability to pause, assess and challenge what you're seeing is becoming an increasingly valuable part of the job.

The risk isn't using AI. It's switching off your own thinking.

One of the things that concerns me is how easily the process of creating work can become separated from the process of understanding it.

Imagine a junior marketer is asked to prepare a campaign performance report. They use AI to help analyse the results, organise the findings and draft a summary. The report looks professional, the recommendations sound reasonable, and the work is ready to go.

But have they checked the numbers against the original data? Do they understand why one campaign performed better than another? Can they explain what the results mean for the business? Have they considered whether the recommendation actually addresses the original brief?

If they can't answer those questions, the report may look complete without being particularly useful.

The same applies to customer research, competitor analysis, content development and campaign planning. AI can help with the process, but it doesn't remove the need for someone to understand the context and make a judgement about the result.

And in marketing, context matters enormously.

A recommendation that works for one audience might fall flat with another. A message that fits one brand might feel completely wrong for the next. A set of results might look impressive until you consider the commercial objective behind them.

Those are the nuances that come from experience, curiosity and an understanding of the business.

I don't expect a junior marketer to know everything. In fact, I think being early in your career is precisely when you should be asking questions, testing your assumptions and learning from the people around you.

What I do want to see is the willingness to think beyond the first answer.

Why this matters for junior marketers building their careers

When I think about what makes someone stand out as a candidate, I'm not just looking at their technical skills or the platforms they've used.

I'm interested in how they approach a problem. How they think. Whether they're curious. Whether they can explain their decisions and recognise when something doesn't quite add up.

Those qualities have always mattered in marketing, but I believe they'll become even more valuable as AI becomes a more established part of the way teams work.

For people early in their careers, this presents both a challenge and an opportunity.

There will be tasks that AI can help you complete faster. But speed alone won't necessarily distinguish you from another candidate who has access to the same tools.

Your judgement might.

Can you look at an AI-generated report and identify an assumption that needs checking? Can you spot when a recommendation doesn't align with the customer insight you've gathered? Can you explain why you've changed an AI-generated draft rather than simply accepting it?

Can you tell a hiring manager what you learned from the output, what you challenged and how you arrived at your final recommendation?

These are the sorts of examples I'd encourage candidates to bring into an interview. They demonstrate more than familiarity with a tool. They show that you understand the work, take responsibility for the quality of your output and can apply your own thinking.

You don't need to pretend you know everything. I'd much rather hear someone explain what they questioned, what they couldn't verify and how they worked through it than hear them confidently describe a piece of work they don't fully understand.

Being confident using AI is useful. Being confident enough to challenge it is what makes that skill more meaningful.

The opportunity for marketing leaders: teach the thinking, not just the tool

For GMs of Marketing, CMOs and team leaders, I think there's a real opportunity here.

If we're asking junior marketers to use AI, we also need to help them develop the judgement to assess what it produces.

We can't assume those skills will automatically develop just because someone is using the tools regularly. And we shouldn't expect someone at the beginning of their career to have the same level of discernment as someone who has spent 15 or 20 years working across brands, campaigns and customers.

Experience gives people a reference point. They've seen campaigns succeed and fail, worked through competing priorities, made mistakes and learned to recognise when something doesn't feel right.

Junior team members are still building that reference point. They need opportunities to see how experienced marketers think.

That doesn't necessarily require a new training program or a lengthy policy document. Often, it can start with the work already happening in the team.

Here are a few practical ways I'd approach it.

1. Work through the output together

When a junior team member brings you an AI-generated report, piece of content or recommendation, resist the urge to simply approve it or send it back with edits.

Sit with them and talk through it.

Ask what they think is working, what they're unsure about and whether anything surprised them. Show them where you'd look for supporting evidence, which assumptions you'd challenge and what you'd want to investigate further.

Explain your reasoning, not just your final decision.

The aim isn't to make them dependent on your approval. It's to help them understand the thinking behind a good piece of work so they can apply it independently next time.

2. Ask questions that encourage judgement

It's easy to focus on whether a task has been completed. But if we want people to become better thinkers, we need to make room for questions that go beyond the finished product.

For example:

• What makes you confident this information is accurate?
• Does this recommendation address the original brief?
• What would you want to verify against the original data or a reliable source before sharing this?
• What alternative explanation could there be for these results?
• What's missing from this analysis?
• If you had to defend this recommendation, what evidence would you use?

These questions encourage people to engage with the work rather than simply deliver it.

Over time, they can also help build the confidence to challenge their own assumptions before someone else has to.

3. Make it safe to say, "I'm not sure"

Critical thinking requires a degree of confidence, and confidence doesn't grow particularly well in an environment where people feel they need to have every answer.

I'd rather a junior marketer tell me they aren't sure whether an AI-generated insight is correct than confidently present something that hasn't been checked.

Leaders can encourage this by treating uncertainty as a starting point for investigation, not a sign of incompetence.

That doesn't mean lowering expectations around accuracy or accountability. It means creating an environment where people feel comfortable raising questions, acknowledging gaps in their knowledge and asking for help when they need it.

Those behaviours are part of good judgement, too.

4. Talk about the changes, not just the mistakes

When you edit an AI-generated piece of work, explain why.

Perhaps the language doesn't sound like the brand. Perhaps the recommendation is too broad to be actionable. Perhaps the data supports a different conclusion, or the output overlooks an important customer consideration.

Making those decisions visible gives junior marketers a chance to learn from your experience.

It also helps them understand that good marketing isn't simply about producing something that looks polished. It's about making sure the work is relevant, accurate and useful.

5. Give people ownership of the final result

AI can be part of the process, but the person delivering the work still needs to understand and take responsibility for it.

Encourage team members to explain their recommendations in their own words. Ask them to identify what they've checked, what they've changed and where they still have questions.

The goal is to build marketers who can use AI thoughtfully, rather than people who can generate an impressive-looking output but struggle to explain what it means.

That distinction matters for the quality of the work, the development of the individual and the confidence you can place in your team.

What this means when you're hiring marketing talent

At New Chapter Talent, I spend a lot of time talking with marketing leaders about the people they need in their teams, and with candidates thinking about their next career move.

Those conversations have reinforced something I believe strongly: the best hiring decisions go beyond matching a list of technical skills to a job description.

It's about understanding how someone thinks, how they approach their work, how they collaborate and what they'll bring to the team around them.

As AI becomes more embedded in marketing, I think these questions deserve even more attention.

For hiring managers: look beyond AI proficiency

It's reasonable to ask candidates how they use AI. I'd also encourage you to explore how they evaluate what it produces.

Ask them to talk through a project where they've used AI to support their work. What did they use it for? What did they check? Did they change the output, and why? What did they learn from the process?

You could even discuss a hypothetical scenario relevant to the role and ask how they'd assess an AI-generated recommendation before acting on it.

You're not looking for someone who challenges everything for the sake of it. You're looking for someone who can distinguish between a useful insight and an unsupported assumption, who understands the brief and who knows when further investigation is needed.

And remember that expectations should reflect the level of the role. A junior candidate won't have the same depth of experience as a senior marketing leader. What you can look for at every level is curiosity, a willingness to learn and an ability to explain their thinking.

For candidates: show how you think, not just what you've produced

If you're preparing for interviews, think about the work you've done with AI and how you can talk about it in a meaningful way.

Don't stop at saying you've used it to write content, analyse data or streamline reporting. Be ready to explain how you approached the task and what you did to make sure the result was fit for purpose.

If you've spotted an error, challenged a recommendation or changed an output because it didn't reflect the customer or the brand, talk about that.

If you've had to verify information or recognise that AI wasn't the right tool for a particular part of the task, that's worth discussing, too.

You don't need an elaborate story. A straightforward example that shows your thinking can tell an interviewer a great deal about how you work.

And if you haven't had much opportunity to use AI in a professional setting yet, don't feel you need to overstate your experience. Focus on your approach to problem-solving, the questions you ask and how you evaluate information. Those are valuable foundations to build on.

The human skills that will continue to set good marketers apart

I don't see AI use and critical thinking as competing skills. The opportunity is to develop both.

The marketers who can get value from AI while applying their own judgement, commercial understanding and customer knowledge are, in my view, well placed to contribute to their teams.

For leaders, that means making space for people to learn how to think, not just how to use another tool.

For candidates, it means recognising that your value isn't limited to the tasks you can complete or the platforms you know. It's also in the questions you ask, the decisions you make and the reasoning you bring to your work.

This is one of the reasons I value being part of the broader marketing community through initiatives such as the CMO Chapters Podcast, CMO Collective Lunch Club, Marketing Leadership Awards and Marketing Mentorship Program. Creating opportunities for marketers to share experiences, learn from one another and develop their careers matters, particularly as the expectations of the profession continue to evolve.

We don't need junior marketers to have all the answers. We need to give them the experience and support to develop their own judgement, and the confidence to speak up when something doesn't look right.

Because AI can help you produce the work. But knowing whether that work is worth sharing? That's where good marketing judgement comes in.

And it's a skill worth developing at every stage of your career.

Let's talk about the future of marketing talent

Whether you're leading a marketing team and thinking about the skills you'll need next, or you're a marketer considering your next move, I'd love to hear how you're approaching AI and critical thinking in your own work.

At New Chapter Talent, I've built the business around a personalised approach to marketing and sales recruitment. With more than 20 years of experience in marketing recruitment, I know that finding the right person is about more than a CV or a list of capabilities. It's about understanding the people, culture and values that make a team work.

If you're looking to build a team that can adapt, think critically and do great work, or you're ready to explore your next opportunity in marketing, get in touch with me at New Chapter Talent.

Sometimes, the best next step starts with a conversation.