Developing the next generation of communicators

One of the quieter questions behind the excitement about AI in communications is this:

What happens to early-career professionals when
many of the fundamentals they need to learn are now automated?

For decades, communications followed an informal apprenticeship model. Early career practitioners learned by drafting content, writing briefs, monitoring media and collecting data. Much of that work was repetitive, but it was formative. It is where judgment, risk awareness and audience understanding were built.

AI is rapidly changing that model. Drafting, summarising and monitoring can now be done in seconds. While this creates efficiency, it also removes many of the learning experiences that helped people develop professional judgement. The risk is not job loss. It is a capability loss.

Building judgement, not just output

Good communication judgment does not come from theory alone. It comes from seeing what works and what fails—watching a media release turn into a news story. Seeing a social post get engagement…for the wrong reasons. These experiences shape instinct and decision-making over time.

When AI handles first drafts, those learning moments can disappear unless they are deliberately replaced. Early career communicators are not doomed, however what we teach them needs to change and be intentionally designed rather than assumed.

Our clients tell us that junior staff are already relying heavily on AI tools to produce content quickly, but with limited critical thinking. Speed without judgment is not capability. The issue is not the use of AI, but the absence of challenge, reflection, and accountability regarding its outputs.

Learning through analysis

One important shift in building skills is moving from doing the work to analysing it. We should use AI to produce a first draft, but the task for an early-career communicator should not be to edit and submit. It should be to explain it.

What is missing?
What assumptions are being made?
What risks does this create?
What would change for a different audience or channel?

Critiquing AI output can be as valuable as writing from scratch, but only if leaders require that analysis as part of the process. The learning comes from explaining decisions, not just making edits.

Making senior experience visible

This also places new responsibility on senior communicators. Much of our decision-making is instinctive. “This doesn’t feel right” or “we should wait”. In an AI-enabled environment, those instincts need to be made transparent.

Talking through trade-offs, risks and past experiences turns implicit knowledge into something teachable. Early-career communicators cannot develop judgment if they never see how it is applied or the thinking behind the decision.

Experience also needs to be designed. Exposure to executive meetings, risk discussions, crisis planning, and project meetings matters far more than the volume of content produced. Experience is no longer about output. It is about proximity to decision-making.

Redefining early career roles

This points to a broader shift in how early career roles should be structured. Rather than shielding junior staff from complexity, we should bring them into it earlier, but with support. That means involving them in audience analysis, message framing, ethical considerations and channel decisions, not just execution.

Coaching becomes critical. AI can speed up production, but it cannot replace feedback, reflection or professional development. Without strong mentoring, early career communicators risk becoming highly efficient operators with shallow judgment.

Rethinking performance and value

Finally, leaders need to reconsider how performance is measured. If speed and volume are rewarded, the wrong skills will be reinforced. Instead, managers should value curiosity, ethical awareness, the ability to explain decisions, and the confidence to ask “should we?” not just “can we?”

The real risk is not that early-career communicators will lack job opportunities. They will lack the experience required to do those jobs well. And this will impact us all. AI makes this challenge unavoidable, but it also creates an opportunity to build more intentional, higher-quality professionals than we have ever had before.