In boardrooms and classrooms alike, artificial intelligence has moved from a speculative buzzword to a working reality. From generative AI reshaping content creation to machine learning models guiding business decisions, today’s workforce is rapidly learning how to work alongside — and in some cases, compete with — intelligent systems.

But this shift is raising deeper questions, especially for early-career talent and the programmes that shape them: What is the purpose of an internship in the age of AI? Are we onboarding students into a world of automation simply as users of tools — or are we preparing them to ask better questions, make ethical decisions and collaborate across disciplines and cultures?

In other words, are we training the machines or the mindsets?

The shifting ground beneath traditional internships

Internships have long served as a critical bridge between academia and industry. Traditionally, they provided a structured way for students to gain ‘real-world experience’ — shadowing professionals, contributing to small projects, and picking up the soft skills of office culture.

But today’s internships look radically different. In a post-pandemic, hybrid-first environment:

• Interns may never step into a physical office. Their entire onboarding, mentorship and output may happen online.
• The tools they’re expected to master have changed. From Slack and Notion to AI copilots like ChatGPT, interns are now judged as much on their digital fluency as their domain knowledge.
• The pace and nature of work have accelerated. Interns are expected to deliver impact faster, often with fewer human touchpoints.

These changes raise important questions for both organisations and academia: Are internships still serving their purpose as a training ground for long-term capability? Or are they becoming an extended test of how well young people can adapt to tech without context?

The AI layer: Opportunity or overshadowing?

Artificial Intelligence has added both capability and complexity to internships.

On the one hand, AI has levelled the playing field. An intern can now use AI tools to:

• Draft code or emails
• Analyse large datasets in seconds
• Brainstorm marketing campaigns
• Translate or localise content instantly

This allows interns to work faster, experiment more and contribute real value in shorter timeframes.

But on the other hand, AI can mask gaps in understanding. If an intern uses ChatGPT to write a business strategy without knowing how to evaluate its feasibility, they may never learn the fundamentals of critical thinking, analysis or storytelling.

As AI becomes a co-pilot, the risk is clear: Are we letting tools do the thinking for us — before we’ve learned how to think?

From talent pipelines to talent ecosystems

We often think of internships as the top of the talent funnel. But in the age of AI, we must start treating internships as the foundation of a talent ecosystem — one that values creativity, resilience, critical reasoning and collaboration just as much as technical skill.

Yes, we need interns who can write a great prompt or visualise data in minutes. But we also need interns who can identify which problems are worth solving, whose voices are missing from the room, and how their work fits into a larger social and organisational purpose.

These are not luxuries. They are leadership capabilities. And internships must be their earliest training ground.

What internships need to teach now

To stay relevant and valuable, internships in the age of AI need to go beyond tool-based training and cultivate deeper capabilities:

Critical Thinking Over Clicks: Young professionals must learn how to question what the machine outputs. Is this data valid? Is the recommendation biased? Is the response ethical? Critical thinking isn’t about rejecting AI — it’s about engaging with it intelligently.

Creativity and Original Thought: AI can remix ideas, but only humans can originate them. Internships should encourage interns to explore original thinking, take creative risks and contribute perspectives shaped by their context, background and interests.

Collaboration and Human Skills: In a hybrid workplace, communication is both more frequent and more fragmented. Interns need to learn how to communicate with clarity, seek feedback, navigate ambiguity and collaborate across digital divides.

Curiosity and Adaptability: The tools of today will not be the tools of tomorrow. Instead of training interns on a fixed tech stack, programmes should nurture curiosity, experimentation and the confidence to learn continuously.

Ethical Decision-Making: As AI is used in recruitment, performance management and content creation, ethical questions are everywhere. Interns should be exposed to discussions on fairness, bias and accountability — shaping not just what they do, but how they think.

Rethinking internship design for the AI era

To foster these deeper skills, organisations and institutions must redesign internship experiences with intention. Some strategies include:

Project-based learning: Assign real problems, not repetitive tasks. Let interns define the problem, gather data, propose solutions and iterate — even if imperfectly. This mirrors how actual work happens and teaches resilience.

Mentorship over management: Assign mentors who are invested in guiding interns, not just evaluating them. A good mentor doesn’t just answer questions — they teach interns how to ask better ones.

Feedback loops that teach reflection: Encourage interns to reflect on their process, not just their output. What did they learn? Where did they use AI and how did it help or limit their thinking?

Hybrid-ready, human-centred design: Even in remote internships, create space for human connection — team bonding calls, informal chats, storytelling sessions with senior leaders. Culture must be experienced, not just documented.

Are we measuring the right things?

To make this shift, we also need to change what success looks like in internships.

• Don’t just measure how quickly interns deliver.
• Track how they think through problems, collaborate with teams and learn from feedback.
• Celebrate curiosity, not just competence.
• Reward those who ask better questions, not just those who get faster answers.

After all, AI can do speed. It’s the human mind that brings depth.

The stakes are higher than we think

Why does this matter? Because the way we train interns today will define the workforce of tomorrow. If we treat internships as plug-and-play tech sprints, we risk raising a generation of professionals who are fast but shallow — great at prompt engineering, poor at people leadership.

But if we reimagine internships as spaces to build judgement, creativity and humanity, we create a talent pipeline that can do more than use AI. It can shape the future alongside it.

Internships are a mirror of our leadership philosophy

How we structure internships reflects how we define success.

If we treat interns as task-runners and output machines, we reinforce the idea that speed and tool mastery are the only currencies that matter. But if we design internships as spaces for questioning, connection, and cultivation of potential, we send a much deeper message — one that will shape not just careers, but the kind of future we build as a collective.

In the age of AI, it’s easy to train the tools. Let’s not forget to train the thinkers, feelers, and builders behind them.