Why AI Belongs in Every Graduate Programme

In the last 12 months, something has changed in how graduates arrive in the workplace. 

They’re no longer just bringing technical skills and academic knowledge—they’re bringing day-to-day experience using AI and GenAI tools to get things done. From summarizing long readings with ChatGPT to debugging code using Copilot, today’s graduates are already applying AI as a productivity tool before they even walk through your office door. 

But there’s a disconnect. 

Many companies still don’t include AI or GenAI skills in their graduate onboarding programs, treating AI as something separate—experimental, advanced, or limited to data science teams. This creates a gap: graduates know how to use these tools, but aren’t shown how to apply them safely and effectively within a business context. Worse, they may be discouraged from using AI at all, or left to figure out the risks and responsibilities on their own. 

At Neueda, we believe that needs to change. That’s why we now include AI and GenAI awareness and hands-on training in every graduate program we deliver—across all roles, technical and non-technical. 

At Neueda, we don’t treat AI as a standalone topic. We integrate it into everything we do—from lectures and labs to deliverables and assessments. Every task becomes an opportunity to reinforce responsible, effective use of AI tools in context.

Here’s how we do it: 

• End every day with a “Prompt of the Day” – One participant shares a GenAI prompt they used that day to improve quality, productivity, or insight. It builds confidence and peer learning. 

• Reinforce leadership messaging – If your organisation encourages AI adoption (e.g., “Use GenAI five times a day”), we make sure that’s reflected in the training culture too. 

• Weekly GenAI Challenges – We run mini-challenges that require participants to solve realistic tasks using GenAI—like generating seed data, writing documentation, or proposing a system design. We share and celebrate the most creative or efficient approaches in weekly recaps. 

• Annotated AI usage in submissions – Participants are encouraged (and often required) to annotate how they used GenAI tools in their project work. This builds reflection, promotes transparency, and helps them develop thoughtful rather than lazy habits. 
• AI across labs and projects – Whether they’re debugging a process, designing a workflow, writing a user story, or reviewing documentation, participants are expected to explore how GenAI can support the task—and to think critically about its strengths and limitations. 
  
  This integrated approach ensures graduates build not just technical competence, but AI fluency—learning to work with AI rather than around it.