The AI education landscape is moving faster than ever, and the gap between what's taught in the classroom and what's needed in industry continues to widen. At the recent London AI Summit, I had the privilege of facilitating a panel discussion that brought together three voices in this area.
As we finished the discussion, I asked each panelist for their top three takeaways for the audience and Class Futures. What emerged were nine practical tips which I have written up below.
About the Panel
This discussion brought together three diverse perspectives:
academic leadership from Dr Hemachandran Kannan, Associate Dean and Director of AI Research Centre at Woxsen University,
staff learning and development from Steven Toy, CEO, Memrise, and
university and industry application from Richard Ogundele, AI Cloud Engineer at Manchester Metropolitan University.
The 9 Key Takeaways For AI Skills
Steven Toy, CEO, Memrise
1. Speed and AI Frameworks
The AI landscape moves at a fast pace. Organisations and individuals must stay agile, closely following developments while making sure frameworks are in place to guide teams and decision making.
2. Resilience
Success in AI requires embracing failure as part of the learning process. The ability to try, face setbacks, learn from them, and try again is vital for long term success in the evolving area of AI skills.
3. Stop Being a Spectator
The time for passive observation is over. Professionals and students need to engage with AI tools, experiment with them, and gain hand on experience rather than watching from the sidelines.
Dr Hemachandran Kannan, Associate Dean & Director of AI Research Centre, Woxsen University
4. Create Space for Real Time Learning
Given the rapid pace of AI developments, educational organisations and professionals must carve out dedicated time for experimenting with new tools and technologies as they emerge, rather than waiting for formal training programmes.
5. Prioritise Training with Proper Support
While training is crucial, it requires significant effort and support to be effective. Organisations must make AI education a priority with adequate resources and commitment.
6. Balanced Time Allocation Across Areas of AI
AI education shouldn't focus solely on technical skills. Time must be thoughtfully allocated across all aspects including ethics, application, safety, and technical implementation to create well rounded and literate AI users.
Richard Ogundele, AI Cloud Engineer, Manchester Metropolitan University
7. Provide Safe and Responsible Learning Guidance
Students need clear direction on how to develop AI skills in a way that's both safe and responsible. This guidance is essential for preventing misuse and ensuring ethical development practices. Often this can be supported by wider reading.
8. Emphasise Values and Ethics
AI education must place strong emphasis on values and ethical considerations. These shouldn't be an afterthought but a core part of any AI curriculum or professional development programme.
9. Focus on Purpose and Outcomes
Every AI implementation should start with fundamental questions: why is AI being used? What specific outcomes are we trying to achieve? This purpose driven approach helps ensure AI is applied meaningfully rather than for its own sake.
Summary
The panel highlighted that closing the gap between classroom and industry AI skills requires a multidimensional approach combining:
speed and agility
resilience in learning
hands on engagement
dedicated time to learn
training support
balanced curriculum coverage
ethical guidance
purpose driven implementation.
That’s a lot and success therefore depends on moving from passive observation to active participation while maintaining strong ethical foundations and clear objectives.
I'm available for panel hosting, moderation, fireside chats, and the facilitation of presentations and workshops. All services can be tailored to your specific marketing event needs and audience requirements.
For details, reach out through Class Futures or connect with me directly on LinkedIn.