All business leaders are caught in a race to maximise the value and impact of AI within their organisations. Like any race, having the right equipment (in this case, skills) will make all the difference to your success — plus, it can drastically improve the race experience itself.

But not all skills are created equal in the AI era – and neither are the people who are learning them. According to McKinsey, these are separated into three different groups: the ‘Takers’ – the people who use AI; the ‘Shapers’ – those who integrate AI; and the ‘ Makers’ – those who build AI. Learning technology is not a monolithic, one-size-fits-all, paradigm. Your need for upskilling depends on how you’ll use it. Exactly like an agile project manager and a front-end developer, they might have some overlapping skills but the core of what they need to know, varies widely. Thus, each population needs a separate learning plan as their upskilling needs are very different.

It can be tempting to focus solely on the skills you need to implement and build AI models, such as Python or machine learning, but a mix of human (power) skills and technical skills will help your people run the race for longer, with more efficiency. AI-enabling skills such as data literacy, change management, and leadership skills will help your people work alongside AI and adapt to changes in the workplace.

 

Lifelong learners thrive in the AI age

Make no mistake, the ability to adapt to change will be a significant benefit to individuals and their employers over the next few years. Technology is advancing at an exponential rate with technology adoption declining from years (to reach 100 million users for Netflix, Spotify and Facebook) to months, for TikTok, and mere days for Meta’s Threads feature to hit the same 100 million milestone.

Technology is now outpacing humanity’s ability to upskill in time, meaning skills gaps will only get bigger. Instead of giving up, however, being more targeted in upskilling and reskilling will allow you to build the workforce skills you need right now to implement AI, and in the near future (1-5 years) based on your business goals and strategy. In other words, don’t try to upskill in a broad way to ‘embrace AI’ but look to your specific needs, skills gaps, and then target your upskilling to match this.

 

Three types of learning

As for the types of learning opportunities to offer, three options exist in most organisations:

1. Core training: Compliance-based learning such as cybersecurity and data protection training.
2. Every day learning: The learning that people engage with regularly in the moment of need or when they wish to continuously learn and grow their skills. This includes informal resources such as books, podcasts, and videos as well as peer learning and online courses.
3. Targeted skill-building: Programs that build the critical skills your business will need to fulfil its strategies in the near future. Talent academies centred around specific skill areas like cloud computing, machine learning, and sales/marketing fall into this category.

Depending on the types of skills you identify as required, and the skill level, you may employ a mix of all three of these learning options. As you build your upskilling strategy, be aware that not all parts of the workforce will need the same skill to the same level. Those overseeing, implementing, and making purchasing decisions for AI will need a higher skill level than those who have AI augmenting their work. Governance positions will need high levels of AI ethics and trust-building skills, for example.

 

Building an AI learning culture

Weaving learning into work is a key cultural shift that needs to happen for your workforce to feel empowered and motivated to continuously learn. Again, a variety of learning resources can help people engage with regular upskilling. So too can executive sponsorship (role modelling from senior management), creating psychological safety so people can experiment and learn from failure, and sharing learning across the organisation in peer groups. Dedicated AI learning days and functional deep dives can also help with improving AI literacy across a cohort. Having learning recognised in an OKR (Objectives and Key Result) can also help to show that building your skills is a key role responsibility and not a nice-to-do. This could be personal, such as building your Python knowledge to a specific level, or broad, like asking every worker to do 5 ChatGPT prompts a day.

 

Be loud about AI governance

A final note on AI upskilling that is important, since we’re dealing with a powerful, career-changing technology. You need to have a clear AI governance policy and you need to communicate this widely across your organisation. This will help alleviate any concerns about AI eliminating entire roles (and your upskilling opportunities should also help affected workers move into new careers if needed). It will also keep your AI use on the right track, to benefit all and not increase systemic biases or inequality. As you put your upskilling initiatives in place, work closely with your legal and operational counterparts to understand what AI ethics policies govern the technology’s use in your organisation. You may even find skills gaps here that require targeted skilling to ensure your AI use is responsible and sustainable long-term.

AI is going to be increasingly prevalent in our society and workplaces. Getting your AI upskilling in order now means that your organisation and workforce will be better equipped to deal with the next ChatGPT when it comes along. In this race, it doesn’t pay to sit out. Take steps every day to improve your AI skills and you’ll be ahead of the pack as AI continues to advance.

 

By Annee Bayeux 

Chief Learning Strategist at Degreed