Walk into a White Castle and you may just encounter Flippy, the fast-food frying robot. It does the work of an entire fry station, enabling its human colleagues to focus on giving the best customer service possible. Of course, like all forms of artificial intelligence (AI) and automation, working alongside Flippy requires a host of new skills. Workers need to know how to operate Flippy, how to work safely alongside Flippy, how to maintain it and any troubleshooting that may occur during a shift. That creates a demand for new knowledge and skills.

AI Is Changing Skills

It’s a familiar situation that’s unfolding across all organizations now that AI is (seemingly) everywhere. Indeed, economists predict that 300 million full-time jobs could be impacted by automation. Employees are well aware of the shifting sands, with 97% of them believing their employers should be prioritizing AI skills in their employee development strategy. Over half (57%) believe ethical AI and automation skills are going to be in higher demand.

These two skills are part of a growing list of AI-enabling skills workers need to be proactively upskilling in, ready to work alongside AI. Importantly, these skills are no longer limited to technology or data teams. All workers, in all roles and industries will need a baseline level of AI skills to work effectively with the technology.

Skills in understanding how AI works (machine learning versus generative AI versus deep learning), how to critically assess its outputs, and the pitfalls and limitations of AI are all vital in today’s workplace. Consider how ChatGPT took the world by storm last year. In the space of a couple of months, we suddenly had the ability to generate content in seconds with just a few prompts. It opened up a host of opportunities for speedy research, drafting emails, creating content and more. Yet, it can sometimes provide inaccurate or outdated information. Understanding how to separate the valuable from the noise with ChatGPT and other generative AI tools is now an essential skill.

AIEnabling Skills

There are other skills that aren’t strictly related to AI but will come in useful, such as teamwork, emotional intelligence and collaboration (for working well with others and robot/AI counterparts) and change management (to help your organization adapt). Learning agility, the ability to quickly learn and use a new skill, is also vital given the pace and scale of change that we’re experiencing. The reality is that as soon as you’ve come to grips with ChatGPT, another AI innovation will come along.

Did you know that 60% of today’s workers are employed in occupations that didn’t exist in 1940? That predates social media, big data and the internet. Imagine then, the tasks and roles that workers will be expected to do by 2100. We can’t begin to predict the roles that AI will create, so we need a degree of flexibility in our mindset and skills to keep on top of the unpredictable.

Building These Skills at Scale and Speed

Thankfully, we have access to a wealth of learning resources from traditional sources (e.g., books, articles, lectures) to tech-based alternatives such as podcasts,  virtual reality (VR), augmented reality (AR) and eLearning courses. Everything we need to learn AI skills with speed, agility and detail.

Let’s double back on that last point, because it’s important to understand that building the right skill is half the job. The other half is developing it to the right level, to a workable proficiency. Returning to Flippy, a kitchen worker might need basic to intermediate maintenance skills to complete a deep clean and fix minor issues as they arise during a shift. A maintenance worker called in to service and troubleshoot larger issues will need a more advanced skill level.

So how can you identify the AI skills and levels needed in your organization?

Align Skills With Strategy

Apart from the core competencies discussed earlier which all workers will need regardless of the AI strategy in their organizations, the first step in building AI skills is to consider how you plan to use AI. The technology stack that you use to make your AI plans a reality will determine the skills you need. There may also be industry-specific AI skills; for example, in using AI for medical imaging, or implementing automation to improve a manufacturing process.

That assessment isn’t a one-time activity, either. Because technology is ever-changing, whenever you discover a new AI application that you wish to explore, you need to return to the process of mapping out the skills needed to make it a success, and the minimum skill level to make it workable.

Find Your Hidden Sources of Skills

Adding another layer to this is the current skills and capabilities you have within your workforce. Those who already possess the skills you need to implement AI may not work directly with AI or even within your data science or IT team. Some employees may have taken on stretch assignments or side projects that have built their AI skills. Others may have developed them in a past role or project. Encouraging everyone to keep a skills profile that’s populated with all of their skills from past work, volunteering, side projects, gig work and learning, will help you uncover hidden talent.

This was the case with one telecommunications company that discovered it needed machine learning skills. Instead of limiting itself by searching only for people who had AI and machine learning jobs or degrees, it analyzed the profiles of thousands of workers who self-identified as machine learning experts. This allowed the company to understand the aggregation of skills, experience and pathways these workers took to develop these skills. These insights have enabled the company to triple its available talent pool for machine learning projects and inform upskilling to improve its future pipeline.

Take It a Step at a Time

Embracing AI, along with its associated skills, will be a race. But it’s a marathon, not a sprint. Take the first step today by understanding what AI your organization plans on using in the immediate future and the skills that enable this. Technology changes constantly, so ensure that the processes and platforms you implement today can accommodate the flexibility of the future.