Artificial intelligence (AI) is reshaping the future of work, and your employees know it. New research from 5Mins.ai shows employees believe that AI and machine learning are going to be the most significant drivers of change in the workplace. It’s high time L&D leaders recognize the critical role they play in preparing their workforce for this shift.

The risks of failure to act are huge, from efficiency losses to recruitment and retention problems: According to the LinkedIn 2022 Workforce Confidence Index, 25% of Gen Z employees planned to leave their current job in the next six months, and of those respondents, 76% cited a desire for better opportunities to learn and practice new skills as their reason for job change. Let’s look at what we know so far, and more importantly, what to do about it.

AI: The Inescapable Force

Also according to the research, 98% of professionals acknowledge the importance of upskilling in their career development, and that 84% of employees believe AI will have the biggest impact on the future of work. AI has gone from being a buzzword to becoming the driving force in shaping the modern work environment. Many team members are likely already using AI in their work and personal lives to varying degrees, but without effective training, the risks of misuse, inefficiency, bias and even data privacy breaches are all very real. Along with ensuring safe and ethical use, implementing AI training can provide a big opportunity to enable greater productivity.

The responsibility to respond to this shift lies on the shoulders of learning and development (L&D) teams. To adapt effectively, these teams have to embrace AI upskilling initiatives that give employees the necessary skills to thrive in an AI-driven world.

5 Key Action Points for L&D Leaders

1. Become familiar with AI tools. L&D leaders should lead by example and become familiar with AI tools. This can allow them to be effective AI champions and set a precedent for the rest of the organization.
2. Champion ethical AI practices. Upholding ethical and secure AI practices should be at the forefront of L&D’s agenda. This can ensure that AI is used responsibly and in alignment with established ethical standards.
3. Enhance the employee experience. Explore AI-driven solutions that improve the workplace learning experience, making it more efficient and enjoyable. Don’t be afraid to broaden the conversation to your human resources (HR) colleagues, who will be able to reinforce the drive for greater AI literacy in other workplace functions. Ask yourself which AI solutions can deliver the largest impact for each relevant task.
4. Promote a culture of continuous learning. L&D leaders should promote a culture of continuous learning to stay ahead of the big shifts, like AI, that shape the world of work. Identifying skills gaps and providing relevant training in the right format are vital components of this approach.
5. Prepare for change. In a rapidly evolving AI landscape, L&D leaders should guide their organizations through change. Embracing change as an inherent part of leadership will help companies navigate the challenges and opportunities that AI presents.

Actions You Can Take Right Now

1. Audit the use of AI in your business. And don’t neglect talking to and surveying employees about the tools they are using on their own initiative. Just because you haven’t told an employee to make use of an AI model, doesn’t mean they haven’t started harnessing the power of AI to make their work more efficient. Evaluate the AI tools being used, and ask employees how various tools have added efficiency, and where each AI model falls short or underdelivers. You should be thinking about building a rough competency map, so you can see where the knowledge gaps lie.
2. Explore new AI tools regularly. Your L&D team should have their ear to the ground when it comes to new services emerging that can further the work of various teams in your business, because new capabilities are emerging every week. A great way to keep up to date with new models is by using “There’s an AI for That,” a website that tracks the release of new AI models and groups them by categories and business application.
3. Make AI upskilling accessible. Give employees the learning resources they need to get familiar with AI, how it works, where the risks lie and how it can help them perform their roles better. A short course on core AI skills, like prompt engineering, could rapidly increase the performance of your teams. You could use AI resources like Synthesia, Veed or HeyGen to create your own training videos, or you can outsource the provision of learning materials to an external learning provider.
4. Develop an AI policy. This should be based on what you have learned and your business priorities and be an open conversation between leaders from across the business with L&D taking the lead on how to implement the most effective training. Making sure employees know what is an acceptable use of AI, and the process to follow in the workplace is an important step to avoiding data security risks, copyright infringement and misinformation.

A Deeper Dive

When rolling out your new AI upskilling program, you might want to treat the following four tips as a checklist to drive greater literacy:

• Use real industry data. Provide employees with access to industry data for hands-on practice. For example, in the finance sector, you might try using historical stock market data to teach predictive modeling and algorithm development.

• Showcase industry use cases. Share case studies and examples of machine learning applications within your specific industry. In e-commerce, illustrate how recommendation algorithms power personalized product suggestions for customers. You can find a whole host of ways in which AI is already being used behind the scenes in many of your employees’ day to day activities (i.e., online shopping, chatbots, autocorrect, maps and navigation) to drive home the point that the age of AI is already here!

• Demonstrate data cleaning and preprocessing. Highlight the importance of data quality by demonstrating the process of cleaning and pre-processing data. For example, if you were to work in manufacturing, you might want to explain how machine learning can optimize production processes by cleaning and analyzing sensor data. Data cleaning is essential practice for any accurate prediction or modelling, and yet so many non-quantitative workers have no idea what it entails. A basic understanding of data principles could empower your teams to use data with more confidence in their every day jobs, and hugely boost the reliability of outputs your teams get from their AI tools.

• Teach model evaluation. Train employees to evaluate machine learning models. This doesn’t have to mean training all of your teams to be engineers! It’s about giving them the literacy to understand why some models might be more reliable than others for certain tasks. For example, in marketing, show how models are assessed for predicting customer churn or optimizing advertising campaigns. Make sure your teams don’t just get familiar with one model, like Chat GPT, Jasper or Bard, and stop there.

The Role of L&D Leaders (And What They Stand to Gain!)

L&D leaders are uniquely positioned to lead the charge in AI upskilling. AI’s potential can be harnessed to benefit both employees and their organizations — provided that L&D leaders take the initiative and provide timely, continuous and relevant training. Better yet, AI can massively streamline L&D teams’ most time consuming tasks. According to a report by Synthesia, the average training video production time was reduced by eight days (62%) using modern AI tools.

Moving forward, L&D leaders must adapt and guide their organizations toward a future where AI upskilling is not an aspiration but a tangible reality. The efficiency gains will be noticeable, and it’s a key example of how the full impact of L&D teams on the functionality and futureproofing of a business can be felt.