AI is coming fast
I am not sure who said it first, but there are only two ways to react to exponential change: too early or too late.
— Ethan Mollick, “Reshaping the Tree: Rebuilding Organizations for AI”, One Useful Thing
When I hear the term disruptive technology, I tend to roll my eyes. The phrase is used with such abandon that it has become a joke. If you’re not peddling a disruptive technology, are you even a tech company?
So it is with some hesitation that I find myself starting with this: AI is the real deal. I don’t know how to describe what the new generation of AI is about to do to our lives and our businesses without referring to the concept of disruptive technology.
“AI [is] becoming a general-purpose technology with an impact similar to that of the steam engine, electricity and the internet. The hype will subside as the reality of implementation sets in, but the impact of generative AI will grow as people and enterprises discover more innovative applications for the technology in daily work and life.”
— Gartner, “Gartner Experts Answer the Top Generative AI Questions for your Enterprise”
AI’s disruptive technology story is not based merely on hype. ChatGPT is estimated to have reached 100 million monthly active users just two months after launch, making it the fastest-growing consumer application in history. For comparison, six years after Tim Berners-Lee created the first website at CERN, the World Wide Web had only 36 million users.
Image source: Financial Times, “Will generative AI transform business?” , October 25, 2023
Businesses are also taking AI seriously. A just-released study describes the change in AI use in business in the last year this way:
In 2024, Generative AI (Gen AI) entered a new phase, as companies moved beyond initial hype and amazement towards a focus on proving ROI and understanding its performance. Trial of Gen AI surged this year, with 72% of decision-makers reporting uses of Gen AI once a week, compared to 37% in 2023…Greater experimentation has shifted sentiment, with more decision-makers feeling “pleased,” “excited,” and “optimistic,” and less “amazed,” “curious,” and “skeptical.”
— Wharton and GBK Collective, “Growing Up: Navigating GEN AI’S Early Years” , October 2024
AI is coming fast and it promises to radically reshape how work gets done, including the work of training professionals.
The impact of AI on the training industry
Relatively little attention has been given to how AI might play out specifically for the training industry. But there are some clues. McKinsey released a study comparing how AI might impact task automation across various job categories. It concluded that the growth potential for AI automation is higher for educator and workforce training roles than for any other occupation group.
Note that growth potential does not mean the total percentage of work that will be automated, but the gap between how much automation was possible before generative AI compared to what will be possible with it.
Image source: McKinsey “The economic potential of generative AI: The next productivity frontier” , June 14, 2023
Training has been less susceptible to automation than many other job categories because it requires skills that, up to now, only humans possessed—creativity, audience analysis, dealing with ambiguity, nuanced communication, understanding human psychology, etc. McKinsey estimates that prior to the advent of generative AI, only 15% of the work time for educator and workforce training roles consisted of tasks that could be automated. This is the lowest of any occupational group.
However, with the advent of generative AI, McKinsey estimates that the potential for automation for training roles jumps to 54% of work time. This 39% jump in potential automation is the largest of any occupation group, although it’s worth noting that the 54% total time savings with AI is still slightly below average (42nd percentile) when compared to all job categories.
The possibility that roughly half of the work time for those in training roles could be automated in the future may feel both exciting and disconcerting. A worst-case reading is that training industry jobs could be cut in half. Before you panic over the worst-case scenario, consider these additional factors:
- Potential automation is not the same as achieved automation. No company succeeds in automating every task that it is possible to automate. And introducing automation at scale can take years. Competing priorities may keep training lower down the priority list for large-scale AI initiatives.
- Efficiency is not the only use case for AI in training organizations. It may not even be the most impactful. AI has the potential to improve training quality, consistency, learner engagement, and personalization. These improvements increase the business impact of training, which could result in additional investment. This type of innovation does not replace employees, it empowers employees.
- Many of the tasks that AI can do well are rote—the kinds of tasks training developers would gladly give away. Pushing these tasks to AI leaves more time for creative tasks, making training work more interesting and rewarding. In fact, working with AI is itself an interesting and creative task. Surveys show that the majority of knowledge workers who have tried ChatGPT in the workplace come away with positive feelings toward the experience.
- The coming changes are not a zero-sum game between employees and computers. As AI changes the nature of work, new roles and tasks will emerge. The World Economic Forum’s *Future of Jobs* report for 2023 shows that **25%** of companies surveyed predict that AI will lead to job losses, However, **50%** expect AI to lead to a net increase in jobs. Although automation may reduce the need for some training roles, new roles are likely to emerge.
The changing nature of training work
AI isn’t likely to make training professionals obsolete. However, it’s almost certain to change the nature of training work. Fortunately, the skills that make you good at training—creativity, problem-solving, precise use of language, analytical expertise, logic, research, etc.—are likely to make you good at using AI once you learn how to apply them. However, there is an important asterisk. Your previous skills alone won’t be enough to keep you competitive in your career moving forward.
As AI becomes increasingly common in training departments, those who have made the effort to develop related skill sets will have a leg up. If you want to be prepared for the evolution of the training industry, here are a few things you can begin to work on right away:
Learn about AI – Study how AI algorithms work and explore use cases for AI in the training sphere. Forward-learning training organizations will always be looking to push the envelope. The more you know about AI, the better you will be at finding clever ways to use it.
Get hands-on experience – It’s easy to get mediocre results from AI with simple prompts. However, to get the best out of AI, you need to learn prompting and workflow best practices. Tutorials and sample prompts can help. But blindly following recipes won’t lead to proficiency because AI responds very differently across various user contexts and tasks. Only hands-on experience can help you figure out how to use AI effectively for your setting and use cases.
Understand AI risks – Learn about responsible AI practices and how to address challenges around social bias, intellectual property issues, data leaks, and fact-checking AI outputs. This knowledge will be increasingly valuable as AI takes root and may become a job role of its own.
Focus on human skills – Double down on soft skills like empathy, collaboration, creativity, and human-centered design. Current AI is most capable when handling routine or intellectually oriented tasks. Employees who have both soft skills and AI proficiency will have a leg up.
Improve data literacy – Whatever your training context, eventually you’ll bump against the need to supplement the built-in knowledge of AI models with your own data. Learn how to gather, clean, integrate, and analyze relevant data available in your organization.
Embrace continuous Learning – AI isn’t something that you learn once and then move on. A commitment to continuous learning is critical if you want your AI skills to stay relevant. The techniques you use today will not be the techniques you need tomorrow because AI is evolving at a breathtaking pace.
The advent of generative AI can be a risk or an opportunity, depending on how you approach it. We are still in the early stages of the generative AI revolution. It pays to be cautious about large investments in a technology that is very much in flux. And, of course, you should always avoid falling for the inevitable hype that accompanies a disruptive technology in its early days.
That said— unlike other learning technology trends that never really took off— it’s hard to imagine a future where AI isn’t a significant force driving the future of training. There’s too much early momentum, too much potential, and too much money already being invested in AI for it to simply fade away.
We may not be able to predict exactly what the AI training future will look like, but a head-in-the-sand mentality is not a good bet. Whether you are a training leader responsible for the performance of your team or an individual contributor thinking about your career, investing the time and effort to understand AI will give you a head start. History shows that even when initial forays into a new technology aren’t 100% successful, the lessons learned by early adopters tend to give them a lasting advantage that separates them from the laggards over the long haul.
