Do you have a sense of how many people in your organization regularly use AI? Whatever you are thinking, the number is likely much higher — and the contexts might surprise you. Whether your employees use it as a research assistant, a writing aide, an image generator or a coding tool, the AI transformation of your organization is already underway.
Good news might also be bad news
The growing awareness and experimentation with AI tools present exciting opportunities for innovation across industries. However, alongside these advancements come new and potentially challenging risks for organizations. From an L&D perspective, ensuring that organizational capabilities and competencies around AI are robust is paramount.
Here are three fundamental questions to consider:
Managing change is not new
Considering the business implications and applications of any technology extends beyond understanding its technical aspects. It’s about understanding the systemic changes that it may cause or enable, and the strategies required to lead and manage those changes. L&D professionals are no strangers to managing change and this new challenge is no different than previous technology-fueled market disruptions.
At MIT Sloan Executive Education, within many of the AI-focused courses that we’ve been developing, up to half of the curriculum is dedicated to understanding how AI impacts organizations and individuals, and the necessary adaptations in organizational structures and management practices. Addressing the human dimensions of AI’s impact is essential for fostering innovation in today’s dynamic business environment.
But the pace of change is unprecedented
The challenge that we are all experiencing, particularly around AI and Generative AI, is the breakneck pace of change along all the dimensions. Your list of things to consider all at once may look something like this:
Traditionally, for learning programs, there’s been a very good practice of being thoughtful about designing around detailed needs analysis, documentation of intended learning outcomes, and iterative improvements. If you have that sort of traditional design model, it risks being out of date by the time any course is ready.
Courses developed over a 9-to-12-month period can quickly become outdated before launch, necessitating constant updates. To stay ahead, we need to explore alternative approaches to learning design that foster agility. This means being able to update existing learning content continuously and swiftly develop new content as needed.
For example, leveraging comprehensive company data analysis to craft impactful learning experiences has long been a reliable strategy. But doing that methodically runs the risk of slowing you down so much that you’re not really distributing useful current content in this space. How do you solve for that? At MIT Sloan Executive Education, we’ve been experimenting with shorter education modules: more focused, but still in-depth content that satisfies the ballooning demand in nearly real-time.
Discovering sources of expertise
Even though we have offered AI-focused courses at MIT Sloan Executive Education for many years, the interest from organizations and individuals has mushroomed in the last 12 to 24 months. As we have been expanding our programming on the topic, we’ve been discovering that not only are there experts in AI and its applications on our faculty and in our ecosystem, but also a lot of leaders at MIT, the Sloan School faculty and researchers have been accelerating the use of these tools in their work.
This is a hypothesis, but many organizations might find that sort of richness as well. Don’t assume that your IT function is the only expert on anything technology-related. In fact, IT could be posing constraints to the use of AI within your organization because they are legitimately concerned about the risk management and support needs.
Filtering out the noise
With the AI field developing so rapidly, the challenge lies in finding training programs that align with your organization’s specific business objective. As interest surges, many individuals are quickly transitioning to become voices in the AI discourse, adding to the diversity of perspectives.
The question arises: to what extent should AI tools be leveraged to accelerate the creation and updating of learning assets? This presents both an intriguing opportunity and a significant challenge for various reasons. These technologies enable anyone to create a curriculum, allowing anyone to generate what appears to be a well-structured executive course on AI simply by engaging a generative AI tool. However, questions persist about the proprietary nature, accuracy and comprehensiveness of the content.
How do you sort through and filter what the high-quality sources of knowledge and insight are, what the future potential is, and how fast that will come into reality? And what does employing these technologies actually mean for organizations and their workforces? It all may feel daunting, considering how fast AI technologies are evolving, but keeping your focus on the human needs and organizational needs is a good place to start.
by Peter Hirst (senior associate dean for executive education at the MIT Sloan School of Management)
Source: hrdive.com
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