This would represent a solution to some of the major challenges in education, such as the need for societies to train many more workers in advanced digital skills. Platforms such as Coursera and innovative models such as that implemented at the 42 network of schools hint at what’s possible: large-scale, low-cost education with minimal human involvement. It is likely that the adoption of technologies at the start of this spectrum will occur more quickly and perhaps more broadly than those towards the end. However, it is also noteworthy that the value created by AI varies across the spectrum; the more disruptive the AI, the more difficult it is to adopt, but the greater the potential value it can generate. Difficulties with AI adoption While we will certainly soon see individual projects showcasing the possibilities of each category of AI, the broad adoption of such technology across our sector is likely to be slower than generally anticipated and we can look to the past for a precedent. The adoption of internet technologies occurred over several decades and turned out to be less disruptive for most people than many enthusiasts had anticipated. Why is this the case? Capital constraints will hinder the deployment at scale as these technologies will prove to be expensive, particularly in the short term. It will also take time to develop the basic know‑how required to use AI effectively, in the sense of intangible capital. Legislation, such as the EU AI Act, will serve its role as a brake on innovation while stakeholders get organised. Incumbent business schools face additional hurdles. The creation of the necessary digital infrastructure and subsequent automation will require the redesign of business processes and the adjustment of business models. This is hard to do and compounded by the fact that business schools (and their broader universities) are traditionally reluctant to make the significant investments in research & development required for such programmes. Then there are organisational challenges. There will be a need to overcome cultural barriers to AI adoption and a significant requirement for skills development, both of which are time- consuming and entail proficient management. A further factor likely to slow the adoption of AI is that incumbent schools are, in the short term at least, unlikely to experience significant pressure to implement the more radical opportunities provided by AI. The market’s preference is for tradition and
of AI increases, so too can the complexity of the tasks that are automated. While this level of automation requires a solid digital foundation, it can lead to significant improvements in both the student learning experience and operational efficiency. Algorithmic management : At this stage, AI begins to take on a more directive role, akin to the way algorithms manage tasks in sectors such as healthcare or logistics. Algorithmic management involves AI systems allocating tasks to educators or students based on data-driven insights. This approach, while potentially unsettling due to the reduced human agency it may entail, offers the potential for more personalised and efficient experiences. System-level AI : The final stage is a fully automated, AI‑driven educational system, analogous to ‘lights-out’ factories in manufacturing, where robots completely manage production and maintenance. This could potentially revolutionise education by enabling high-quality learning at scale and at very low cost. “It is easy to imagine schools adopting digital assistants, but more challenging to picture them pursuing paths to a system-level AI”
26 Business Impact • ISSUE 4 • 2024 12
Made with FlippingBook - Share PDF online