SKILLS DEVELOPMENT
which can sometimes seem tedious, are important in helping employees learn the skills that can help them to critically evaluate the recommendations from an AI system in order to solve these dilemmas. If they have not had the opportunity to learn these skills through practice, it can become difficult for them to know how to react too. There will ultimately need to be a balance – while employees should be able to use AI to free up time and focus on higher-level tasks, they will still need some exposure to underpinning, potentially repetitive tasks to develop their critical thinking skills and acquire the knowledge necessary to apply those skills effectively. Gaining a rounded understanding There is no doubt that AI and other similarly transformative technologies are here to stay. Equally, it’s important to remember that the technology remains in its infancy and – despite a lot of noise in the mainstream media – still has significant limitations. For now, any business that wants to make the most of AI must ensure there is a steady human hand on the steering wheel. That is why it is important for us to think about the development of human skills, as well as the future interplay between humans and AI systems, in addition to the advancements in AI algorithms. As such, academics across multiple disciplines need to work together to ensure that business leaders have a rounded understanding that will enable them to make the best decisions.
The first cohort of students at the CDT in Decision Making for Complex Systems will start in the 2024/25 academic year; we are confident that our approach will provide students with all the tools they need to successfully tackle the biggest challenges in their field. The taught component in the first year of the CDT equips students with the required knowledge in AI and the wider AI context, including a focus on ethics, responsible and reproducible research, entrepreneurship and productivity, where contributions from AMBS and the university’s Faculty of Humanities are of key relevance. The centre’s cohort-based approach ensures that students are exposed to a variety of applications of AI, providing them with an in-depth understanding of how state-of-the-art systems can be meaningfully applied to solve today’s biggest challenges. As well as having a cross-disciplinary advisory team throughout their course, there is also an opportunity for students to share and pick up knowledge from different disciplines through their peers. With five different focus areas for our PhD projects, we are confident that students will emerge with a thorough appreciation of the wide applicability of AI, along with the ability to abstract and generalise across disciplinary boundaries. Mitigating the risk of de-skilling As with any new technology, there are questions regarding the impact AI will have on jobs and whether some roles will be obsolete once AI has developed sufficiently. This is something leaders need to be ready to manage. The technology certainly has its advantages – for example, computational models and algorithms are not impacted by fatigue in the same way as human beings. Furthermore, there is a legitimate argument that AI can free up employees’ time to do complex and potentially more rewarding tasks. Ultimately, this may significantly affect traditional career paths. In theory, it will minimise the amount of time more junior employees spend doing routine, repetitive tasks, enabling them to progress to managerial roles more quickly. In practice though, companies will have to find new ways of equipping employees with the necessary skill sets and critical analysis skills to perform well in higher-level complex tasks. This is particularly crucial to ensure that humans are – and remain – equipped to offer an appropriate level of oversight to any practical AI solution. As we know, AI learns by analysing trends and patterns in historical data. This means it can encounter difficulties when it is faced with cases that are different to anything it has seen before. Where a human may be able to adapt and use their critical thinking skills to devise an alternative solution, AI models can struggle to produce a suitable response. Human oversight will remain crucial in ensuring the correct identification and handling of such situations. This becomes more complex if the responsibility for underpinning simpler tasks lies solely with AI. Those early tasks,
Julia Handl is a professor in decision sciences at Alliance Manchester Business School and a member of the European Laboratory for Learning and Intelligent Systems. Her research relates to data-mining and optimisation approaches across various applications, including protein structure prediction. Handl holds a PhD in bioinformatics from the University of Manchester, as well as master’s and bachelor’s degrees in computer science
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Business Impact • ISSUE 3 • 2024
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