One of the central themes that emerged out of the recent FT Future of AI Summit in London was how the employment landscape and the skills required in the workplace are evolving in line with artificial intelligence development.
Influential commentators have frequently touted the notion that AI ‘will take all our jobs.’ Reports from the likes of Goldman Sachs have only added to these predictions by indicating that AI could replace the equivalent of 300 million full-time jobs by 2030. This being said, it’s important that the benefits which AI has brought, and is continuing to bring to workplaces are not overlooked.
We forget that throughout history there are ample cases of the employment landscape shifting as our technological capabilities have advanced. It is estimated that today, 60% of people in the workforce are doing jobs that did not exist before 1940.
Rather than replacing humans, AI can and should enhance human capabilities, embracing adaptability and rapid learning.
AI entrepreneur, Rotem Farkash remarked at the Summit that ‘AI reshapes jobs by enhancing human capabilities, not replacing them, and developers can lead this shift by building tools that amplify skills and foster innovation.’
For Farkash, this transition is possible, provided we align AI investment with workforce training; propel augmented skills in line with technical proficiency; and seek a balance between automation and human judgement.
Adopting AI is a long-term commitment that involves not only investing in technology but doing this in tandem with building solid AI literacy and competency from intern level to C-Suite. Such fusion is only possible through an organisation-wide commitment with leaders setting the example for all to follow.
During an event at the FT Future of AI Summit centred around embracing the shift to an AI-powered workforce, Global Head of AI and Data Science at Prosus, Euro Beinat, spoke to this precisely.
‘We started with the leaders, so the first top 150 leaders for companies in the group… went through workshops…. It is a process that continues over time and then from there we went down through an entire organisation.’
For Beinat, the logic was clear: if decision-makers understand AI capabilities, they can lead more effectively, meaning this could be scaled across the organisation, resulting in an organisation-wide awareness of AI’s potential applications.
Looking to merge augmented skills with technical proficiency is another crucial step to this.
AI’s integration into workflows has typically required technical skills such as coding and machine engineering. However, as the technology has commercialised, the reliance on technical skills has become less central to extracting the maximum from the technology.
Lee Fulmer, Board Chair of OpenUK, argued at the Summit, that AI implementation should focus on enhancing employees’ existing roles with easy-to-use tools as opposed to complex coding languages like python.
This is certainly true. By making AI accessible, companies can empower employees to leverage their capabilities for improved productivity. Indeed, whilst it is not necessary for every employee to be literate with coding languages like python, in certain industries other technical skills should be retained and expanded.
For instance, in financial services, because an AI system can perform financial modelling more efficiently than a human, this is not to say that the technical skill of financial modelling should be forgotten because of this.
By extension, it is vital that automation and human judgement are balanced.
Although AI can handle repetitive tasks, or tasks that require a high volume of simulations, maintaining human oversight ensures that AI outputs are contextually accurate and are aligned with business values.
Fulmer identified this as the ‘Human in the Loop’ concept, emphasising the importance of maintaining human activity as the driving force behind tasking AI systems. In this sense, AI systems should prove even more effective if they are guided by human initiative.
Again, in financial services where a significant amount of data is unstructured, large language models (LLMs) can validate and deliver key financial data onto the blockchain to provide it with the structure ready for human intervention. This being said, human beings still need the technical acumen to contextualise and read this data.
Fundamentally, if the workforce both understands how and why the data delivered to them by the AI system has been structured, a harmony can be struck between employee and system.
Business and employees have a lot to gain from coordinating their relationship with AI systems. However, to protect the employment infrastructure, it is critical that this relationship is balanced and developed in unison.
The FT Future of AI Summit was a significant step forward in realising this opportunity and tasking business leaders with the mission of educating their workforces.
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