By: Michael McQueen
The best way I’ve heard the role of AI in today’s corporate world describes is as a multivitamin or a painkiller.
As a multivitamin, it can supercharge your activities, working alongside employees to ensure that outcomes are achieved to a greater level of quality, efficiency and productivity. As a painkiller, it takes over all the tasks that are common headaches for workers, freeing them up to focus their attention on more valuable and gratifying work.
In the banking and finance sector, AI is set to enable significant productivity gains for those that integrate it. Providing automated reporting, improving risk transparency, automatically updating policies and procedures and performing compliance and risk audits are among the ways generative AI will be capable of improving efficiency. Beyond this, algorithms can analyse vast amounts of financial data to identify patterns and trends, enabling more accurate predictions and informed decision-making.[1]
In the risk and compliance sector, generative AI can take three broad forms. It can be used as a virtual expert, like the one recently developed by McKinsey which can provide tailored answers on finance related questions according to the firm’s information and assets. It can take the form of manual process automation, performing the time-consuming tasks that typically disrupt individuals’ workflows. Finally, it can perform code acceleration, updating or translating old code or assisting in writing new code.[2]
Role of Leadership
With all of this in motion, finance chiefs and leaders around the world are having to make decisions about the integration of AI within their companies. While the returns can be enormous, the initial cost is no small matter, with some big companies investing millions for the implementation of AI infrastructure and the forming of partnerships with software companies.[3]
There are several options for sourcing AI. Businesses can pay to use the proprietary models from companies like OpenAI or can also build their own generative AI tools using open-source models like Meta’s Llama 2 AI model. Building AI models from scratch is rare.[4]
Motorola is one company testing applications of AI. So far, they have used it to summarise complex industry contracts and assist in the development of code. Airbnb has been experimenting with implementing it within their customer service, using it to automatically identify features of properties on the platform like room types and amenities to help guests find relevant listings.[5]
Effect on Workers
While concerns about the effects of automation have been primarily directed towards blue collar workers for years, generative AI entails a much more widespread impact. People with university degrees, once considered the safest from the effects of automation are now in the direct firing line.
Workers including business analysts, marketing managers, administrators, software developers and lawyers are all at risk with this new wave of machine learning that is only gaining momentum by the day. Especially in organisations with budgets large enough for AI experimentation and implementation – including big corporations like Goldman Sachs and JP Morgan Chase or tech behemoths like Google and Microsoft – jobs are likely to be affected.
In the finance sector, research suggests that banks are spending 60 to 80 per cent of their payrolls on workers most likely to be affected – or replaced – by AI.[6]
It must be noted, however, that what is most likely is that AI won’t act as a complete replacement for human labour but as an addition that significantly streamlines workplace activities. For businesses and workers, there is a rising need to learn to work alongside AI as well as an opportunity to reimagine the human contribution of employees to their work. In any case, the trick will be using the technology to improve the lives of humans, rather than the other way around – lest it create a headache rather than cure one.
[1] Agarwal, R, Kremer, K, Kristensen, I & and Luget, A 2024, ‘How generative AI can help banks manage
risk and compliance’, McKinsey & Co, 1 March.
[2] Agarwal, R, Kremer, K, Kristensen, I & and Luget, A 2024, ‘How generative AI can help banks manage
risk and compliance’, McKinsey & Co, 1 March.
[3] Braughton, K & Maurer, M 2024, ‘CFOs Tackle Thorny Calculus on Gen AI: What’s the Return on Investment?’, Wall Street Journal, 24 March.
[4] Braughton, K & Maurer, M 2024, ‘CFOs Tackle Thorny Calculus on Gen AI: What’s the Return on Investment?’, Wall Street Journal, 24 March.
[5] Braughton, K & Maurer, M 2024, ‘CFOs Tackle Thorny Calculus on Gen AI: What’s the Return on Investment?’, Wall Street Journal, 24 March.
[6] Lohr, S 2024, ‘Bankers, lawyers and tech workers mostlikely to be in AI firing line’, Sydney Morning Herald, 11 February.
Article supplied with thanks to Michael McQueen.
About the Author: Michael is a trends forecaster, business strategist and award-winning conference speaker.
Feature image: Photo by Solen Feyissa on Unsplash