- Investment in AI is growing rapidly as businesses seek to capitalise on its burgeoning capabilities.
- There are questions about the impact of AI on job security, diversity and the environment.
- AI can help deliver more personalised experiences for customers.
Dr Andrew McMullan, CommBank’s Chief Data & Analytics Officer, recently discussed the future of rapidly growing AI technology as part of a panel of AI industry experts.
AI has attracted a lot of attention recently, both in the media and from major tech companies as they race to incorporate AI into their operations. Applications such as ChatGPT have sparked a new wave of interest in the ability of AI to learn and generate content via a seemingly human interaction; and the new ways of working and communicating that AI’s creating.
In his introduction to the forum, Andrew Groth, Infosys Senior Vice President and Regional Head, Australia & NZ, cited research his company had done into 1,200 organisations with revenues of more than US$500 million. Remarkably, it projected that the use of AI and data integration could generate US$467 billion in incremental profits.1
Impact of AI on careers
One of the major concerns that people have with AI is the impact that it may have on their jobs. In particular, white-collar sectors where AI has the potential to take on roles that were previously thought to be beyond the reach of automation.
The World Economic Forum predicts that by 2025, automation will have replaced 85 million jobs worldwide, although it will also have created 97 million new jobs.2
The panellists agreed that while AI will become increasingly capable of taking on new roles, it’ll also require oversight and human intervention. AI will augment the jobs that people do and increasingly employees will need to have AI skills.
The shortage of suitably skilled data scientists and software engineers is forcing many businesses to look at training staff to work with AI applications. A recent McKinsey survey revealed that nearly half of organisations using AI are sourcing talent internally.3
Dr McMullan underlined the importance of upskilling, citing a program CommBank has introduced to train its staff in new business areas.
“As we see the shift in the skills that are needed to serve our customers, we’re training people in frontline jobs to come and engineer with us or become analytics professionals or even get involved in machine learning and AI.”
Building diversity into AI
The panel also looked at questions about diversity in AI and how to ensure that non-discriminatory language and learning is incorporated into AI models. The challenge is to build AI systems that reflect the social and cultural diversity of a population in an unbiased manner.
Dr McMullan highlighted that organisations need to be cognisant of where an AI system is getting its data from and how it is learning.
“A lot of the generative AI that is being built is learning on data that could be potentially biased in terms of when it was written and the narrative that was around at that time.”
“So, from an organisational point of view, at CommBank we're very focused on making sure that we understand completely the AI use cases … to make sure that the principles, the policies and every single use case of AI is well-considered before we would do anything with it.”
AI and the environment
Discussion on the panel raised the issue of AI and its impact on the environment, especially the carbon footprint of the technology being used. Dr McMullan stressed the importance of being carbon neutral in all activities but also highlighted how AI can be used to deliver greater efficiencies, for instance in running data services.
“Our team is always thinking about the proactive and positive use cases of AI. You can use AI to actually improve data processing. We do this all the time. We monitor a lot of the computers and the systems that we have using AI to make them more efficient and ensure we're not burning more energy than necessary.”
Another example is using AI to detect and reduce data duplication. Having multiple copies of the same digital record is not only inefficient, but it can also make it harder to use the data effectively such as in marketing campaigns or targeted customer messaging.
Using AI to create personal experiences
Looking at how AI can be used to augment business practices, Dr McMullan highlighted how AI is enhancing customer experiences at CommBank by focusing on delivering a personal digital interaction.
“If you think about the rapid increase in how customers engage with digital banking, in our app or in our NetBank channel, we want to make sure that every time customers log in, the information that they are looking for is readily available and personalised to them.
“There’s been a huge investment in our Customer Engagement Engine which uses thousands of models to run over the real-time data that we have in order to serve the most important conversation or experience to customers in every interaction that they have.”
“So, we're very focused in trying to make our experience for customers as personal as it can be,” he concluded.
The next Wave of AI is here; What is AfterNext? forum was hosted by the Trans-Tasman Business Circle in partnership with Infosys. It was part of a series of discussions in the Infosys AfterNext series looking at how developments in areas such as technology and sustainability will shape the world we live in.
Speakers on the panel included:
- Professor Toby Walsh, Chief Scientist at UNSW.ai, the AI Institute of UNSW Sydney
- Dr Ian Oppermann, Chief Data Scientist, NSW Government
- Moderator: Professor Sally Eaves, Chair of Cyber Trust
Our expert on AI Technology
Dr Andrew McMullan is Chief Data & Analytics Officer in Technology at Commonwealth Bank, where he leads the data and analytics team. Core functions include Customer Engagement Engine (design and delivery of all customer contact strategies across all systems and all channels), Data Office and Data Science and Advanced Modelling (all AI, Machine Learning, Deep Learning, Adaptive Modelling and Advanced Analytics).
Previously, as Director of Customer Analytics & Decisioning at Royal Bank of Scotland, he was accountable for the delivery of Data, Analytics, Customer Decisioning, Customer Contact, Pricing Strategy and Customer Value Management for the Retail Bank and Small Business portfolio for all brands within RBS.
Earlier in his career, McMullan worked as a lecturer in Mathematics and Statistics, before leading the Rating and LGD methodology team for several years at UBS Investment Bank.
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