Data and AI: The next revolution
From honing competitive edge to solving complex social problems, adoption of data analytics and artificial intelligence is transforming the way businesses operate and informing government policymaking.
By Reuters Plus
Artificial Intelligence (AI) and big data have moved beyond the hype to become embedded in the delivery of goods and services across all sectors – and the shock of a global pandemic has only served to further drive the trend.
The urgency of the response to the pandemic has broken down competitive barriers, prompting organisations to share data – the lifeblood of AI tools – to fight the impact of the virus, observes Dr Ian Oppermann, New South Wales (NSW) Government Chief Data Scientist and former CEO of the NSW Data Analytics Centre.
“Everybody took a huge step forward and shared data in ways that wouldn’t have been considered pre-pandemic,” he says.
Prior to the health crisis, investment in AI and robotic processing automation was already increasing exponentially. According to consulting firm KPMG, investment in these technologies is on a sharp incline, from USD 12.4 billion in 2018 to a forecast USD 232 billion by 2025.
In a 2020 survey of 2,395 executives by global consulting firm McKinsey half of the respondents had adopted AI in at least one function within their organisations1.
Governments are stepping in to accelerate investment in AI, says serial AI entrepreneur Dr Catriona Wallace, the founder and head of Ethical AI Advisory and Executive Chair of Boab AI, a joint initiative between venture capital manager Artesian Ventures and the Victorian government.
Boab AI is an accelerator fund that invests in AI scale-ups and has secured AUD 1.5 million of funding from the Victorian government’s start-up agency LaunchVic, along with AUD 8 million of capital from Artesian. The fund was founded to help Australia catch up with other advanced economies in its investment in AI.
“We recognised that there was no dedicated VC fund or VC accelerator for AI in Australia, which is well behind the rest of the world with its investment into AI. We have an equivalent of about a USD 2 per capita investment into AI, whereas the US would have close to USD 20,” Wallace says.
Boab AI will invest in more than 30 young AI businesses over the next four years. In addition, Artesian is raising an AUD 100 million venture capital fund focused on AI businesses.
While implementation of AI and big data tools has accelerated, many organisations are still struggling to leverage AI effectively. Research by ESI ThoughtLab, co-sponsored by Deloitte, shows a bifurcation between experienced and inexperienced AI users. AI leaders delivered a 4.3 per cent return on investment (ROI) in AI projects, with a payback period of 1.2 years, versus 0.2 per cent over 1.6 years for AI beginners, the study found.
“AI and data analytics hold great potential, but implementation is not easy,” says Bill Mew, digital privacy campaigner and founder of cyber consultancy The Crisis Team. “AI can only add value where it augments or improves what an organisation is doing, but many organisations still only use AI in a very limited capacity.”
Who’s doing it well?
Training machines to perform complex human tasks typically relies on large volumes of detailed data in which patterns can be identified prior to formulating responses. So, it’s no coincidence that businesses that are adept at data collection have been the best-placed to implement AI.
Global tech giants like Google, Facebook and Amazon have pioneered AI’s rapid development with their virtual assistants and image recognition tools reliant on vast data harvesting capabilities. However, successful AI and big data strategies have traction in other industries.
“AI and machine learning have been effectively used across a range of scenarios in the financial services industry to assess risks, reduce fraud and improve processes,” says Surabhi Jain, Chief Data Officer at Westpac.
“A large amount of data is captured during processes such as loan origination and customer on-boarding and AI is being used to make smarter decisions by accurately assessing several data points at lightning speed, whilst reducing human error.”
Jain adds that AI and machine learning are also being leveraged to strengthen key processes, such as know your customer (KYC) and anti-money laundering (AML) checks, by automating workflows, extracting data from multiple documents and authenticating information much faster compared to traditional methods.
AI analyses data in a way that humans can’t and recognises patterns from historical trends to make decisions. As an example, Jain explains, during customer on-boarding machine learning can enable cross-verification and validation of customer data across documents, reducing turnaround time and resulting in an improved customer experience.
In the energy industry, Spanish utility Iberdrola has developed machine learning to monitor electricity usage data, manage distribution and plan maintenance. The company sees AI and data as key to facilitating energy transition away from hydrocarbons.
Automotive manufacturers Mercedes and BMW have revamped production lines with AI-assisted “collaborative robots” that are operated by humans and have delivered unprecedented levels of customisation and efficiency. Worker downtime can be reduced by up to 85 per cent when humans and “intelligent” robots operate together, reports an MIT study,
In healthcare, pharmaceutical multinational Roche has used AI to improve its understanding of Parkinson’s disease, using mobile sensors to track how many tremors patients suffer day-to-day. In the US, regulators have approved a retinal scan algorithm developed by start-up IDx that can diagnose diabetic retinopathy in the absence of an eye care specialist.
Such businesses have forged ahead by leveraging data analytics and AI to accelerate growth. Behind the success of US food delivery group Doordash, which launched in 2013 and raised USD 3.4 billion in an IPO in 2020, are AI-powered software and data analytics that improve the efficiency of order collections and deliveries, and drive sales by making personalised menu recommendations based on customers’ previous preferences.
The value of sharing
Ecosystems that facilitate the collection and sharing of data from multiple sources for AI analysis are also fuelling private sector growth and productivity and helping governments to make better policy decisions.
The NSW Data Analytics Centre (DAC), for example, analysed anonymised debit card, credit card and merchant transactions from retail banks to glean social and economic impacts during the pandemic. The centre also pooled data sets from different government departments, such as transport and healthcare, to build unique insights into the spread and other consequences of COVID-19.
Legal issues govern privacy and what data can be shared, however, and clarity is needed on how sharing will deliver value for participating stakeholders.
According to Westpac’s Jain, “Multiple factors need to be evaluated before sharing data, including data privacy and security considerations. Constraints on data sharing arise, in part, from the operation of the legal and regulatory regimes governing data protection, such as European General Data Protection Regulation (GDPR). Hence, before any data is shared it is crucial to ensure that legitimacy is understood, and there are appropriate safeguards in place to protect data, depending on sensitivity of that data. This includes data-sharing protocols and agreements.”
Concerns about lack of controls around the rapid adoption of AI motivated Wallace, who runs an Ethical AI Advisory consultancy, to work closely with Karen Andrews, until recently Australia’s Minister for Industry, Science and Technology, and Edward Santow, Australia’s Human Rights Commissioner, to develop the official AI Ethics Framework for Australia.
Launched in late 2019, the framework lays out key principles for companies deploying AI to adhere to. Among them, AI must be built with human-centred values and environmental benefits in mind; it must be safe, reliable, fair and not discriminate; and developers are accountable for any harm caused by AI.
In the case of the NSW DAC project, putting in place a strong ethical foundation for data use was vital for building trust and securing buy-in from government departments sharing their data, says Oppermann. Fundamental to its success are clear, credible parameters for how data is categorised, who can access it and what questions it would be used to answer.
“Not all data sets are equal and over the years we have been building frameworks that distinguish between personal and sensitive data that is confidential, and open data that can be made freely available,” Oppermann says.
“We have worked to develop different governance and access mechanisms that specify how skilled, experienced and credentialed you need to be to view the data sets you’re dealing with, and the purpose you are using those data sets for. Ultimately, we are creating systems that support growing and building trust with the public regarding government use of data.”
This approach has allowed the DAC to secure data that has helped the NSW government with better-targeted policymaking and service delivery in the most difficult of areas.
Oppermann cites the creation of the Human Services Data Asset (HSDA), a dataset of vulnerable individuals, as another example of the vast potential data analytics holds to solve complex social problems.
The HSDA has expanded to include 30 years of history tracking all children that have been in and out of home care and identified as at risk of significant harm. Collected from multiple data sets across government, the information has provided unparalleled insight into the major life events of these children. This has enabled policymakers to identify the vulnerable points in a child’s journey that drive towards or away from desired outcomes.
The hope is that lessons from the COVID crisis, and the greater willingness to share data it has provoked, will accelerate collaboration and data sharing between parties in the future to create other prized data resources like the HSDA and harness the full potential of AI capabilities.
“There has been a real watershed in attitudes. People are now really lit up to the possibilities,” Oppermann says.
1 https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2020. See section 1.
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