Brent Balinski spoke to the Australian head of professional services company Genpact about the company’s recent report on artificial intelligence adoption and challenges.
How do Australians’ attitudes on the subject compare to those in other developed nations? What are some places where an impact is being made in manufacturing right now? And what are we even talking about when we say “artificial intelligence”?
We asked Richard Morgan, Country Manager of Genpact Australia, about these and other topics, and what the company’s recent survey of executives and consumers across Australia, the US, the UK and Japan discovered.
@AuManufacturing: Artificial intelligence is an an ambiguous term, famously described as a “suitcase word”. For the purposes of this conversation, what are we talking about when talk about AI?
Richard Morgan: For Genpact’s AI research, AI: 360 insights from the next frontier of business, we defined artificial intelligence as the intersection of technologies that reason, interact, and learn. In the manufacturing sector, this can encapsulate a broad range of applications, including data and analytics, enhanced quality assurance, predictive maintenance and optimising supply chain management.
@AuManufacturing:Tell me about the difference in attitudes between Australians’ and other countries’ attitudes towards AI making their lives better (43 per cent vs 59 per cent for the US). What do you put this down to?
Richard Morgan: There’s a notable difference between Australia’s and other markets’ attitudes towards AI. This has a lot to do with lingering concerns about such issues as AI bias and privacy. Our research supports this, with similarly high proportions of Australians expressing concerns about robots discriminating against them in the decision-making process. Australians say they don’t want companies using AI that intrudes on their privacy, even if the goal is to optimise their experience (70 per cent of Australians vs 64 per cent each in the US and UK.). Another contributing factor could be Australian workers’ fear that AI threatens their jobs (cited by 31 per cent of Australians).
It’s also worth mentioning that other markets such as the US and the UK have a higher uptake of AI than Australia, so their familiarity with these technologies could correlate to their attitude towards AI. While Australia lags its counterparts when it comes to AI adoption and outlook, there’s an opportunity for us to learn from other markets.
@AuManufacturing: In what parts of their business could manufacturers be looking to adopt AI? Where could they see the quickest results?
Richard Morgan: Whether it’s implementing AI solutions to ensure high product quality, using algorithms to make business predictions and forecasts, leveraging data and analytics to reduce costs, improve efficiencies and anticipate changes in the market, manufacturers could benefit from adopting AI at every stage of the production life cycle.
When it comes to seeing quick results, knowledge is power. Investing in AI platforms that input operational data and apply algorithms can help manufacturers to quickly pinpoint inefficiencies in the production process. Over time, these solutions can build on industry knowledge and changes to business processes to deliver useful insights.
Regardless of where AI is implemented, the key thing for manufacturers to remember is that AI gives businesses the opportunity to augment existing processes, rather than remove the need for human contributions.
@AuManufacturing: The majority of Australians surveyed willing to learn new skills is positive. What sorts of skills will become more valuable at manufacturing firms as AI adoption picks up?
Richard Morgan: Employees want training to upskill as more and more AI-related technologies are introduced into the workplace. It’s vital that Australian manufacturers provide their employees with opportunities to future-proof themselves.
While developing technical skills, such as coding or applied math, may immediately spring to mind, we should also encourage workers to develop their soft skills. Australian survey respondents said the ability to adapt to change (54 per cent), the ability to communicate and collaborate effectively (40 per cent), critical thinking and problem-solving skills (43 per cent), and thinking creatively (36 per cent) will help people to succeed professionally as AI becomes more prevalent in the workplace.
@AuManufacturing: I’ve seen a lot of interest here lately in machine monitoring and AI for predictive maintenance. What are you seeing?
Richard Morgan: Predictive maintenance is definitely a good use case of AI streamlining processes in the manufacturing sector. I’ve also seen several cases where AI algorithms have been applied to help streamline supply chain management processes. I also anticipate there’ll be an increase in AI robots working alongside employees on the operations floor over the next few years, so it will be important for Australian business executives to educate their workers and address their concerns during this stage.
@AuManufacturing: You probably can’t speak about clients by name, but can you tell me how one has adopted AI and how it’s changed how they operate?
Richard Morgan: We work with many companies across various industries. Let’s look at a few examples:
1. Contract reconciliation and enterprise performance
Most enterprises end up with thousands of contracts and multiple invoices associated with each contract. AI’s ability to read all the contracts by machine, and extract the relevant parameters (e.g., shipping rate) and associated values (e.g., $30/express delivery) converts all that unstructured data into a usable, structured format. This is also done similarly for all the invoices corresponding to those contracts. Once all this information is available in a structured data format, it is easy to run simple reconciliation algorithms that can identify over-billing and drive better economic performance.
2. Risk management in commercial lending
Risk management in commercial lending traditionally involved reading through each of the balance sheets and associated financial statements of the assets in the portfolio and calculating risk scores that are then aggregated across the portfolio. Today, forward-thinking companies are using AI such as natural language understanding to extract data from thousands of balance sheets – often in multiple languages and across different accounting standards – and dynamically calculating risk across the portfolio. This approach not only improves accuracy and efficiency, it makes risk assessment more responsive and more comprehensive, completely transforming the underlying business value drivers.
3. Wealth management
For institutional investment firms that deal with complex custodial statements each with hundreds of transactions across a broad set of instruments, including hedge funds, derivatives, and specialised funds, calculating and delivering performance reports is traditionally a cumbersome and difficult process. Machine reading these documents with computational linguistics provides the ability to extract all the relevant asset and trade information dynamically, and easily convert this data into automated portfolio performance reports. This cuts down on expense and effort. As an example, this process used to take up to 90 days; with AI, forward-thinking wealth management companies process all this overnight. Furthermore, these reports have become configurable and easily customisable, delivering increased business value to the institution’s customers, and transforming its overall service delivery.
Featured image: journal.jp.fujitsu.com
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Note about AI 360:
“Genpact worked with Wakefield Research for the current study, conducted between November 20 and December 3, 2018. The survey of C-suite and senior executives included 500 executives in the United States, United Kingdom, Australia, and Japan, and was conducted via an email invitation and an online questionnaire.
Respondents were from the financial services, healthcare, life sciences, high tech, consumer packaged goods, retail, and industrial manufacturing industries, and worked at companies with at least $1 billion in annual revenue or at least $50 billion in annual revenue for financial institutions.
Wakefield Research also used an email invitation and online survey to poll 4,000 adults in the United States, United Kingdom, Australia, and Japan, of which 2,103 were working at least eight hours a week. In 2017, Genpact conducted similar research, working with research firm YouGov, to survey 5,179 people in the United States, United Kingdom, and Australia. Of the total survey population, 2,795 were employed at least eight hours per week. YouGov conducted the fieldwork online between August 15-30, 2017. In a separate study conducted in June 2017, Genpact and Fortune Knowledge Group surveyed 300 global senior executives. Respondents were from the financial services, healthcare, life sciences, high tech, consumer packaged goods, retail, and industrial manufacturing industries, and worked at companies with at least $1 billion in annual revenue or at least $50 billion in annual revenue for financial institutions.”