This article was first published in the May 2019 International edition of Accounting and Business magazine.

Once upon a time, artificial intelligence (AI) was all science fiction and no fact. Now, AI-enabled products and services are proliferating, and AI’s capacity to significantly change how we live and work is becoming ever more apparent.

Public perception changes with familiarity, but predictions about AI’s pros and cons span a bafflingly broad range. At one end of the spectrum is tech entrepreneur Elon Musk. ‘Mark my words, AI is more dangerous than nukes,’ is just one of his dire warnings about all-singing, all-dancing ‘general AI’ rather than the functional, narrow AI used in his Tesla cars (and other current AI applications).

At the other end of the spectrum is Steve Wozniak, co-founder of Apple. He used to share Musk’s forebodings, but last year declared: ‘AI doesn’t scare me at all.’ Why? Because a two-year-old needs only see a dog once to be able always to recognise one, whereas a computer has to see a dog over and over again to achieve that level of recognition.

Wendy Hall, computer science professor at the University of Southampton and an expert on AI, has a more balanced perspective. ‘There will be lots of positive benefits, but we need to get a grip of the downsides,’ she says, because change is happening very fast. AI technologies such as natural language processing, machine learning and machine processing are already improving processes, enhancing interactions, solving problems, performing functions and making decisions that used to be the preserve of humans. Expect ‘escalation and acceleration’, says Hall.

Mother lode

Only time will tell what AI is capable of. Meanwhile all kinds of organisations across all business sectors are jumping on the bandwagon, implementing solutions that AI makes possible today and exploring what it could make possible tomorrow. The really big AI successes, however, may be concentrated among the biggest online service and storage companies such as Alibaba, Amazon, Google and WeChat, because they have a head start and vast amounts of data.

‘Data is the key raw material that feeds machine-learning algorithms,’ says Narayanan Vaidyanathan, head of technology insight at ACCA. Massive growth in data volumes is one of the keys to AI advances. ‘We are producing lots more data than in the past, and our processing and computing capabilities are also expanding like never before,’ he says. ‘Tools like machine learning are poised for significant take-up because we have raw material and the ability to process it.’

Access to that raw material can be uneven. AI pioneers such as Amazon and Google have always valued the vast amounts of data willingly ceded to them, and have spent years preparing for the transition to macro-level applications of AI. ‘The last 10 years have been about building a world that is mobile-first, turning our phones into remote controls for our lives. But in the next 10 years, we will shift to a world that is AI-first,’ wrote Google CEO Sundar Pichai in a 2017 blog.

Pichai predicts a world where ‘computing becomes universally available – at home, at work, in the car, or on the go – and interacting with all of these surfaces becomes much more natural and intuitive, and above all, more intelligent’. This shift appears to be well under way. There are chatbot educators, and legal and finance professionals are interacting with AI applications in areas as diverse as audit, financial services delivery and close processes, and fraud detection.

A new ACCA report, Machine learning: more science than fiction, examines early-stage AI applications, shares current thinking on the use of machine learning, considers the ethical implications for the professional accountant, and explores how AI will influence future skills for professional accountants. The report also considers emerging issues such as how machine-learning algorithms make judgments, the avoidance of bias in data sets and algorithms, algorithmic accountability, and ensuring data provenance and veracity.

‘Data can only be used to create insight if it is clean and has been validated and properly managed,’ says Vaidyanathan. This presents an opportunity for the profession. In many organisations, senior finance people manage governance, structure and processes; as the amount and value of data increases, so will the involvement of finance professionals.

The vital go-between

The growing ubiquity of data may drive growth in the profession. Accountants have access to data from across the business, work with operational data and are increasingly involved in processes that reflect the changing nature of strategic and corporate reporting. In the brave new world of data-enabled AI, the profession brings some very valuable skills to the table. ‘If you want to get insights from the data that add value, you need to understand where you are going as a business and to link what you are doing with the data with where you are trying to go as a business,’ says Vaidyanathan.

Professional accountants understand how an organisation’s strategy, financial and non-financial information interact, and can communicate the resulting picture of value creation and direction for the company. Algorithms do lots of clever computation but you need business knowledge to ask the right questions and interpret the answers. ‘Professional accountants can add value in terms of bringing their professional scepticism and ability to interrogate, and having oversight of what the algorithm is doing,’ says Vaidyanathan.

Who’s liable?

The spread of machine-learning algorithms raises a host of thorny questions on accountability. Professor Karen Yeung at Birmingham Law School in the UK says there are questions to be asked about the distribution of authority, responsibility and liability, and who should be held accountable. AI’s utilisation of data is creating new data protection issues. However, meaningful ethical regulation of AI systems will be difficult to mechanise, not least because AI components and data from multiple jurisdictions are being built into products and services. ‘Grappling with these questions is the Wild West; nobody really knows what data ethics is,’ Yeung says.

Given that the abilities of AI are growing in proportion with data volumes, the world’s non-stop data growth seems likely to bring more problems. At some stage there may be an argument for breaking up tech giants or curbing their emerging monopoly on data. If we want to enjoy the benefits of AI we need to deal with some of the burdens – fast. Because as Hall observes: ‘The genie is out of the bottle.’

Lesley Meall, journalist