Banks seem to hate when their customers go on holiday. Somewhere in the world, Joe Bloggs has gambled that he won't need to bring cash with him to sunny Sweden, since the Swedes seem to accept card payments more or less everywhere. As soon as he taps his plastic to the reader, however, there's still a small chance that his bank decides to block the transaction. After all, say the algorithms, what evidence is there in the corporate records that Mr Bloggs is ever likely to pay for his kladkakka in Stockholm? Billions of these types of decisions are made every day by machine learning (ML) algorithms in banks.

Impersonating someone is hardly a revolutionary type of fraud, but this summer Patrick Hillmann, chief communications officer at cryptocurrency exchange Binance, found himself victim of a new approach to spoofing – using an artificial intelligence (AI) generated video also known as a deepfake. In August, Hillmann, who has been with the company for two years, received several online messages from people claiming that he had met with them regarding "potential opportunities to list their assets in Binance" – something he found odd because he didn't have oversight of Binance's listings. Moreover, the executive said, he had never met with any of the people who were messaging him. In a company blog post, Hillmann claimed that cybercriminals had set up Zoom calls with people via a fake LinkedIn profile, and used his previous news interviews and TV appearances to create a deepfake of him to participate in the calls. He described it as "refined enough to fool several highly intelligent crypto community members."

Artificial intelligence is being adopted by organisations across the world to make better decisions, to innovate and to achieve increased efficiency with intelligent automation of business processes. This is leading organisations towards what I call'enterprise autonomy' – and to a world in which the majority of what we today call office work is automated. In my book'The Autonomous Enterprise – Powered by AI', published by BCS, The Chartered Institute for IT, this week, I define autonomous enterprises as having the bulk of their transactional and simple decision-making processes automated, but that is not all. They also tap the enormous amount of data that they are capturing from their digital and automated transactions for analysis and decision support. They use the data extensively to optimise their processes and business operations and, importantly, to innovate.

A "new milestone in competitive programming" was trumpeted by Google's British AI subsidiary DeepMind earlier this month, when it unveiled AlphaCode, a system it claims can write fully fledged computer programmes that compare favourably to the work of humans. Software development has long been pinpointed as an area where AI can have a significant impact, and with the advances AlphaCode and other systems offer, is the prospect of machines replacing human coders a realistic one? DeepMind says AlphaCode is capable of understanding a problem then writing a programme which solves that problem. It says it has tested the system against people who took part in coding contests and found that its results rank within the top 54% of human participants. "Solving competitive programming problems is a really hard thing to do, requiring both good coding skills and problem-solving creativity in humans," said Google software engineer Petr Mitrichev, who takes part in coding competitions.

Synthetic datasets are becoming increasingly popular for training artificial intelligence models. Proponents of this computer-generated data say it protects personal information and reduces the chances of bias emerging in AI systems. But for many, concerns over privacy and accuracy remain. New use cases for synthetic data are emerging daily. On Thursday, the International Organization for Migration (IOM) charity announced the launch of a synthetic dataset for human trafficking, which has been developed in partnership with Microsoft Research.

In August 2019, lobbyist Bryan Smith told a board of Utah's police chiefs, municipal officials and emergency responders that his company, Banjo, could provide them new insights on where crime was occurring in real time. To be sure, that would require running huge data flows through its proprietary algorithm – CCTV camera feeds, 911 calls, emergency vehicle locations – but Banjo would achieve this without endangering the personal privacy of anyone caught up in this new surveillance dragnet. Armed with a contract allowing Banjo to operate in every county in Utah, by January 2020 the company began to receive data flows from around 70 municipalities, the state's Highway Patrol, and Department of Public Safety. Any optimism among lawmakers as to Banjo's effectiveness, however, was short-lived. In May of that year, the company's CEO resigned after his past as a white supremacist was exposed, prompting the suspension of its contract with the state of Utah and an audit into its practices.

With the research showing growth and transformation are the top priorities for their organisations, this puts technology leaders and the exploitation of data capabilities at the heart of digital business agendas. The research also found that data analytics was the area where technology leaders expected their investments to grow the fastest, and is only behind cybersecurity for where the most technology investment is going in the coming year. Members of the Technology Leaders Agenda 2021 advisory panel of CIOs and CTOs said that after years of hype around AI, innovations in the data ecosystem had finally reached enterprise maturity. As such, it was no surprise that AI and automation initiatives are increasingly being implemented. "AI and automation are now solidly delivering the use cases and value promised three years ago," says Nadine Thomson, global CTO at MediaCom.