4 ways to avoid a AI disaster at work

To avoid ending up on the front page due to your own AI stuff-up, the experts have some simple rules to maintain ethical, and safe, practice.

It’s not wrong to use AI in your work. It is wrong to trust it blindly – and to lie about using it.

That’s the message from experts following yet another high-profile incident of AI hallucination-riddled work being passed off as expertise.

“You cannot blame an individual for wanting to be more efficient,” he says. “In fact, we have a productivity problem in Australia. We want workers to be more efficient. So seeking out ways to be efficient is something to be celebrated.”

But Nyilasy says many leaders have developed “magical thinking” when it comes to large language models (LLMs), believing they have the power to do work, create efficiencies and deliver a return they were not designed to deliver.

“They say, ‘It didn’t solve all my problems.’ Well, it was never meant to. It’s not a wizard, you know? Some people think about AI as a wizard.”

Gartner AI futurist Aaron McEwan warns that extreme pressure from the top might be incentivising a less than cautious approach to AI down the ranks.

A report by AI analytics company Dataiku released in May found 74 per cent of CEOs admitted they were afraid of losing their jobs over the next two years if they failed to deliver “measurable AI-driven business gains”.

“CEOs are putting enormous pressure on their workforces and leaders to do something with AI,” says McEwan.

“It’s leading to a lot of pressure on organisations and employees to use the technology, even potentially when it’s not appropriate or the best tool to solve a problem.”

To avoid ending up on the front page due to your own AI stuff up, the experts have some simple rules to maintain ethical, and safe, practice.

1. Follow your firm’s AI guidelines

Firstly, ensure your AI usage adheres to company policies. “There must have been policies [about using AI] within Deloitte,” says Nyilasy. “So, number one is knowing those policies and following them.”

It’s also encumbent on companies to make sure those policies are clear and communicated.

And if your firm doesn’t have AI guidelines it would be a good idea to ask for some, so you know what you need to avoid falling foul of.

2. Use AI for the right tasks

Management chair in the Faculty of Business and Economics at the University of Melbourne, Professor Danny Samson, says until AI can be relied upon to avoid all errors – which he is sceptical will ever be the case – it is paramount that the tool be used only to “augment human capability, not to replace human capability, on anything but the most trivial tasks”.

“It would be fine and dandy if AI was completely trustworthy, but it’s just not,” he says.

“AI is essentially a black box to managers. We can’t know why it does what it does or even how. This is the first technology in human history where that is the case. For the first time ever we’re expected to accept and act on what it tells us without knowing why, to trust what it tells us.”

Samson says the only real check for factual errors is experienced human supervision.

And when it comes to public-facing work or strategic decision-making, an AI should not be the primary author.

Steven Hunwicks, a partner at law firm Thomson Geer, advises companies on technology, cybersecurity and data privacy. He says the risk of using AI is “at least as high as using an unsupervised junior team member to do work and them making a mistake”.

Should you leave a strategic decision to AI? “I think the answer is no, you should not,” Samson says.

3. Be transparent in your use of AI

While the Office of the Information Commissioner recommends businesses “update privacy policies and notifications with clear and transparent information about their use of AI,” no laws or regulations force a disclosure about when you’ve used AI in your work.

“If it goes against policies, obviously you shouldn’t [use AI], but if it’s in a gray zone that’s where you get into trouble. Maybe it’s not explicitly banned, but you’re not sure,” Nyilasy says. If in doubt, disclose.

“You should never pass work off as something that it isn’t because that’s an ethical failure. That’s deceit, that is not right.”

Hunwicks says the simple question to ask when faced with a grey area is: “If your employer or a customer found out you used this information in an AI tool, would they be OK with it? The answer to that question will send you in the right direction.”

He adds disclosure is important on all sides. “If you’re the employee, you should tell your boss where you have used AI and if you’re a company, you should communicate, disclose and seek consent for that use. What we’re seeing today are the consequences of situations where you used it, didn’t disclose it, and passed it off as your own.”

4. Practice critical thinking when assessing AI output

LLMs are designed to be very persuasive, so you need to make sure you’re maintaining your critical faculties and incentivising yourself to do the work needed to check results, even when it would be easier not to.

Nyilasy says his hypothesis is that AI vigilance is like going to the gym, a theory the BehavAI Lab is exploring. “We know how to motivate people to go to the gym. Having a routine, for example, having reminders, socialising it, having other people reminding you that you need to do it.”

But as any lapsed gym-goer will tell you, consistency is both key – and difficult to maintain.

“It is hard,” Nyilasy says. “When I talked to many business leaders about this they didn’t really like that message. They were sold on AI as easy. Everybody’s telling them, this is easy. But it’s actually not. This is hard. This is complicated. It will take a lot of effort from the organisation standpoint too.”

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