Stacy Boit,

When AI engineer Malcolm was working at a data analysis firm, executives wanted to use generative AI to categorise the customer database into a range of personas.

“Don’t use AI,” was his advice.

A traditional machine learning model would have been much more appropriate, he argued, producing consistent, repeatable results. And it would have been much cheaper.

“They still went ahead with Gen AI,” says Malcolm (we have not used his real name).

That meant a process that was less accurate and much more expensive, but it also allowed the organisation to say they were embracing AI.

Malcolm’s experience will be familiar to staff at other companies. More bosses are embracing AI and insisting their staff use it.

In February, global consultancy Accenture reportedly told staff that promotions to top roles would require “regular adoption of AI tooling” and it would be tracking their usage of the AI platform it has developed.

And in May, rival firm KPMG said it had developed a dashboard to track whether its US employees’ meet a 75% usage target for its AI tools.

The company says this is part of “a holistic effort… to help people move up the AI maturity curve.”

Other organisations are taking a less targeted approach to implementing AI but nevertheless expect it to transform how their workforces spend their days.

Governments are also hoping to tap into some AI magic.

The UK government is banking on AI to help “rewire” the state and boost efficiency across Whitehall.

However, research by the civil servant union, the FDA, shows that while civil servants were open to the idea of using AI to improve productivity, there’s doubt that management can handle the transformation.

Less than a third of civil servants had been consulted on how the technology could be rolled out, the union found, meaning “change is being done to workers, not with them”.

FDA general secretary Dave Penman said the rollout was “inconsistent across departments which limits the productivity gains”.

If organisations are quick to highlight AI adoption, says Dan Boyles, CEO of consultancy Hello AI Collective, they’re not always clear on why they’re adopting it and how they expect to benefit.

“I was with an oil and gas company, and I sat with the C-suite, and I just went ‘what’s the reason for using AI?’ And none of them could agree.”

The firm’s CEO cited the need to keep up with competitors, Boyles continues, while the head of sales said they wanted to make more money, and the marketing team wanted to stop using outside contractors.

This sort of confusion at the top can mean AI investments fail to deliver on expectations.

“I think the wreckage is organisations not getting the ROI [return on investment] from it that they were expecting and not getting their people engaging with it,” says a senior consultant at one large consulting firm, who did not want to be named.

In his firm, everyone had access to two AI tools, but could request specialist tools for specific tasks, such as coding.

If their job demands it, “some of our people will have access to four or five, AI, tools”.

Organisations needed to consider the people side of the equation, he continues. “There are generational differences in terms of confidence levels with regards to this. There are potentially gender differences.”

And before anyone in his organisation can have access to a tool, he says, they must take mandatory training covering AI ethics and risks such as bias. This training also makes clear that AI tools can be sycophantic and hallucinate, he adds.

The pre-existing culture in an organisation can make or break an AI rollout, not least because AI tends to accelerate things for better or worse, says Caroline Rawlinson, CEO of Culture Amp, which tracks employee experiences and feedback.

The firm says that while nine out of 10 HR professionals expect to increase their use of generative AI, a third said “say no one currently owns AI strategy at their companies”.

“If you’re putting AI technology on top of a fragmented culture or a fear-based culture, it is not going to succeed,” says Rawlinson.

“At best, it becomes a very slow roll out as people don’t understand what they’re being asked to achieve or the tools that they’re being provided with. At worst, it ends up as quite a big, wasted effort.”

In the case of the oil and gas company Boyles was helping, the president eventually said: “I want to increase my operating earnings because I want to sell [the company] in years.”

That motivation was the key bit of information for Boyles.

His team could then go to each department, talk through their processes and technology, identify bottlenecks, and work out where AI could actually help.