For all the experiments the news media industry is running with GenAI, what is the biggest challenge they face? 

Leaders are concerned about a slew of risks, ranging from unpredictable costs to reputational damage due to unreliable output. But the one that pops up most often in conversations with those building GenAI applications is: dealing with humans.

“Employees don’t resist AI because they fail to understand its benefits — they resist because they’re not convinced those benefits apply to them. And why should they fully support a change that might disrupt their jobs or overturn familiar processes? AI anxiety is real,” data scientist Tobias Zwingmann wrote recently.  

“To be clear: The human factor is what makes or breaks your AI road map. Without engaged employees, AI will never reach its full potential.”

Indeed, an informal poll during the end-of-year INMA GenAI Town Hall, for which almost 500 people registered, showed user acceptance and adoption of GenAI was the biggest challenge for the largest number of respondents.

“AI is a people business,” as Robert Zilz, head of data at Germany’s Kölner Stadt-Anzeiger, recently said. 

“Building products and applications is the easiest part of the process. Then, putting in place related processes is a bit more difficult. But organisation and culture are the most challenging to change.”

It turns out running a GenAI pilot can be easy — but scaling GenAI adoption across an enterprise often proves to be difficult. People are afraid of being replaced by the machine and, in some cases, will even use the tool but pretend that they did not. 

A recent survey by Slack of more than 17,000 workers across the world found about half (48%) of all desk workers felt uncomfortable admitting to their manager they used AI for common workplace tasks because they feared it made them seem less competent or appear lazy.

In other cases, the novelty and excitement of having a cool new toy wears off, and users shrug off the new technology.

What works best? When editors build tools themselves, as they do at Omni in Sweden. Similarly, Newsquest and The Financial Times in the UK give journalists a GenAI toolbox to experiment with and build their own solutions. This is now possible because GenAI is so easy to work with, unlike older AI, which required a more technical background. 

It also helps ensure GenAI efforts are focused on solving real problems. 

“The advantage of being led by editorial is that you don't end up with AI solutions looking for journalism problems. This also means that journalists are having to think about things like costs and data more than before, meaning a generally less siloed workflow,” wrote Enders analyst Niamh Burns in a recent report on AI usage in the British media industry.

Other important steps are to reduce fear by specifying clearly where AI can be used and to have leaders model that by using it themselves, according to Wharton professor Ethan Mollick. 

“Figure out how to reward people for revealing AI use. If productivity gains happen, workers need to benefit as well,” he said, citing not only monetary benefits but also incentives such as the ability to work from home. “Give others the opportunity to show their uses as well.”

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