Firms are rushing to implement AI, blindsighted by the promise of a productivity surge. But the main quality they’ll need during this industry revolution is empathy, one tech pundit thinks.
At a time when media headlines are screaming mass layoffs and disruption of the labor market, David Johnson, VP of Insights and AI Architecture at North Highland, is hopeful.
"We will see major improvements in the quality of life and innovation over the next several years. That is very promising to me," he told Cybernews.
David, a technologist himself, is now in the consulting business and isn't denying the not always thought-through rush toward AI. Instead, he guides decision-makers through changes, asking to put people at the heart of every decision.
David's insights provide a breath of fresh air in the public discourse, particularly amidst stories like that of Stellantis, a car manufacturer that laid off 400 employees after requiring them to work from home that day.
Which jobs are at risk
There's no denying that certain occupations are at risk. David first mentioned content creation and translation.
"Think about organizations like IBM. They have hundreds of thousands of products. They create content for all those 100,000 products. They create them in English and they do translations into every other language around the world. The concept of translation and rewriting content in different dialects is instantaneous for generative AI," David said.
IBM recently announced it was about to slash jobs in marketing and communications departments. Last year, it cut nearly 4,000 positions and announced a massive upskilling of its workers in AI.
David also believes that call centers will be heavily affected by generative AI’s capabilities, leaving machines to have conversations and gather documentation while a human assistant can come up with recommendations for better customer experience.
"The irony of generative AI is also the amount of disruption that will probably be seen in the data science world because the citizen data scientist is going to have a tremendous amount of power. They're going to bring to the table domain knowledge and expertise that data scientists traditionally don't have. But they'll have all the power of data science at their fingertips through all these new technologies that are coming out. It's going to be very interesting in the next ten years," David said.
Promises of labor productivity (output divided by labor hours), either improving too slowly for businessmen's liking or even dropping in some sectors over the last few decades, are driving AI adoption. However, while innovation will drive organizations toward the results they are looking for, David believes it will be a journey rather than a productivity boost that happens overnight.
You can't just lay these technologies at the feet of people and, you know, claim the benefit of all the efficiencies and the productivity that comes from it. It really drives massive changes in ways of working.
Companies will have to be empathetic
Tech companies laid off nearly 57,000 people this year, as per layoff tracker layoffs.fyi. Last year, this number surpassed 260,000. But David doesn't seem to be alarmed by the numbers.
"We're nearly full employment here in the US [unemployment in the country was only 3.8% in February 2024]. We have a retiring workforce," he noted. Coupled with the skills shortage, especially regarding tech specialties, companies simply won't have the luxury of getting rid of people. Companies, as David pointed out, will have to be empathetic.
"Successful companies are going to be the ones that can adapt and address those needs of their customers and employees in the most efficient manner going forward."
Talking with client organizations, he noticed an "AI everywhere" approach. So David asks them to stop for a moment, take a breath, and zoom out – what skills and people does a company need going forward, and what technologies and innovations would actually support the firm's goal?
"AI gives us the opportunity to rethink the role of work, to rethink department level work, and not automate what we have been doing, but automate what we should be doing. That's going to be more efficient and productive going forward," David said.
He believes knowledge to be the foundational component of this shift, a first principle for AI implementation.
"I like the idea of shifting the conversation away from doing everything everywhere randomly, hoping that things converge at some point in the future to, you know, really zooming out a little bit, looking at the bigger picture, creating context for what we would do and then breaking it up, but have a vision for where we're going, not have it happen accidentally or not at all, because it wasn't well planned from the beginning, right?"
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