Companies need to build “innovation muscle” to thrive amid AI-driven disruption, according to Joe Depa, global chief innovation officer at EY.
Depa made the case at the recent Reuters Next leadership summit, where he described how his firm is adapting to AI and what other companies should consider as they get started on their own AI journeys.
Depa, who sat with Steve Hasker, CEO of Thomson Reuters, at the event, said his remit spans EY’s data office, its global AI program and what the firm views as the next frontier — physical AI, quantum and blockchain.
“We launched a new physical AI lab, and we’re going to invest in physical AI because we think that’s the next big trend to come,” Depa said.
Physical AI refers to the integration of AI with real-world systems such as robots, self-driving vehicles, cameras and other machines, so they can operate autonomously. EY’s physical AI lab is in Alpharetta, Ga., where its central technology team is also located, Depa said.
While Depa touted EY’s inroads with AI, others at the event sounded a more cautionary tone. Hasker, for example, noted that the technology has been characterized as an extinction-level threat to certain industries and jobs, including consulting.
He cited comments from Peter Thiel that “consulting will go the way of the wagon wheel and be replaced by Palantir-type data and analytics offerings.”
Depa acknowledged that AI is changing the consulting landscape but argued that AI and AI agents will be used to assist and enable consulting teams. “We don’t expect a net job loss in consulting … the jobs are 100% going to change very, very quickly and already have,” he said.
Learning a new form of job security
Adaptability will be the new job security, Depa said. It will also be vital for the survival of companies. Organizations that build up what he called “innovation muscle” through workforce training, data and other assets will enjoy an advantage over the next two to 10 years. “The ones that don’t are going to fall behind very, very quickly,” he said.
In pursuit of such advantages, humans must learn how to manage AI agents, not just team members, Depa said. That will include treating those agents comparably to people — recruiting, training, certifying and attesting to the functions the agents fulfill. When the time comes, those agents will be retired to make way for new ones. Physical AI, such as robots, will also require that human touch, he said.
Still, when compared with other industrial revolutions and technological evolutions, Depa said the pace of disruption — and near-term impact on jobs — is different. “The difference this time is that the disruption is going to occur faster,” Depa said.
AI is here to stay, Depa added, and he hopes it will have a positive impact on society with medical outcomes, quality of life and future job growth, even if the current moment is “a little painful now.”
Key elements to get started with AI
Depa said companies still finding their feet with AI should focus on four essential elements in order to make the most of it.
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Get the data right. Generative AI and physical AI require high-quality data. According to a 2024 EY study, some 83% of 500 senior business leaders surveyed said they did not have the right data infrastructure and could not leverage the latest AI.
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Embed responsible AI in everything the organization does. This includes data, models, algorithms and training of employees. The inclusion of such AI can reduce compliance risk and improve job growth and revenue through such practices, Depa said. “It gives people guardrails to experiment.”
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Value. Attach innovation efforts to real use cases and opportunities, such as updating back office, finance and procurement for productivity savings. He said software development and programming jobs have taken a hit because of AI, falling from a top area of growth just a few years ago to almost net zero. Companies should retool and reskill their staff in such jobs.
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Adoption. Having the best technology and data does not matter if no one uses it. “I’ve never heard a client say they’ve overinvested in training,” Depa said. Training itself will change, however, and can be led by AI.
What lies ahead for AI
Depa outlined several areas that will be significant to AI’s future and for the development of companies that aim to leverage its resources.
Data. Data is the lifeblood and bottleneck for AI, he said. AI can be used to improve data quality. Industries such as financial services have long invested in data and are well-equipped to take advantage of next-generation technology.
Autonomous enterprises. Self-driving enterprises will rise, according to Depa. The phenomenon has already been seen in some smaller companies and startups where a lone CEO runs the business with AI agents. He said he expects large companies to explore comparable approaches for their back office, finance, human resources and procurement, but with agents working with humans and then physical AI and robots.
Sovereign AI. Depa said as more countries establish policies on how to treat data and AI models, he foresees the rise of the concept of sovereign AI. Countries may require teams, data, infrastructure and tech to be located within that country in order to use AI. This would lead to multinational companies creating what he called “AI islands” within those countries.
Trust. Trust will be the new currency in the AI-dominated landscape, especially after issues already seen with misinformation and deepfakes, Depa said. Organizations that do not embed responsible AI into their operations may face a disconnect with a public that wants authenticity. Consumers are growing more aware of AI slop spawned online, which may have contributed to increased attendance at live events such as concerts and sports. “People are getting somewhat tired of the fake AI and deepfakes,” Depa said.
