Eric Hazan, an expert in strategic marketing and digital transformation issues, is Senior Partner at McKinsey & Company. He is one of the leaders of the Growth, Marketing & Sales Practice in Western Europe. He also works within the High Tech, Media & Entertainment, and Telecommunications Practices, as well as in the Digital & Analytics and in the Retail Practice with a focus on marketing and strategy, and the co-author of a June report on the economic potential of generative AI. He is also a member of the board of directors of the McKinsey Global Institute, the economic think tank of McKinsey & Company. Prior to joining McKinsey, Hazan was a Senior Partner at Arthur D. Little, where he led the global telecoms, Internet, media, and entertainment practice and the consumer practice. He started his career in marketing and sales in consumer goods at Kraft Jacobs Suchard and at Danone. Eric Hazan, a speaker at the Viva Technology conference in Paris June 14, holds a Master of Science degree in management from HEC Paris, where he is a professor of business strategy. He recently spoke to The Innovator about the findings of McKinsey’s new report.
Q: What are some of the key findings of the report?
EH: Generative AI’s impact on productivity could add trillions of dollars in value to the global economy. Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion across 63 use cases. About 75% of the value that Generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering and R&D. This translates into big productivity gains, around $400 to $600 billion for retail, which can use AI to accelerate collection of consumer insights and assist customers in their shopping journey; $60 to $110 billion in life sciences, which can use the technology to improve drug discovery process efficiency and reinforce safety throughout the drug development lifestyle, and $200 to $340 billion in banking, which can use Generative AI to speed the transformation of legacy systems and automate reporting and risk documentation. Banking, telcos, relational businesses like retail and life sciences will benefit the most in the short term, other sectors will follow. The potential is great. The question after that is the speed of adoption. Already we are seeing companies like Morgan Stanley, Walmart and Novartis moving fast to adopt the technology.
Q: While there is a need for speed to stay ahead of competitors what about the risks?
EH: There are big risks without guardrails; our report outlines some of them. They include algorithms that reflect historical bias; training data and model outputs that infringe on copyrighted, trademarked, patented or otherwise legally protected materials; models that hallucinate or produce different answers to the same prompts, impeding the users’ ability to assess the accuracy and reliability of outputs; and finally Generative AI models could significantly impact the workforce and/or lead to detrimental impact on society if not used with care. We need to envisage the concept of technology social responsibility in our digital and data strategies.
Q: So how can companies capture the potential value while managing the risks that AI presents?
EH: Move fast but make sure the technology is being deployed in a socially responsible way. Don’t deploy AI without having in mind the potential negative consequences. Look at everything from a 360 view rather than only measuring the impact on productivity.
Q: What will be the impact on jobs?
EH: We estimate that 75% of today’s work activities could be automated by 50% between 2030 and 2060, roughly a decade earlier than in our previous estimations. AI’s impact is likely to most transform the work of higher wage knowledge workers. Our advice is don’t think about this of this as a way to cut expenses without thinking about your people. Create AI copilots to augment people in their work so that they can focus on higher value tasks and level up their performance. If you make them redundant you risk throwing out the baby with the bathwater. This will necessitate training and reskilling people and require huge organizational redesign. The net creation of jobs in tech is very positive but the demand is not equal to the offer. That is the transition we are in, and it is happening faster than we thought. We have accelerated our models by ten years.
Q: What is your advice to corporates?
EH: Immediately start analyzing and testing and select use cases that will have economic impact while ensuring that you are being socially responsible and managing the organizational consequences. If you don’t move ahead on AI others will. There is no going back.