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McKinsey: Gen AI Could Boost Retail Industry Margins and Revenues

McKinsey: Gen AI może zwiększyć marżę i przychody branży handlowej

AI on board - fewer employees, profitability grows rapidly

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AI on board – fewer employees, profitability grows rapidly

Gen AI in trade could unlock $240-390 billion

Generative AI (gen AI) has become a mainstream phenomenon in late 2022, and retail executives have quickly realized the potential. Mentions of artificial intelligence (AI) in retail earnings calls have skyrocketed in the past year, which is no surprise given that gen AI is poised to unlock $240 billion to $390 billion in economic value for retailers, representing an industry-wide margin increase of 1.2 to 1.9 percentage points. That, combined with the value of non-generative AI and analytics, could turn billions into trillions, according to McKinsey & Company.

Over the past year, most retailers have begun testing different ways to use gen AI across the retail value chain. But even with all that experimentation, few have managed to fully leverage the technology’s potential at scale. Of the more than 50 retail executives surveyed, most say they are piloting and scaling large language models (LLMs) and gen AI broadly, but few say they have successfully implemented gen AI in their organizations, according to a McKinsey & Company study.

Retail harnesses the power of AI

Some retailers have struggled to widely implement gen AI because it requires reprogramming parts of the organization in terms of technical capabilities and skills. Concerns about data quality and privacy, insufficient resources and expertise, and implementation costs have also challenged the speed at which retailers can scale their gen AI experiments, McKinsey & Company finds.

Retail companies that have successfully harnessed the power of gen AI tend to excel in two key areas. First, they consider how gen AI use cases can help transform specific areas of the business, rather than dissipate resources. Second, they successfully move from pilot and proof-of-concept to large-scale implementation. This requires not only data prioritization and technology integration, but also significant organizational changes to support widespread AI adoption.

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