AI Governance Gap: Despite 87% of surveyed executives claiming to have clear AI governance frameworks, fewer than 25% have fully implemented and continuously review tools to manage risks like bias, transparency, and security. This reveals a critical gap in operational oversight.
“AI is no longer just a tool; it’s a strategic imperative,” said Dee Waddell, Global Industry Leader, Consumer, Travel & Transportation Industries at IBM. “Retail and consumer product companies are at a tipping point where embedding AI across their operations can help define not just productivity gains, but the future of brand relevance, engagement and trust.”
The report emphasizes that successful brands can evolve from viewing AI as merely a productivity booster to positioning it as a core driver of enterprise innovation. Achieving this transformation will require rethinking traditional governance and reskilling strategies. Retailers should tailor AI initiatives to align with their brand priorities and collaborate with strategic partners, including start-ups and technology companies. Equally important is breaking down silos between finance, technology, and business leaders. By fostering cross-functional collaboration, these stakeholders can jointly build strong business cases that demonstrate how AI can help deliver a long-term competitive advantage.
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The IBM Institute for Business Value (IBM IBV), in cooperation with Oxford Economics, surveyed 1,500 global retail and consumer products executives in 15 countries in Q3 2024. 50% of the sample represented retail industry and 50% represented consumer products in nine countries in Q3 2024.
Participants were asked a range of questions in various formats (such as multiple choice numerical and Likert scale). They were asked about their organization’s expectations, results, concerns, and barriers for scaling or planning to scaleAI/Gen AI within the enterprise and across their ecosystem partners.
To accomplish this, the IBM IBV ran a series of contrast analyses, including pairwise comparisons and data classification using hierarchical clustering highlighting results differences as shown in this report. Significance level for all analyses was set
at (p < 0.05) level.
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