If 2024 was the year of experimentation with generative AI, 2025 is the year organizations are racing to scale. Yet many are discovering that scaling is far more difficult than piloting. Gartner predicted that 30% of initiatives will be abandoned after proof-of-concept stage by the end of 2025. The enthusiasm surrounding AI’s promise is colliding with the reality of organizational complexity: legacy systems, siloed data, skill shortages, and rising regulatory scrutiny.
Executives are increasingly asking a pressing question: Why do so few AI pilots scale?
Our 2025 IMD AI Maturity Index provides an answer. Drawing on data from the world’s 300 largest companies, the index evaluates how effectively organizations embed and scale AI across five dimensions:
These dimensions reveal that success with AI is not about deploying the newest model but about alignment of leadership, people, and technology around a shared purpose. There are business benefits, too. Companies investing across all five areas are outpacing their peers. In our 2025 data, the top 100 firms achieved average year-over-year revenue growth.
The Index covers 10 industries and highlights distinct routes to AI maturity.
Automotive leaders such as Volkswagen Group and Mercedes-Benz Group are redefining mobility through software-defined vehicles. Volkswagen’s in-house AI copilots personalize driving behavior, while Mercedes-Benz’s MB.OS™ platform continuously optimizes vehicle performance via over-the-air updates. BYD in China accelerates scale through partnerships with PTC, combining design and production data in a single AI environment.
In manufacturing, firms like Siemens and GE Aerospace are embedding AI across design-to-production cycles. Siemens’ partnership with Nvidia enables real-time simulation and predictive maintenance on factory floors, while GE Aerospace applies AI to parts forecasting and quality assurance. These industrial pioneers illustrate how embedding intelligence into physical products and operations transforms AI from a side experiment into a core capability.
“Mastercard uses generative AI for real-time fraud detection, scaling models across millions of daily transactions.”
Mastercard uses generative AI for real-time fraud detection, scaling models across millions of daily transactions. KKR integrates AI into investment modeling and deal sourcing, blending machine insight with human judgment. Ping An Insurance has built an in-house AI research arm that powers underwriting and claims automation across its ecosystem, while Goldman Sachs and Visa have formalized Responsible AI principles and oversight councils.
These firms show how AI can become an enterprise-wide decision engine – streamlining risk management, regulatory compliance, and customer service simultaneously. Their maturity lies not just in analytics capability but in governance and workforce upskilling to ensure models remain transparent and explainable.
The shift is from efficiency to creativity in this sector. Walmart leads with its Wallaby™ large-language model (LLM) that assists associates and optimizes merchandising decisions in real time. Kroger uses predictive analytics and smart-shelf technologies to anticipate demand fluctuations and reduce waste. Unilever’s Beauty AI Studio™ develops customized product formulations and marketing content using generative AI, while L’Oréal and Nike apply AI to brand storytelling and product design.
These firms illustrate how mature organizations use AI to create hyper-responsive and differentiated customer experiences. They invest heavily in employee AI-fluency programs to ensure adoption extends from headquarters to the shop floor.
Companies such as Equinor and Engie stand out for embedding AI into grid forecasting, carbon tracking, and safety optimization.
Companies such as Equinor and Engie stand out for embedding AI into grid forecasting, carbon tracking, and safety optimization. SLB and PTT use digital twins for real-time subsurface analytics that cut both cost and environmental impact. In these heavy-asset sectors, scaling is pragmatic: firms integrate AI within existing systems to balance reliability, regulation, and sustainability targets.
In healthcare, leaders like Medtronic and CVS Health are embedding AI into diagnostics, remote monitoring, and clinical decision support. Medtronic’s surgical-analytics systems assist physicians during operations, while CVS Health’s AI platform personalizes patient-care plans. In pharma, AstraZeneca and Merck & Co. apply large-language models to accelerate drug discovery and clinical-trial design. Sanofi’s partnership with OpenAI marks a new phase of AI-enabled R&D collaboration.
These organizations exemplify responsible scaling: they build robust ethics frameworks and internal sandboxes before deploying AI at enterprise level, ensuring compliance and patient trust.
Telecom giants like Deutsche Telekom and KDDI are embedding AI to predict network outages, optimize bandwidth, and personalize service.
Unsurprisingly, technology companies top the Index: Nvidia, Microsoft, and Alphabet lead global AI infrastructure development, Nvidia dominates in AI chips, Microsoft’s Copilot® suite and partnership with OpenAI democratize AI tools, and Alphabet’s Gemini models set new benchmarks in foundation-model capability.
Telecom giants like Deutsche Telekom and KDDI are embedding AI to predict network outages, optimize bandwidth, and personalize service. These firms show what hyperscaled AI looks like – treating AI not as an application but as the platform underpinning all future products and services.
Our research highlights several lessons for executives determined to move from pilot to performance:
Scaling AI is as much about managing change as it is about managing code. The most successful firms treat it as a transformation across several dimensions. The message for executives is clear: moving beyond pilots means building maturity. The future belongs not to those who experiment with AI, but to those who trust it, govern it, scale it, and make it work.
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