The artificial intelligence era is entering its second act: when hype meets reality.
Enthusiasm over the technology continues to build, both in the popular imagination and in financial markets, especially in the US. But a sense of unfulfilled promise is also beginning to set in. While 97% of organizations recognize the value of AI and have used it as a strategic lever for transformation, only 17% have found that their investments in AI have exceeded expectations, according to an Oliver Wyman survey of 300 global firms.
That is frustrating considering AI’s massive potential to help companies simultaneously become more efficient and seize growth opportunities at a time of tremendous global change.
To tap AI’s full potential to drive productivity and revenue, companies first need to understand how the landscape is evolving across three dimensions: the geopolitical, the industrial, and the organizational. Those that figure out their place across all three will be best positioned to reap the benefits of AI over the next few years. While there is no single, all-encompassing solution, there is a possible path for everyone.
Navigating the global dimension
Understanding AI requires a global perspective and an understanding of who is leading and why. A clear set of winners is emerging, according to the Oliver Wyman Forum AI Index, which ranks countries by their readiness for an AI-driven future across over a dozen metrics. The United States is at the forefront, with enormous data center capacity and eight times the private funding and startup density of second-place China, which leads in infrastructure and scientific output, holding nearly half of all global AI patents.
Other nations in the Magnificent Eight excel through specialization: South Korea leverages semiconductor prowess, the United Kingdom shows strength in private funding and startups, while Canada’s renewable energy capacity and research excellence and Germany’s combined startup ecosystem and patent development secure their positions among the leaders. Emerging players like Saudi Arabia, benefitting from low-cost electricity and a Google partnership, and Singapore, leading in per capita AI publications and investment, are gaining ground.
As geopolitical competition intensifies, organizations must navigate emerging blocs of influence strategically — especially if they can’t continue to remain neutral and play all sides. So far that has been a challenge, but companies that follow the right road will position themselves for success.
Spotting industry trends
Our research shows that different industries are emphasizing varied combinations of efficiency gains and revenue growth, with leaders emerging through distinct approaches tailored to their specific contexts and capabilities.
The healthcare sector is one early standout, with more than 11% in average cost savings and 10% in revenue growth. This success reflects careful expansion of proven use cases, particularly in areas targeting operational efficiency — for example, AI-powered medical coding and documentation systems that improve revenue cycle productivity while reducing administrative burden.
Banks are another early success story, with nearly 12% in cost savings and 8% in revenue growth through AI initiatives in 2024, according to an Oliver Wyman global survey. Their data infrastructure and experience with machine learning for risk assessment and fraud detection is a key driver, reflecting long-term strategic AI investments, in some cases spanning over a decade.
The good news is that we are still in the early stages of AI value capture across all industries. Organizations in every sector that develop systematic approaches to identifying and scaling proven use cases can position themselves to generate significant value from this technology.
Setting organizational strategy
Beyond industry dynamics, companies of different scales face distinct advantages in their AI journey. Startups excel at breaking down enterprise-scale projects into lightweight “AI as a microservice” components, while small-cap companies focus on targeted, high-impact use cases with clear return-on-investment thresholds. Large enterprises can typically leverage their substantial computing resources, massive proprietary datasets, and ability to make significant sustained investments in AI infrastructure and talent. CEOs of large firms are much more likely to be investing heavily in AI with the aim of becoming a market leader than smaller companies, according to an Oliver Wyman Forum/New York Stock Exchange survey of CEOs of publicly listed companies.
Different organizations need different approaches to AI transformation, and success hinges on understanding which is right and why. Four corporate archetypes are emerging that show how extensively and how quickly AI is being implemented across business.
About 11% of companies are all in, according to Oliver Wyman surveys, pursuing rapid enterprise-wide AI transformations. Most are tech companies in the US and China, enabled by modern infrastructure and substantial investment capacity.
The largest group of organizations, at about 46%, are implementing AI selectively, particularly in regulated industries or those with legacy systems. This approach allows them to carefully validate AI’s impact in controlled environments, minimize disruption to critical operations, and build internal capability before scaling.
Almost a third of companies are pursuing rapid but targeted transformation in select parts of their business where impact will be highest, typically chosen for quick wins and clear returns.
About 12% of organizations are undergoing broad but gradual transformation. These are often established market leaders with extensive business partnerships. They are systematically integrating AI across their entire business network, recognizing that lasting change requires bringing their whole ecosystem along.
There is no right or wrong answer. Companies that understand their organizational context — from their technology infrastructure to their regulatory environment to their competitive dynamics — can select and adapt their approach accordingly. The key is not the speed or scope of implementation but rather ensuring the chosen path reflects capabilities and constraints and can maintain momentum.
Fortune favors the tenacious
History suggests that transformative technologies build slowly before taking off, with productivity gains surging once adoption hits 60%. Given AI’s widespread rollout already, we expect this productivity surge to materialize faster than previous technological revolutions.
It hasn’t yet kicked in yet. But organizations that maintain momentum through this critical phase will be best positioned to capture the dramatic productivity gains that historically emerge when a breakthrough technology reaches mass adoption.