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With 71% of organizations actively deploying artificial intelligence (AI) at scale, the companies experiencing the most dramatic success share a common characteristic: They’ve made the most significant investments in IT modernization and are undertaking multiple AI projects simultaneously.

The emergence of this critical divide in the enterprise AI landscape is one of the key findings from of more than 250 senior IT leaders representing multi-billion-dollar companies across the U.S., EMEA, and APAC about IT modernization and AI.

The survey paints a clear picture of AI’s current impact on enterprise operations. More than half (56%) of the surveyed organizations reported revenue growth directly attributable to AI initiatives, and 54% are seeing increased staff productivity. Another 51% reported enhanced customer engagement. But the degree of success varies significantly, depending on an enterprise’s underlying infrastructure investments.

Given the positive results that respondents reported, it’s little wonder that spending on AI is exploding. Worldwide generative AI (genAI) spending alone is expected to reach $644 billion in 2025, a 76.4% increase over 2024, according to Gartner.¹ What’s more, nearly half of the organizations had dedicated budgets for AI projects in 2024, up from 26% the year before, according to the Foundry AI Priorities Study 2025.²

Big bets pay off

The most compelling insight from the survey centers on what researchers classified as “heavy investors” in IT modernization: enterprises that have undertaken four or more significant modernization efforts. These organizations consistently outperformed their peers across every critical business metric.

Heavy investors achieved improved IT efficiency rates of 89%, compared to just 61% for all others. Similarly, these organizations reported faster AI adoption of 85%, versus 60% for their less modernized counterparts, and 98% of the heavy investors experienced increased innovation, compared to 75% of other organizations.

The most significant disparity appeared in accelerating time-to-market capabilities, where heavy investors achieved success rates of 87%, compared to just 32% for all others. This dramatic differential underscores how IT modernization extends far beyond technical improvements to deliver core business advantages.

The modernization advantage extends directly to AI implementation capabilities. Heavy investors demonstrated significantly higher confidence in their infrastructure’s ability to support AI applications, with 48% expressing strong confidence, compared to 33% among other organizations. This confidence translates into more aggressive AI deployment strategies, with 72% of the heavy investors actively modifying AI applications in production, compared to 41% of other organizations.

Building on a foundation of innovation

The survey also reveals that successful organizations are incorporating AI into critical aspects of the business, building on prior innovations such as cloud and DevOps. Over the past five years, leading enterprises have prioritized developer experience enhancements in digital transformation, with 71% investing in automation to improve developer productivity. This focus on developer empowerment reflects a recognition that people remain central to successful technology deployment, even as AI automates many routine tasks.

Platform standardization emerged as another critical investment area, with 66% of the surveyed organizations working to gain visibility across diverse environments. This effort addresses one of the most persistent challenges in enterprise IT: managing complexity across hybrid and multicloud environments. Platform-as-a-service (PaaS) adoption followed closely, with 58% of the organizations pursuing PaaS strategies to streamline development processes.

Infrastructure abstraction represents a more sophisticated modernization approach, with 42% of the organizations working to reduce complexity by abstracting underlying infrastructure concerns from development teams. Nearly a third (32%) have undertaken the significant effort of refactoring applications into microservices architectures.

Platforms are imperative

also highlight the growing importance of platform engineering teams and dedicated AI platforms in successful enterprise AI strategies, with 53% of the survey respondents describing such teams as “very important” to accelerating AI implementation.

Similarly, nearly half (48%) of the respondents identified structured AI platforms as “essential” to their operations, and an additional 34% described such platforms as “important.” This recognition has translated into concrete investment decisions, with 70% of the organizations either purchasing or building platforms specifically designed for AI application delivery.

“You have to look at what you’re trying to do,” said a VP of IT at a U.S. retail giant. “If you have an organization that’s using more modernized applications, then a platform is better, because you’re already in that ecosystem and you can build out using the technologies that you already have in place.”

The platform approach addresses several of the most significant barriers to AI deployment. Complexity topped the list of obstacles, at 49%, followed by security and compliance concerns and model costs, each cited by 44% of the respondents. Dedicated AI-native platforms can systematically address all three challenges through standardized deployment patterns, built-in security controls, and optimized resource utilization.

A migration is on to private-cloud PaaS

Enterprises are moving away from self-managed on-premises platforms. Currently 42% of custom applications run this way, but 76% of the surveyed organizations plan to migrate these applications within the next 12 to 24 months. The largest segment, representing 44% of the planned migrations, will move to private-cloud PaaS environments.

The drivers behind this migration reflect core enterprise concerns about security, cost, and performance. Security considerations motivate 58% of the planned migrations, demonstrating that data protection remains top of mind even as organizations seek to leverage cloud capabilities. Cost savings drive 40% of migration decisions, and concerns about scalability, flexibility, performance, and latency each influence 28% of the organizations.

This migration pattern suggests that enterprises are seeking to balance the benefits of cloud-native architectures with the control and security of private environments. Private-cloud PaaS solutions offer the standardization and automation benefits of public-cloud platforms while maintaining the governance and compliance capabilities enterprises require.

Building AI-native organizations

that successful AI adoption requires more than technology investments — it also demands organizational transformation toward AI-native operating models. This transformation builds on established patterns — including cloud-native architectures, microservices designs, and DevOps practices — but extends these concepts to encompass AI-specific requirements.

Success requires substantial up-front investment in IT modernization, with particular emphasis on developer experience improvements, platform standardization, and AI-native infrastructure. Organizations that approach AI as an isolated technology initiative, rather than as part of comprehensive modernization efforts, consistently underperform their more strategic counterparts.

Finally, an AI-native PaaS platform is a central component of deploying and scaling AI. One example is the , a pre-engineered and AI-ready private-cloud PaaS solution that enables organizations to develop, operate, and optimize mission-critical applications easily and securely.

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_{1 March 31, 2025, Gartner.com.
2 February 25, 2025, FoundryCo.com.}