AI Success Hinges on IT Modernization: New Study Reveals Critical Link
Archyde Staff
| august 16, 2025, 10:00 AM EST
Breaking news: A landmark study of over 250 senior IT leaders in multi-billion-dollar companies reveals a stark division in enterprise AI adoption. Organizations intensely modernizing their IT infrastructure and pursuing multiple AI projects together are experiencing dramatically higher success rates, driving notable revenue growth, productivity gains, and enhanced customer engagement.
Wiht 71% of global organizations actively deploying artificial intelligence (AI) at scale,this research underscores a critical factor for unlocking AI’s full potential: robust and modern IT foundations. Worldwide generative AI (genAI) spending is projected to skyrocket to $644 billion in 2025, a testament to AI’s growing importance, according to Gartner data from March 2025. This surge in investment mirrors a significant increase in dedicated AI project budgets, nearly doubling from 26% in 2023 to almost half of organizations in 2024.
The ‘Heavy Investor’ Advantage in AI
The survey data, compiled by Foundry in February 2025, highlights a clear correlation between IT modernization efforts and AI success. Enterprises classified as “heavy investors”-those undertaking four or more significant IT modernizations-consistently outperform their less modernized counterparts. These leading organizations report 89% improved IT efficiency, compared to 61% for others. Their AI adoption speed is also significantly faster at 85% versus 60%.
Perhaps most compellingly, 98% of heavy investors witnessed increased innovation, a stark contrast to the 75% reported by other companies. The ability to accelerate time-to-market capabilities shows an even greater disparity, with heavy investors achieving 87% success rates versus a mere 32% for others. This indicates that IT modernization is not just about technological upgrades but directly translates into core business advantages and a competitive edge.
Key Differentiators for AI Leaders
| Metric | Heavy Investors | All Other Organizations |
|---|---|---|
| IT Efficiency Improvement | 89% | 61% |
| AI adoption Speed | 85% | 60% |
| Increased Innovation | 98% | 75% |
| Time-to-Market Acceleration | 87% | 32% |
These “heavy investors” also express greater confidence in their infrastructure’s ability to support AI, with 48% showing strong confidence compared to 33% of their peers. This confidence fuels more aggressive AI deployment, with 72% actively modifying AI applications in production, versus 41% of others.
Building on a Foundation of Modern Practices
Prosperous AI integration is built upon a bedrock of prior digital transformations. Organizations leading the charge have prioritized enhancing the developer experience, with 71% investing in automation to boost productivity. This focus on empowering developers acknowledges their central role in successful technology deployment,even as AI automates routine tasks.
Platform standardization is another critical area. Sixty-six percent of surveyed companies are working to gain visibility across diverse IT environments, tackling the complexity inherent in hybrid and multicloud setups. Platform-as-a-service (PaaS) adoption is also on the rise, with 58% pursuing PaaS strategies to streamline development.
More advanced modernization includes infrastructure abstraction, with 42% of organizations simplifying by shielding development teams from underlying infrastructure intricacies. A significant third (32%) are even refactoring applications into microservices architectures, a move towards greater agility and scalability.
Platforms: The Imperative for AI Success
The emergence of dedicated AI platforms and the teams that manage them is proving crucial. Over half (53%) of respondents deem platform engineering teams “very significant” for accelerating AI implementation. Similarly, 48% find structured AI platforms “essential,” with another 34% calling them “critically importent.”
This recognition has driven investment, with 70% of organizations either acquiring or building platforms specifically for AI application delivery. “If you have an organization that’s using more modernized applications, than a platform is better,” stated a VP of IT at a leading U.S. retail company. “You’re already in that ecosystem and you can build out using the technologies that you already have in place.”
Dedicated AI platforms are instrumental in overcoming deployment hurdles like complexity (cited by 49%), security and compliance (44%), and model costs (44%).These platforms offer standardized deployment patterns, built-in security, and optimized resource use.
The Shift Toward Private-cloud PaaS
A notable trend is the migration away from self-managed on-premises platforms. While 42% of custom applications currently run on such systems, a substantial 76% of organizations plan to migrate them within the next 1-2 years. The largest group, at 44%, intends to move to private-cloud PaaS environments. This shift is motivated by security (58%), cost savings (40%), and the need for greater scalability, flexibility, performance, and lower latency (each cited by 28%).
This pattern indicates a strategic balance between leveraging cloud-native benefits and maintaining the control and security of private environments.private-cloud PaaS solutions provide the standardization and automation of public clouds while adhering to strict governance and compliance requirements.
Forging AI-Native Organizations
Achieving AI success is not solely about technological investment but also organizational transformation. Companies are evolving towards AI-native operating models, building upon cloud-native architectures, microservices, and DevOps practices with AI-specific enhancements. This requires upfront investment in IT modernization, focusing on developer experience, platform standardization, and AI-ready infrastructure.
Organizations that treat AI as a standalone initiative, rather than integrating it into broader modernization strategies, consistently lag behind. A robust AI-native PaaS platform, such as VMware’s Tanzu, plays a pivotal role in effectively deploying and scaling AI initiatives, offering a pre-engineered, AI-ready private-cloud PaaS solution.
As we navigate the AI revolution, are organizations adequately investing in the foundational IT modernization needed to truly harness its power? What steps can your business take today to ensure its infrastructure is ready for the AI-driven future?
Evergreen Insights: The Enduring Importance of IT Modernization for AI
The insights from this 2025 study on enterprise AI adoption remain highly relevant. The core principle-that substantial IT modernization is a prerequisite for effective AI deployment-is a timeless truth in the technology landscape. Companies that view AI as an isolated project risk plateauing their growth, while those integrating it with comprehensive infrastructure upgrades, embracing cloud-native principles, and prioritizing developer enablement are poised for sustained innovation and competitive advantage. The strategic shift towards private-cloud PaaS also highlights an enduring need for balancing flexibility with control, a key consideration for any forward-thinking enterprise IT strategy.
Frequently Asked questions About Enterprise AI and Modernization
- What is the primary driver for successful enterprise AI adoption according to recent findings?
- The primary driver for successful enterprise AI adoption is significant investment in IT modernization and the simultaneous pursuit of multiple AI projects.
- How does IT modernization impact AI implementation speed?
- Organizations with heavy IT modernization efforts report faster AI adoption rates, with 85% of these companies experiencing quicker implementation compared to 60% of others.
- What role do AI platforms play in enterprise AI strategies?
- Dedicated AI platforms are considered essential or important by nearly 82% of organizations, playing a critical role in accelerating AI implementation and overcoming deployment challenges.
- What is the projected global spending on generative AI (genAI) for 2025?
- Global spending on generative AI (genAI) is expected to reach $644 billion in 2025, reflecting a substantial increase in AI investment.
- What is the trend regarding on-premises platforms versus cloud solutions for custom applications?
- There is a significant trend towards migrating custom applications away from self-managed on-premises platforms, with 76% of organizations planning such migrations, predominantly to private-cloud PaaS environments.
- What are the main obstacles to AI deployment that dedicated platforms can address?
- Dedicated AI platforms can address key obstacles to AI deployment including complexity, security and compliance concerns, and model costs.
What are your thoughts on the link between IT infrastructure and AI success? Share your insights in the comments below!
