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For most organisations, the AI conversation has shifted. The question is no longer whether to invest in artificial intelligence, but why the outcomes feel so inconsistent. Budgets are growing, pilots are underway, and new tools are everywhere — yet meaningful AI returns still feel out of reach for many teams. According to Cloudflare’s 2026 App Innovation Report, the issue often isn’t the AI itself. It’s the condition of the applications supporting it.
Based on insights from more than 2,300 senior leaders across APAC, EMEA, and the Americas, the report highlights a clear pattern: application modernisation is the biggest factor separating organisations that see real AI value from those that don’t. In fact, Cloudflare says modernising applications can triple the chances of seeing returns from AI, making it less of a technology race and more of a foundation challenge.
Modern Applications Make AI Pay Off
The data shows a striking divide. Organisations that are ahead of schedule in updating their applications are almost three times more likely to report tangible benefits from their AI investments. In the APAC region, the link is even stronger — 92% of leaders say modernising software was the single most important step in improving their AI capabilities.
This reframes AI success as an infrastructure issue, not a tooling problem. AI relies on fast data access, flexible architectures, and systems that can integrate smoothly. Older applications, siloed systems, and fragile workflows make it difficult for AI projects to scale beyond isolated experiments. Modern platforms, on the other hand, give teams the flexibility to test, deploy, and adapt without constantly rebuilding what already exists.
From AI Experiments to Everyday Integration
Earlier waves of AI adoption focused heavily on pilots and proof-of-concept projects. That mindset is changing. The report shows that leading organisations are now prioritising integration over experimentation, treating AI as part of everyday operations rather than a standalone initiative.
This shift creates a reinforcing cycle. Companies modernise their applications to better support AI, then use early AI successes to justify further modernisation. Leaders report much higher confidence in their infrastructure’s ability to support AI development — and that confidence leads to faster action. In APAC, 90% of organisations ahead of schedule have already embedded AI into existing applications, and nearly 80% plan to expand that integration further over the next year.
AI is increasingly being used to improve internal workflows, power content-driven applications, and support revenue-generating activities. Organisations that lag behind tend to move more cautiously, keeping AI efforts fragmented and limited in scope.
The Hidden Cost of Falling Behind
Delaying modernisation comes with growing risks. Organisations that trail in application updates often act reactively, upgrading systems only after security incidents or operational failures. In APAC, these teams report lower confidence in both their infrastructure and their people’s ability to support AI initiatives.
That lack of confidence slows decisions and restricts how far AI projects can go. Instead of expanding use cases, teams spend time managing risk, patching vulnerabilities, and dealing with technical debt. Over time, this reactive approach becomes a bottleneck that prevents AI from reaching production at scale.
Security Alignment Shapes AI Speed
Security plays a crucial role in this dynamic. The report finds that organisations where application and security teams are closely aligned are far more successful at scaling AI. When that alignment is weak, security issues consume attention and push AI and modernisation further down the priority list.
Leading organisations treat security as part of application design, not as an afterthought. This reduces the need for emergency fixes and allows teams to focus on building reliable systems. Stability and trust in infrastructure have become practical limits on how quickly AI can move from idea to impact.
Cutting Tool Sprawl to Move Faster
Another challenge highlighted in the APAC data is technology sprawl. Managing sprawling stacks of overlapping tools makes modernisation harder and slows AI integration. About 86% of APAC leaders say they are actively reducing redundant tools and tackling shadow IT.
The goal goes beyond saving money. Simpler stacks make it easier to modernise applications, enforce consistent security controls, and integrate AI without friction. They also free up developer time. In modernised environments, developers spend more energy improving existing systems. In lagging organisations, they’re often stuck rebuilding or troubleshooting — leaving less capacity for AI innovation.
AI Success Starts With Strong Foundations
Taken together, the findings suggest that AI success isn’t about deploying the newest models first. It’s about removing the obstacles that slow everything else down. Modern applications create the conditions for AI to deliver value, while fragmented systems and reactive practices limit what’s possible.
For organisations in APAC and beyond, the message is clear: investing in AI without modernising applications leads to shallow results. Modernising without a plan to integrate AI risks becoming an endless rebuild. The strongest outcomes come from treating application updates, security alignment, and AI integration as connected efforts — not separate initiatives.
