The debate surrounding artificial intelligence has shifted from *if* organizations should adopt the technology to a more complex question: why are the results so inconsistent? While new tools are being implemented, pilot programs are underway, and budgets are expanding, tangible returns on AI investments remain elusive for many. According to Cloudflare’s 2026 App Innovation Report, the disconnect often stems less from AI itself and more from the underlying state of an organization’s applications.
This report, which surveyed over 2,300 senior leaders across the Asia-Pacific (APAC), Europe, Middle East, and Africa (EMEA), and Americas regions, highlights application modernization as the key differentiator between companies reaping significant AI value and those still struggling. Organizations that are ahead of schedule in modernizing their applications are nearly three times more likely to report a clear return on their AI investments. In the APAC region, this link is even more pronounced, with 92% of leaders identifying software updates as the most critical factor in enhancing their AI capabilities.
### Modernization, Not Just Experimentation, Drives AI Returns
This finding reframes the pursuit of AI success as a foundational challenge rather than a tooling issue. AI systems are inherently reliant on rapid data access, agile architectures, and robust integration points. Legacy applications, fragmented infrastructure, and brittle workflows impede AI projects, confining them to isolated use cases. Conversely, modernized applications provide organizations the flexibility to experiment, scale, and adapt without the constant burden of rework.
The report illustrates this relationship as a self-reinforcing cycle: organizations modernize applications to better support AI initiatives, and the resulting AI insights then justify further modernization efforts. Leaders within this progressive group express significantly higher confidence in their infrastructure’s ability to support AI development, which naturally translates into decisive action. In APAC, 90% of leading organizations have already integrated AI into their existing applications, a figure substantially higher than their less-modernized counterparts. Approximately 80% of these leading organizations plan to deepen this integration further in the coming year.
This strategic shift signifies a change in perspective. Earlier phases of AI adoption were primarily characterized by testing and pilot projects. Now, the emphasis is squarely on integration. AI is no longer viewed as a standalone endeavor but as an integral component of everyday systems, impacting both internal workflows and customer-facing applications. The report indicates that leading organizations are leveraging AI to streamline internal processes, develop content-rich applications, and support revenue-generating activities. In contrast, lagging organizations maintain a more cautious and fragmented approach.
### The Cost of Delay Manifests in Security and Confidence
The repercussions of falling behind are becoming increasingly evident. Organizations that delay modernization often find themselves modernizing reactively, typically in response to a security incident or operational failure. In APAC, these organizations report lower confidence in both their IT infrastructure and their teams’ capacity to support AI. This deficit in confidence contributes to slower decision-making and restricts the scope and ambition of AI projects. Instead of expanding use cases, teams are often occupied with risk management, addressing security gaps, and mitigating technical debt.
Security plays a pivotal role in this dynamic. The report reveals that organizations with strong synergy between their security and application development teams are considerably more successful in scaling AI initiatives. Where this alignment is weak, security concerns consume valuable time and attention, pushing modernization and AI development further down the priority list. Many organizations lagging in modernization report challenges in tracking risks within applications and APIs, which inherently slows their progress and increases their exposure to potential threats.
For forward-thinking leaders, security is embedded into the application design process from the outset, rather than being an afterthought. This proactive approach minimizes the need for reactive measures following incidents and allows teams to concentrate on building and enhancing systems. Over time, this also reduces the operational overhead that can hinder AI progress. The report suggests that system reliability has emerged as a practical constraint on speed; organizations unable to maintain stable, secure systems find it difficult to transition AI projects into production environments.
### Streamlined Tools, Clearer Foundations, Faster AI Integration
Another critical pressure point highlighted by the APAC data is tool sprawl. While nearly all organizations acknowledge challenges in managing extensive and complex technology stacks, leading organizations are responding more decisively. Approximately 86% of APAC leaders report actively consolidating redundant tools and addressing the issue of shadow IT. The objective extends beyond mere cost control to fostering clarity. A reduced number of platforms and integrations simplifies application modernization, enables consistent security enforcement, and facilitates seamless AI integration.
Developer productivity is also a significant factor. In organizations with a modernized foundation, developers can dedicate more time to maintaining and enhancing functional systems. Conversely, in lagging organizations, developers are more likely to be engaged in rebuilding from scratch or spending time on configuration and remediation tasks. This disparity directly impacts the speed at which new AI capabilities can be introduced and refined. When development teams are preoccupied with problem-solving, the prioritization of AI initiatives becomes more challenging.
Collectively, these findings suggest that achieving AI success is less about a race to deploy new models and more about systematically removing the obstacles that impede overall progress. Application modernization creates the necessary environment for AI to deliver tangible value, whereas fragmented systems and reactive practices impose limitations on AI’s potential. Without this robust foundation, organizations face greater difficulty in translating AI investments into measurable outcomes.
For organizations in the APAC region, the message is clear: AI investment without concurrent modernization efforts tends to yield superficial results. Similarly, modernization without well-defined integration plans risks becoming an endless cycle of rebuilding. The organizations that are realizing the strongest returns are those that approach application updates, security alignment, and AI integration as interconnected strategic imperatives, rather than disparate initiatives.
While the report does not prescribe a single, universal pathway forward, it clearly delineates the advantages held by organizations that act proactively compared to those that delay. The competitive edge now lies not merely in possessing AI capabilities, but in having applications that are adequately prepared to leverage them.
Original article, Author: Samuel Thompson. If you wish to reprint this article, please indicate the source:https://aicnbc.com/16551.html
