For many organisations, the AI debate has moved on from whether to adopt the technology to a harder question: why do the results feel uneven? New tools are in place, pilots are running, and budgets are rising, yet clear AI returns remain elusive. According to Cloudflare’s 2026 App Innovation Report, the difference often has less to do with AI itself and more to do with the state of the applications underneath it.
The report, based on a survey of more than 2,300 senior leaders in APAC, EMEA, and the Americas, points to application modernisation as the clearest divider between organisations seeing real AI value and those still struggling. Companies that are ahead of schedule in modernising their applications are nearly three times more likely to report a clear payoff from their AI investments. In APAC, the link is even more explicit: 92% of leaders say updating their software was the single most important factor in improving their AI abilities.
Modernisation, not experimentation, drives AI returns
The finding re-frames AI success as a foundation problem not a tooling problem. AI systems depend on fast access to data, flexible architectures, and reliable integration points. Legacy applications, fragmented infrastructure, and brittle workflows make it harder for AI projects to move beyond isolated use cases. Modernised applications, by contrast, give organisations room to experiment, scale, and adapt without constant rework.
The report describes this relationship as a reinforcing cycle. Organisations modernise applications to support AI, then use AI results to justify deeper modernisation. Leaders in this group report far higher confidence that their infrastructure can support AI development, and that confidence translates into action. In APAC, 90% of leading organisations have already integrated AI into existing applications, compared with much lower levels among those behind schedule. Around 80% plan to increase that integration further over the next year.
The shift marks a change in mindset, as earlier waves of AI adoption focused on testing and pilots. Now, the emphasis is on integration. AI is not treated as a standalone project but as part of everyday systems, from internal workflows to customer-facing applications. The report shows that leading organisations are using AI to improve internal processes, build content-driven applications, and support revenue-generating work, while lagging organisations remain more cautious and fragmented in their approach.
The cost of delay shows up in security and confidence
The cost of falling behind is becoming clearer as well. Organisations that lag on modernisation tend to modernise reactively, often after a security incident or operational failure. In APAC, these organisations report lower confidence in both their infrastructure and their teams’ ability to support AI. That lack of confidence slows decision-making and limits how far AI projects can go. Instead of expanding use cases, teams spend time…
Source link
Disclaimer
We strive to uphold the highest ethical standards in all of our reporting and coverage. We blogs.grocliq.com want to be transparent with our readers about any potential conflicts of interest that may arise in our work. It’s possible that some of the investors we feature may have connections to other businesses, including competitors or companies we write about. However, we want to assure our readers that this will not have any impact on the integrity or impartiality of our reporting. We are committed to delivering accurate, unbiased news and information to our audience, and we will continue to uphold our ethics and principles in all of our work. Thank you for your trust and support.
Website Upgradation is going on for any glitch kindly connect at [email protected]
