F5 2025 Report reveals talk becomes action as AI gets to work
F5 2025 Report reveals talk becomes action as AI gets to work F5 Report highlights AI-driven transformation Amid operational complexity. 96% of surveyed IT decision-makers have deployed AI models, up from a quarter in 2023 IT leaders are increasingly trusting AI with business critical tasks from traffic management to cost optimization, according to the industry’s most comprehensive report on application strategy. F5’s 2025 State of Application Strategy Report, which surveys global IT decision makers, found that 96% of organizations are now deploying AI models, up from a quarter in 2023. There is also a growing willingness to elevate AI to the heart of business operations. Almost three-quarters of respondents (72%) said they want to use AI to optimize app performance, whereas 59% support the use of AI for both cost-optimization and to inject security rules, automatically mitigating zero-day vulnerabilities. There is also a growing willingness to elevate AI to the heart of business operations. Almost three-quarters of respondents (72%) said they want to use AI to optimize app performance, whereas 59% support the use of AI for both cost-optimization and to inject security rules, automatically mitigating zero-day vulnerabilities. Today, half of organizations are using AI gateways to connect applications to AI tools, and another 40% expect to be doing so in the next 12 months. Most are using this technology to protect and manage AI models (62%), provide a central point of control (55%), and to protect their company from sensitive data leaks (55%). “This year’s SOAS Report shows that IT decision makers are becoming confident about embedding AI into ops,” said Lori MacVittie, F5 Distinguished Engineer. “We are fast moving to a point where AI will be trusted to operate autonomously at the heart of an organization, generating and deploying code that helps to cut costs, boost efficiency, and mitigate security problems. That is what we mean when we talk about AIOps, and it is now becoming a reality.” Operational readiness and API challenges remain Despite growing AI confidence, the SOAS Report highlights several enduring challenges. For organizations currently deploying AI models, the number one concern is AI model security. And, while AI tools are more autonomous than ever, operational readiness gaps still exist. 60% of organizations feel bogged down by manual workflows, and 54% claim skill shortages are barriers to AI development. Furthermore, almost half (48%) identified the cost of building and operating AI workloads as a problem, up from 42% last year. A greater proportion of organizations also said that they have not established a scalable data practice (39% vs. 33% in 2024) and that they do not trust AI outputs due to potential bias or hallucinations (34% vs. 27%). However, fewer complained about the quality of their data (48%, down from 56% last year). APIs were another concern. 58% reported they have become a pain point, and some organizations spend as much as half of their time managing complex configurations involving numerous APIs and languages. Working with vendor APIs (31%), custom scripting (29%), and integrating with ticketing and management systems (23%) were flagged as the most time-consuming automation-related tasks. “Organizations need to focus on the simplification and standardization of operations, including streamlining APIs, technologies, and tasks,” said MacVittie. “They should also recognize that AI systems are themselves well-suited to handle complexity autonomously by generating and deploying policies or solving workflow issues. Operational simplicity is not just something on which AI is going to rely, but which it will itself help to deliver.” Hybrid app deployments prevail Allied to soaring AI appetites is a greater reliance on hybrid cloud architectures. According to the SOAS Report, 94% of organizations are deploying applications across multiple environments—including public clouds, private clouds, on-premises data centers, edge computing, and colocation facilities—to meet varied scalability, cost, and compliance requirements. Consequently, most decision makers see hybrid environments as critical to their operational flexibility. 91% cited adaptability to fluctuating business needs as the top benefit of adopting multiple clouds, followed by improved app resiliency (68%) and cost efficiencies (59%). A hybrid approach is also reflected in deployment strategies for AI workloads, with 51% planning to use models across both cloud and on-premises environments for the foreseeable future. Significantly, 79% of organizations recently repatriated at least one application from the public cloud back to an on-premises or colocation environment, citing cost control, security concerns, and predictability. This marks a dramatic rise from 13% just four years ago, further underscoring the importance of preserving flexibility beyond public cloud reliance. Still, the hybrid model can prove a headache for some. Inconsistent delivery policies (reported by 53% of respondents) and fragmented security strategies (47%) are all top of mind in this respect. “While spreading applications across different environments and cloud providers can bring challenges, the benefits of being cloud-agnostic are too great to ignore. It has never been clearer that the hybrid approach to app deployment is here to stay,” said Cindy Borovick, Director of Market and Competitive Intelligence, F5. APCJ AI adoption and challenges – key highlights: AI Gateways on the Rise: Nearly half of APCJ organizations (49%) are already using AI gateways to connect applications to AI tools, with another 46% planning to do so in the next 12 months. Top Use Cases for AI Gateways: Among those leveraging AI gateways, the most common applications include protecting and managing AI models (66%), preventing sensitive data leaks (61%), and observing AI traffic and application demand (61%). Data and Trust Challenges: Over half (53%) struggle with immature data quality, and 45% are deterred by the high costs of building and running AI workloads. Hybrid Complexity: The hybrid model of AI deployment introduces hurdles, with 79% citing inconsistent security policies, 59% highlighting delivery inconsistencies, and 16% dealing with operational difficulties. Toward a programmable, AI-driven future Looking ahead, the SOAS Report suggests that organizations aiming to unlock AI’s full potential should focus on creating programmable IT environments that standardize and automate app delivery and security policies. By 2026, AI is expected to move from isolated tasks
