F5 2025 Technology Outlook: Navigating the New Enterprise Landscape
As we enter 2025, generative AI may be dominating headlines, but it’s just one of several forces reshaping the digital enterprise. Rising costs and complexity are driving companies to repatriate key workloads, such as storage and data, from the public cloud back on-premises. While these shifts aim to enhance control and optimize resources for AI-driven initiatives, full cloud abandonment isn’t on the horizon. Enterprises will continue with a hybrid approach, balancing workloads across public cloud, on-premises, and edge environments.
However, this hybrid approach brings new challenges in securing, delivering, and operating applications across varied platforms. At F5, we are focused on emerging technologies that promise to address these complexities and transform application delivery, security, and digital operations. Here’s a look at five technologies we believe will impact enterprises most in 2025.
- WebAssembly
WebAssembly (Wasm) offers a practical solution for the hybrid multicloud era, providing the ability to run applications anywhere a Wasm runtime is available. Wasm’s benefits go beyond portability; it enhances performance and security, allowing organizations to deploy and scale applications across different environments without compromising efficiency. - Agentic AI
Agentic AI is revolutionizing enterprise operations by replacing traditional SaaS workflows with automated, AI-driven processes. By acting autonomously within predefined guidelines, Agentic AI allows enterprises to streamline operations more effectively, reducing reliance on costly, complex software and enabling greater adaptability. - Data Classification
With growing attack surfaces and evolving data regulations, real-time data classification is becoming essential. Generative AI models are advancing data classification beyond traditional rule-based systems, enabling enterprises to improve security, meet compliance standards, and manage sensitive information more effectively. - AI Gateways
AI gateways are emerging to support the unique demands of AI traffic, handling bi-directional, unstructured data flows and managing increased interactions with "good" bots. These gateways go beyond typical API functions, enabling enterprises to scale AI applications smoothly without sacrificing performance or security. - Small Language Models
Concerns around AI hallucinations and bias have accelerated the adoption of Small Language Models (SLMs) and retrieval-augmented generation (RAG). By combining retrieval systems with generative models, SLMs and RAG provide more precise, contextually accurate results, making AI-driven applications more reliable for real-time decision-making.
And, lastly, we’re looking “beyond transformers” because the technology is already pushing against boundaries that must be broken through. As the demand for efficient AI architectures grows, limitations of transformer models—like high resource consumption—are pushing innovation. New models, such as 1-bit large language models, aim to optimize AI’s accuracy while minimizing hardware demands. These innovations are paving the way for AI that’s more accessible and scalable across enterprise environments.
As we look forward to 2025, F5 remains committed to exploring these technologies, anticipating their implications, and guiding enterprises through the complexities of the evolving digital landscape.
To explore these technologies—and why we think they’re so important—in depth, you can grab the full F5 2025 Tech Outlook, which contains commentary and observations on each of these technologies by F5 experts on the subject. I hope you’ll find it as insightful to read as I did while putting it all together.