F5 Report 2026: AI inferencing has arrived, complicating an already complex IT landscape

Kunal AnandChief Product Officer | F5

AI inference has crossed from experiment to production. According to the F5 2026 State of Application Strategy Report, 78% of enterprises now run AI inference in-house, and the average organization has seven AI models in production or active evaluation. The age of speculative AI investment is over.

The age of operational AI is here, and it is about to break what has worked reliably for a decade.

AI inference is now the dominant AI activity for companies

The same report shows that for 77% of organizations, inference, not model training or tuning, is the dominant AI activity. That number reflects an underappreciated shift in where AI value actually lives.

Training is the headline. Inference is the bill. Inference is where a model takes real input from a real customer and produces output that real systems then act on. It is where ROI gets booked, where errors get noticed, and where every choice about latency, accuracy, and cost compounds against revenue. The organizations seeing measurable returns from AI today are not the ones with the most models. They are the ones operating their inference well.

That is also why this finding from the report matters: 88% of organizations report architectural, organizational, or security challenges operationalizing AI insights into day-to-day workflows and decision-making. The benefits of inference are real. So is the operating cost of getting it wrong.

AI Inference is quickly becoming a distributed system

It also matters that these organizations are not running one model. They are running an average of seven, and the reasons mirror the rationale for hybrid multicloud. Cost arbitrage between providers. Availability across regions. Sovereignty and regulatory boundaries. The simple fact is that no single model is best at every task. A reasoning model for hard analysis. A small model for high-volume classification. A domain-tuned model for the regulated workflow. A cheaper model for the long tail.

Multi-model is not a transitional architecture. It is the steady state, because the business and technical pressures that produced it are not going away.

The architectural consequence is that inference stops looking like an API call and starts looking like a fleet. It inherits every characteristic of a distributed system: latency budgets that blow up under load, partial failures that don't surface until a customer takes a screenshot, costs that compound in places where no one has dashboards, and a security perimeter that now includes every model endpoint a developer can spin up.

This is where the report's most uncomfortable number lands. Only 28% of respondents report streamlining developer workflows through a single AI management point. The other 72% are operating distributed inference without a single point of control. Inference has become business-critical, and most enterprises run it across fragmented control surfaces.

A new level of complexity for enterprises

What does this mean for digital leaders? It means the next wave of competitive advantage is operational, not algorithmic.

The use cases are concrete and already in production. Financial services firms running real-time fraud detection. Healthcare organizations accelerating diagnosis. Manufacturers forecasting equipment failures before they occur. In each case, the model itself is not the differentiator. Most competitors can access similar models. The differentiator is the ability to run those models reliably, at the right latency, under the right governance, at a cost the business can absorb.

Which reframes the question every enterprise architecture team should be asking. The question is not, "Which model should we adopt?" It is, "How do we operate a distributed fleet of models with consistent security, reliability, and cost control across cloud, on-premises, and edge?"

The answer is not bigger models. It is better operations. Reduced latency. Agile scaling. End-to-end visibility. Strategic control over data, identity, and cost. Operational discipline applied to a workload that, until eighteen months ago, most teams had never run in production.

A unified platform for multi-model AI inference

As enterprises adopt multi-model inference, the resulting complexity will shape the outcome of their AI investments more than any single decision they make about models themselves.

Organizations that fail to consolidate the disparate tools running across their inference fleet expose themselves to compounding security, reliability, and cost risk. IDC's FutureScape 2026: Worldwide Agentic Artificial Intelligence report projects that “by 2027, companies that fail to establish high-quality, AI-ready data foundations will suffer a 15% productivity loss as generative and agentic systems falter.” That is not a small number. It is the cost of fragmentation, paid quarterly.

Companies that take the opposite path get the opposite result. By integrating delivery, security, and AI governance into a single operational model, they gain something that compounds in their favor: real-time visibility across distributed inference, control over cost and risk, and the ability to say yes to the next AI use case without inheriting another tooling stack.

The lesson from hybrid multicloud applies directly. Converged services are what made hybrid manageable at scale. The same will be true for multi-model AI inference. The companies that figure this out in the next 12 to 18 months will hold a structural advantage that is difficult to copy after the fact.

To go deeper, explore the F5 Application Delivery and Security Platform, built exactly for this problem. For the full data behind these findings, download your copy of the 2026 State of Application Strategy Report.

About the Author

Kunal AnandChief Product Officer | F5

Kunal Anand leads the F5 product organization as Chief Product Officer. Responsible for product vision, strategy, and execution, he ensures development of breakthrough solutions that solve critical challenges and create exceptional experiences for customers. In his previous role as Chief Technology and AI Officer, Kunal charted the company’s technology and AI strategy and vision. Prior to F5, Kunal held the dual role of Chief Technology Officer and Chief Information Security Officer at Imperva. His journey to Imperva began in 2018 with the acquisition of Prevoty, an application security startup he co-founded in 2013. Before joining Prevoty, he was the Director of Technology at BBC Worldwide. Kunal has a deep history of innovation and technical expertise, and has held roles leading security, data, technology, and engineering teams at Gravity, MySpace, and the NASA Jet Propulsion Lab. Kunal has over 15 years of experience in AI and machine learning, ranging from model training, employing AI-driven algorithms to enhance products, and designing and implementing AI architectures. Kunal holds a Bachelor of Science degree in computer science from Babson College.

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