Tech Trend #3: AI inference is reshaping enterprise compute strategies
As artificial intelligence (AI) moves from experimental pilots to production-scale deployment, enterprises are discovering that their existing computing infrastructure is reaching its limits in supporting AI-driven operations. According to Deloitte Tech Trends 2026, the rising cost, scalability requirements, and latency challenges of AI inference are prompting organizations to fundamentally reconsider where and how their AI workloads should operate.
Inference economics expose infrastructure gaps
The rapid evolution of generative AI has accelerated business innovation across industries, but it has also exposed a critical infrastructure challenge. While many organizations initially relied on cloud-based services to experiment with AI, the continuous and high-volume nature of AI inference is placing unprecedented strain on existing computing strategies. Frequent API calls, rising usage intensity and always-on AI applications are driving significant and often unpredictable cost escalation.
Beyond cost pressures, enterprises must also navigate data sovereignty requirements, latency constraints, intellectual property protection and system resilience. According to Deloitte Insights, the answer is not a binary choice between cloud and on-premises infrastructure, but a more deliberate, workload-driven hybrid approach that aligns technical requirements with business priorities.
Organizations that act now to modernize infrastructure and build workforce capabilities are better positioned to shape the next phase of enterprise computing. Advances in specialized chipsets, high-speed networking and intelligent workload orchestration are becoming foundational elements for operating AI at scale.
Hybrid computing becomes a strategic imperative
For many enterprises, the operational expense of AI has become a catalyst for significant change. Some organizations are already facing monthly AI compute costs in the tens of millions, especially as agentic AI systems move into production. At the same time, regulatory expectations around data residency, the need for ultra-low latency in real-time use cases such as manufacturing or autonomous systems and resilience requirements for mission-critical applications are reshaping infrastructure decisions.
Intellectual property protection is another critical consideration. A significant share of the organization’s highly sensitive data remains on-premises, making leaders cautious about exposing it to external AI services. Together, these pressures are driving significant global investment in new data center capacity.
Leading organizations are responding by adopting a three-tier hybrid model: public cloud for elastic training workloads and experimentation, private infrastructure for predictable, high-volume inference and edge computing for time-critical decision-making. This approach moves the conversation beyond the traditional cloud-versus-on-premises debate.
“Cloud makes sense for certain things. It’s like the ‘easy button’ for AI. But it’s really about picking the right tool for the job,” Dimitar Dimitrov, Senior Manager Technology Strategy Transformation. “Companies are building heterogeneous platforms, choosing environments that deliver optimal cost efficiency.”
From legacy infrastructure to AI-first environments
Many enterprise data centers were designed for traditional IT workloads and are fundamentally misaligned with AI’s technical requirements. AI systems require specialized processors, advanced networking and significantly different cooling and power architectures, making retrofitting complex and costly.
“The infrastructure many enterprises have today was designed for the pre-AI era,” said Aleksandar Ganchev, Director Technology Strategy Transformation. “No enterprise could reasonably have been expected to have designed their architecture for something that didn’t exist at the time. Very quickly, most infrastructure capacity will be dedicated to AI systems rather than traditional workloads.”
This shift is accelerating the emergence of so-called “AI factories” – purpose-built environments that integrate AI-optimized hardware, high-performance networking, data pipelines and unified orchestration platforms. These environments are designed to support multimodal AI workloads efficiently, reduce architectural risk and enable faster deployment at scale.
Workforce transformation and sustainability become core focus areas
The infrastructure transformation required to support AI at scale also demands significant workforce reskilling. IT teams must evolve from managing traditional servers to operating GPU clusters, high-bandwidth networks and advanced cooling systems. Network architects need to design for AI-specific traffic patterns, while cost engineers must develop expertise in
hybrid compute portfolio optimization and inference economics.
Sustainability is becoming an equally important consideration. Innovations in thermal management, advanced cooling and energy-efficient server design are improving performance per watt, while the shift of certain AI workloads to client devices such as AI-enabled PCs may help reduce overall carbon impact.
As AI becomes central to enterprise strategy, computing architecture is increasingly a board-level priority. Organizations that proactively align infrastructure, talent and sustainability goals around AI-first principles are well positioned to achieve а durable competitive advantage in the decade ahead.
For more insights on how enterprises are rethinking compute infrastructure to meet AI demand at scale, read the full Deloitte Tech Trends article: The AI infrastructure reckoning: Optimizing compute strategy in the age of inference economics.
