Trend 2: Autonomous, agentic AI platforms reshape enterprise operations

Agentic AI platforms represent a significant evolution from simple automation to AI agents that autonomously reason, plan, and execute actions to achieve specific goals, bridging traditional rule-based automation with adaptable, decision-making AI capabilities.

A global footwear conglomerate, in collaboration with Infosys, co-created an agentic solution to address inventory discrepancies. The staged implementation helped the client transition from a monolithic architecture to one built on poly cloud and poly AI principles of modularity, event-driven design, and seamless integration.

Trend 3: GPU-as-service emerges as the new infrastructure model

As AI adoption matures, organizations — especially in regulated industries — are building on-premises GPU-as-a-service (GPUaaS) platforms for training and inference.

A leading European telecom provider is developing a sovereign GPUaaS platform that delivers secure, high-performance GPU infrastructure to its clients. This initiative has enabled the company to support large-scale model training while eliminating the need for hardware ownership and management.

Trend 4: Alternate hardware drives cost-efficient AI interence

There is increasing demand for ROI-positive, power-efficient compute options for generative AI applications, particularly in regulated environments and lower-throughput use cases.

Recent innovations include Intel Xeon Gen 6 with performance cores (P-cores) and AMX instruction sets and AMD EPYC 5th Gen with AVX-512, DDR5 memory, and PCIe Gen 5 I/O. These CPUs make it feasible to run small- and medium-sized models efficiently, reducing total cost of ownership while broadening deployment options.

Trend 5: Smaller language models gain relevance

While LLMs continue to dominate, small language models (SLMs) are gaining traction as lightweight, cost-sensitive, and domain-specific alternatives.

Using techniques such as pruning, quantization, and knowledge distillation, SLMs deliver strong NLP performance with lower latency, faster inference, and reduced computational overhead. Their compact size enables their deployment on edge devices and embedded systems, extending AI to smartphones, IoT, and other hardware platforms.

Easier to train, fine-tune, and debug, SLMs accelerate prototyping and development, making them a practical choice for enterprises seeking efficient, accessible, and scalable AI.