Artificial intelligence has raced into its operational decade. As enterprises move from generative experimentation to agentic execution, the leaders will be those who can run AI actions securely, economically, and responsibly across device, edge, data center, and cloud.
We are moving beyond generative AI experimentation into the era of agentic AI—agents that can reason, plan, orchestrate tools, and autonomously execute multi-step objectives across enterprise environments, using AI tokens at scale and demanding AI inferencing everywhere: from AI PCs, and workstations, to edge, data center and the cloud. Autonomy does not mean that all decisions or actions occur without human involvement; it means there is a clear, governed, and secure path with defined points for human intervention. In practice, agentic AI is designed to work alongside people—accelerating routine execution while preserving human judgment in situations that demand empathy, context, and accountability. Delivering deployment flexibility for agentic AI is the promise of Lenovo Hybrid AI Advantage™ with NVIDIA.
Due to latency, security, compliance, privacy, data, and AI sovereignty requirements, 84% of organizations plan to leverage on-premises or edge deployments for AI workloads alongside cloud environments.1
This is not incremental progress. It is an architectural change.
The focus on agents is intensifying, increasing more than 50% year over year. Yet only 21% of organizations have deployed agentic AI at scale.2 Most remain in exploration or pilot phases, highlighting the gap between ambition and operational readiness.
From Generative AI to Agentic AI
Unlike generative AI, agentic AI demands deeper process redesign to realize value. Workflows must be standardized, monitored, and in some cases restructured entirely—making proactive process governance and cross-functional collaboration imperative.
As Lenovo CTO Tolga Kurtoglu outlined in “Beyond agentic: Leading the next phase of orchestrated AI transformation,” the next phase of enterprise AI will be defined by orchestration across intelligent systems. Delivering that orchestration in production environments now depends on distributed inference architectures engineered for execution economics as much as decision intelligence.3
Generative AI creates outputs. Agentic AI drives outcomes.
Agentic AI decomposes objectives into sub-tasks, retrieves data, invokes tools, evaluates results, and iterates within defined governance boundaries. AI is shifting from generation to execution—and execution requires distributed infrastructure built for production, not demos.
The Inference Inflection Point
Training built the AI era. Inference will scale it.
As AI systems evolve from one-off interactions to persistent, agent-driven execution, inference becomes the dominant driver of cost and performance. In token-based AI models, each reasoning loop, tool invocation, and validation step consumes tokens—turning inference into a compounding economic factor rather than a marginal cost.
Lenovo and Futurum’s Achieve Better Economics and Performance Through Hybrid AI reinforces that sustaining agentic AI at scale depends on where inference runs. Placing inference closer to data sources reduces unnecessary token consumption, limits latency accumulation across multi-step workflows, and avoids escalating costs associated with uncontrolled cloud execution—making hybrid AI essential to viable enterprise token economics in production environments.4
Key metrics that now define enterprise AI strategy:
- Time to First Token (TTFT): In multi-step workflows, latency compounds across chains of reasoning.
- Cost per token: As agentic AI scales, inference events multiply. Without optimized workload placement, cost per action becomes unsustainable.
Lenovo’s Tech World @ CES 2026 announcement of inferencing-optimized servers with NVIDIA and Hybrid AI Factory Services reinforces the operational reality enterprises face: AI value from agentic AI is realized in production, at inference time, across hybrid enterprise environments.
Lenovo Hybrid AI Advantage with NVIDIA: Industrializing Agentic AI with AI Factories
Agentic AI does not succeed on infrastructure alone. Enterprises need repeatability: validated foundations, clear workload placement, and continuous optimization.
Lenovo’s Hybrid AI Factory with NVIDIA brings together infrastructure, AI-PCs, AI workstations, devices, data, models, and agent platforms into validated, production-ready foundations. These AI factory models align with industry reference architectures, including those developed with NVIDIA, enabling agentic systems to be deployed quickly, operated reliably, and optimized continuously at scale.
This full-stack approach reflects a core reality of agentic reasoning and Mixture-of-Experts (MoE) models, wherein performance and efficiency are determined by how compute, memory, networking, and software work together. By designing the entire system as one, NVIDIA’s Vera Rubin enables efficient communication, and scalable execution—driving higher utilization, better efficiency, and lower cost per token for reasoning at scale.
Powering agentic AI future, Lenovo’s expanded portfolio at NVDIA GTC features production ready data-center-scale AI, delivered through pre-validated system designs integrated with NVIDIA AI Enterprise software and services, including:
- Two Lenovo Hybrid AI platforms: one featuring NVIDIA RTX™ PRO 6000 GPUs for scale-out AI and multi-modal inferencing, and another powered by NVIDIA B300 – among the first NVIDIA design review board–certified systems for enterprise AI training.
- Lenovo Hybrid AI inferencing starter platform with NVIDIA RTX PRO 4500 Blackwell, delivering up to 3X performance gains for video and data processing and 4X better performance for content generation compared to NVIDIA L4 for single-node deployments.
- Lenovo AI Cloud Gigafactory at gigawatt-scale with next-generation NVIDIA Rubin platforms, accelerating deployment for hyperscale and sovereign AI cloud providers. As a launch partner for NVIDIA Vera Rubin NVL72, Lenovo delivers fully liquid-cooled, rack-scale AI systems engineered for faster deployment and dramatically improved token economics—achieving up to 10x higher throughput and up to 10x lower cost per token compared to previous generations.
To accelerate adoption into these data‑center platforms, Lenovo offers a clear on‑ramp from development to production. AI‑ready workstations—led by ThinkStation P5 Gen 2 and ThinkStation PGX—let teams prototype and validate inference locally, then scale seamlessly into Lenovo Hybrid AI infrastructure. Mobile workstations extend this entry point without shifting the center of gravity away from the data center.
Backed by Lenovo Hybrid AI Factory Services, these platforms combine lifecycle management, global deployment expertise, and operational optimization—helping AI cloud providers move from build-out to revenue generation faster and with lower risk
Looking Ahead: NVIDIA GTC March 2026
The next chapter of enterprise AI will be judged on operational outcomes delivered through agentic AI: time-to-value, inference economics, and governance at scale. As enterprises move from experimental agents toward persistent, production-grade AI systems, the infrastructure demands of agentic AI will continue to evolve.
1 IDC CIO Playbook 2026 Survey, commissioned by Lenovo
2 The State of AI: Global Survey 2025 | McKinsey
3 Lenovo StoryHub – Beyond agentic: Leading the next phase of orchestrated AI transformation
4 Achieve Better Economics and Performance Through Hybrid AI, The Futurum Group for Lenovo