In the race to operationalize AI, success depends not on flashy pilots, but on turning experimentation into measurable business value. According to David Ellison, Chief Data Scientist and Director of AI Engineering at Lenovo, the most successful AI projects start with clear business outcomes—not models. From cost savings to new revenue streams, the focus is on impact, supported by infrastructure that can scale and systems that users trust.
Enterprises today are realizing that AI must integrate into existing workflows rather than operate in isolation. Whether it’s ERP, CRM, or logistics, solutions need to connect seamlessly with the tools employees already use. For Lenovo, that means pairing cutting-edge engineering with responsible AI guardrails, so organizations can adopt new capabilities with confidence.
At the platform level, Lenovo’s strength lies in its One Lenovo strategy—a unified approach that spans devices, data centers, and the cloud. By delivering end-to-end interoperability, Lenovo simplifies customer decision-making and ensures that AI investments scale efficiently across environments.
- AI That Fits the Business: Lenovo’s AI Discovery Centers have executed hundreds of POCs, with the most successful tied to measurable business outcomes and backed by executive sponsorship.
- Efficiency at Scale: CPUs are playing a growing role in AI inferencing, offering cost-effective, energy-efficient alternatives to GPUs—especially for small and mid-sized models.
- Responsible by Design: Ellison emphasizes that responsible AI isn’t just a regulatory checkbox—it’s a business imperative. Building trust through privacy, transparency, and inclusion ensures adoption while expanding market reach.
The shift from experimentation to adoption is happening fast. Lenovo has supported projects as diverse as real-time NASCAR pit stop optimization, AI-powered sports strategy analysis, and logistics solutions for the U.S. Postal Service—all proving that CPU-based inferencing can deliver surprising scalability and speed. These real-world deployments show how AI transforms not just data, but decision-making under pressure.
Looking ahead, Ellison sees CPUs shining at the edge, where cost, latency, and power efficiency matter most. From healthcare to retail to manufacturing, organizations are turning to CPU-based inference for fraud detection, recommendation systems, and conversational AI—all running on existing infrastructure.
For C-suite leaders, the advice is clear: design for total cost of ownership, not just performance. By weighing power, licensing, talent, and integration, executives can make smarter infrastructure decisions that balance innovation with pragmatism.
The future of enterprise AI isn’t about chasing hype—it’s about building trust, delivering measurable outcomes, and scaling responsibly. Lenovo’s open, business-first approach is helping customers do just that.
The episode is available on all major podcast platforms—or watch the full video on the Intel on AI YouTube channel.
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