Guest: Ari Weil (LinkedIn)
Company: Akamai
Show Name: An Eye on AI
Topic: Edge Computing

AI doesn’t create value when insights arrive minutes or hours later. The real breakthrough happens when intelligence is delivered instantly, at the moment decisions are made. That shift is driving enterprises to rethink where AI inference runs — and why the edge is becoming essential.

In this clip, Ari Weil, VP of Product Marketing at Akamai, outlines the real-world AI experiences that become possible when inference moves closer to users, data, and machines.

From delayed insights to real-time outcomes

Many AI-driven workflows today still rely on centralized processing and delayed responses. According to Ari, this model limits what businesses can do. When intelligence lives far from where data is generated, experiences like real-time personalization, instant approvals, or live video analysis become impractical.

Edge-based inference changes that equation. By processing AI workloads near the source of data, enterprises can move from analysis-after-the-fact to action-in-the-moment.

New experiences unlocked by edge inference

In media and entertainment, real-time video intelligence is one of the clearest examples. AI can identify key moments in live streams, detect anomalies, and instantly generate derivative content — whether for highlights, engagement, or entirely new business models. This intelligence works because it happens close to the video capture itself, not in a distant data center.

Commerce and travel are seeing similar shifts. Instead of static recommendation engines, AI can now generate highly personalized experiences in real time — adapting to user behavior, preferences, and context without manual intervention or delays. What once required customer service or post-processing can now happen programmatically, instantly.

Why inference at scale is business critical

Ari emphasizes that these experiences are not experimental. Enterprises are already investing in inference at scale because it directly impacts growth, efficiency, and competitiveness over the next several years.

Capabilities like real-time fraud detection, instant credit or loan approvals, and risk evaluation depend on AI’s ability to process proprietary data and deliver outcomes immediately. Delayed intelligence increases cost, risk, and friction — especially in regulated or high-stakes environments.

Preparing for agentic and physical AI

Looking ahead, these same principles extend into agentic and physical AI use cases. Machine-to-machine interactions, robotics, supply chains, healthcare, and life sciences all require intelligence that operates continuously and autonomously.

Edge inference provides the foundation for these systems by enabling AI to sense, decide, and act without constant round trips to centralized infrastructure. As agents become more common, distributed inference becomes a requirement rather than an optimization.

What this means for enterprise leaders

For decision-makers, the message is clear. The next phase of AI adoption is not about bigger models alone. It’s about where inference runs and how quickly intelligence can turn into action.

Akamai’s focus on inference at the edge reflects a broader industry shift toward real-time, outcome-driven AI architectures that are ready for today’s business needs and tomorrow’s agentic systems.