The AI revolution is moving from chatbots to agentic, edge-first systems that can plan, execute, and optimize complex workflows with minimal human input, powered by cost-efficient, focused models.

Edge intelligence boosts device value, cuts bandwidth and cloud costs, reduces latency, and enables new applications in security, manufacturing, retail, and healthcare without overhauling existing infrastructure.

By slashing inference costs from roughly $50 per hour to about $10 per hour, thousands of automation use cases become economically viable, turning AI automation into standard operational infrastructure.

In practice, agentic AI is already delivering results, as seen with Better.com's Betsy, a voice mortgage agent that guides customers, collects documents, and moves loans toward closing, cutting origination costs by 41% and doubling lead-to-lock conversions.

The expansion potential spans procurement, customer service, HR onboarding and benefits, and financial close reconciliation, all achievable with autonomous agents handling most tasks.

The core economic driver is inference cost; domain-focused models like MiniMax M2.5 and its Lightning variant achieve near-frontier reasoning at a fraction of frontier costs, enabling broad deployment.

Edge intelligence achievement: Swann’s on-device AI deployment across 11.7 million IoT security cameras via Amazon Bedrock reduces false alerts through context-aware processing, underscoring real-time benefits of on-edge AI.

Strategically, the convergence of agentic workflows, edge-first processing, and cost-optimized models signals a competitive discontinuity; leaders should design workflows for agentic, edge-first AI to stay ahead.