The AI revolution is accelerating. While generative AI (GenAI) and, more recently, agentic AI have dominated headlines, realizing their full potential depends on achieving enterprise AI maturity – the ability to operationalize AI at scale and integrate it seamlessly into critical workflows. Achieving this level of maturity in AI’s latest wave of evolution depends on distributed inference, a foundational strategy that drives unparalleled efficiency, scalability, and real-time decision-making.

Our latest whitepaper, The Next Phase of AI Maturity: Unlocking Enterprise AI’s Full Potential Through Distributed Inference, explores how AI-mature organizations are redefining their infrastructure strategies to scale AI innovation and deliver tangible business value.

AI leaders set the bar for enterprise transformation

As enterprises push toward greater AI maturity, Edge AI emerges as a critical transformation enabler. By bringing intelligence and decision-making closer to where data is generated, inference at the edge allows organizations to achieve faster, more efficient, and privacy-preserving operations.

• 85% of enterprises are migrating AI workloads to the edge (451 Research)

• Edge AI directly brings intelligence and decision-making capabilities to the network's edge, enabling faster, more efficient, and privacy-preserving applications.” (Capgemini)

Four pillars for scaling enterprise AI

Achieving enterprise AI maturity through distributed inference requires the proper infrastructure to ensure effectiveness at scale. This shift enables real-time processing, cost efficiency, and compliance – critical elements for modern AI applications. The whitepaper identifies four core infrastructure pillars essential for success:

• Specialized compute at the edge: The rise of GenAI and agentic AI demands silicon diversity, with enterprises deploying specialized AI chips across edge locations to optimize performance and control costs.

• Serverless inference: Managed cloud resources that scale on demand eliminate the need for costly, complex hardware investments, enabling organizations to focus on innovation.

• Real-time data integration: Combining retrieval-augmented generation (RAG) with real-time streaming ensures AI models operate with the latest data while maintaining compliance and privacy.

• Open, composable architectures: The future of enterprise AI depends on open, composable stacks that empower organizations to adopt best-of-breed solutions across infrastructure, models, and tools.

Don’t miss out on the insights driving the next phase of AI maturity. Download the full whitepaper to build your path to real-time, distributed inference today.