Identity Infrastructure as the Foundation of Enterprise AI - with Dr. Ali Alkhafaji of Apply Digital
AI in Infrastructure PodcastShow Notes
The enterprise shift at the center of this episode is the growing gap between AI ambition and the underlying data and context infrastructure required to run systems reliably at scale.
In this episode, Dr. Ali Alkhafaji, Chief Executive Officer at Apply Digital, examines how enterprises stall when they focus on adding AI to existing workflows rather than defining the outcomes they want to achieve, as he joins Emerj's Marilie Fouché.
He highlights the practical moves that matter most — strengthening data foundations, building organizational context into AI systems, and driving adoption through top‑down leadership and targeted enablement.
Learn how leading brands and AI startups connect with enterprise AI buyer audiences at scale, download our media kit at http://emerj.com/AD4
More from AI in Infrastructure Podcast
AI agents can place new demands on existing infrastructure, from database traffic to permissions for accessing and modifying information. In this episode, Alistair Speirs, General Manager of Global Platforms in Microsoft's Azure business, joins Daniel Faggella, Emerj CEO and Head of Research, to examine how organizations can adapt their systems while reducing administrative work for employees. He discusses processing expense receipts through familiar chat tools, governing agent access, and preserving model and deployment options as infrastructure costs and business requirements evolve.
Mobile networks were built for predictable human behavior, not the nonstop demands of agentic and physical AI. In this episode, Pallavi Mahajan, Chief Technology and AI Officer at Nokia, examines why AI workloads demand deterministic connectivity rather than traditional reliability benchmarks, and how networks are shifting from passive transport to active participants in AI decision-making. The conversation covers the shift from over-provisioning to AI-native network intelligence, the move toward distributed compute at the edge, and new capabilities like integrated sensing and real-time translation built directly into network infrastructure.
AI pilots that perform flawlessly in testing often stall the moment they reach production, and enterprise leaders are left wondering why.
Enterprises are past asking whether LLMs work; the harder question is how to run them at scale without becoming dependent on a single model, cloud, or chip vendor — and without running out of power or data-center capacity in the process.
General‑purpose AI tools are hitting reliability and accuracy limits in professional workflows, forcing enterprises to rethink where these systems can truly deliver operational value.