AI at Enterprise Scale With AT&T’s Monika Malik

Building AI at enterprise scale is not just about models or algorithms. How do organizations manage governance, data, and risk while still moving fast?

This week’s VentureFuel Visionary is Monika Malik, AT&T’s Lead Data & AI Software Engineer. With a data engineer career spanning telecom (AT&T) and global banking (Barclays), she brings a pragmatic perspective on what it takes to move from experimentation to production-grade AI at massive scale for large enterprises.

This episode is a masterclass in scaling AI capabilities that are reliable, governed, and adopted by the business!

Episode Highlights

• Production-Ready AI Is More Than Model Accuracy – Monika explains why enterprise AI success depends on governance, evaluation systems, deterministic workflows, and logging… not just building accurate models.
• Escaping the Proof-of-Concept Trap – She discusses why many AI initiatives stall in pilot mode and how organizations must redesign processes, build ownership, and prepare their data architecture to scale AI beyond experiments.
• Human-in-the-Loop AI for Regulated Industries – The conversation explores why fully autonomous AI isn’t realistic in regulated sectors and how human oversight, role-based access, and audit trails ensure trust and compliance.
• Balancing Speed, Stability, and Cost – Monika breaks down the engineering trade-offs teams face when building enterprise AI systems and how leaders must choose between rapid experimentation and stable production systems.
• AI Success Is Measured by Business Impact – She shares why enterprises must define clear metrics — like reduced cycle times and real user productivity gains — to prove that AI initiatives deliver measurable value.