AT&T’s Deepak Sharma on Why AI That Feels Like Magic Is AI That Works

Managing millions of daily customer interactions at AT&T, Head of Retail Technology, Contact Center Platforms, GenAI Product & Engineering Deepak Sharma, has learned that successful AI transformation requires building AI-ready infrastructure before chasing AI features. His dual-lane framework separates quick wins like agent assist and call summarization from foundational data pipeline work that enables sophisticated AI at enterprise scale.

His most compelling example, he tells Ashish, involves digital avatars that create three-way interactions between customers, human agents, and AI, delivering experiences customers actually prefer over traditional service. Successful AI adoption happens when solutions are simple enough to feel like magic rather than technology requiring extensive training.

Topics Discussed:

• The infrastructure requirements for creating truly omnichannel customer experiences that work across retail stores, contact centers, and digital channels at enterprise scale.
• A dual-lane approach to AI transformation that separates quick wins like agent assist and call summarization from foundational data pipeline and orchestration work.
• Digital avatar implementations that enable three-way interactions between customers, human agents, and AI to create superior customer experiences.
• Prioritization frameworks for managing thousands of AI use cases across large enterprises while balancing feasibility, time to market, and business impact.
• The critical role of expectation management and stakeholder alignment in AI transformation, treating it as business process transformation rather than technology implementation.
• Change management strategies that work at scale, including making AI solutions simple enough that extensive training programs become unnecessary.
• Why AI should be invisible in successful implementations, embedded seamlessly into existing workflows rather than presented as separate AI-powered features.
• The importance of understanding frontline worker needs by directly observing contact center and retail store operations rather than making assumptions about problem-solving.

Listen to more episodes: 

Apple 

Spotify 

YouTube