Anil Pantangi
Delivery Executive, AI & Analytics
Dallas, Texas, United States
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Anil is an award-winning senior product, tech, and AI leader with over 15 years of experience managing large-scale enterprise platforms and consumer-grade products. He has a proven track record in leading high-performing teams through strategy, development, and change management.
Anil specializes in AI/ML initiatives, including conversational search, recommendation systems, and Generative AI, delivering significant business impact across various industries. His industry expertise spans telecom, media, education technology, and HR.
Professionally, Anil has held key roles at Amazon and Capgemini, driving AI-powered solutions, improving product adoption, and enhancing customer satisfaction. His AI/ML product strategy leadership has led to notable achievements in marketing, sales, and customer care platforms.
Anil is an official Forbes Technology Council member. He has been honored with several prestigious awards, including recently the top AI 75 leader.
Area of Expertise
Topics
Designing for Outcomes: Accelerate Innovation and AI Impact
In today’s product and AI innovation cycles, teams often jump to models or metrics before understanding what matters most: the customer experience and business outcomes. In this talk, I’ll walk through a practical approach I've used to lead high-impact initiatives- starting with writing down definition of your success, aligning cross-functional teams early, and embedding CX/UX thinking at the ideation stage. I'll share how rapid experimentation, telemetry-driven reporting, and a "fail-fast to succeed faster" mindset can drive measurable business impact.
Drawing from my work consolidating enterprise platforms, embedding NLP for smarter case routing, and building data-driven feedback loops across telecom, HR tech, and big tech environments, I’ll offer a pragmatic playbook for moving from friction to flow, while balancing innovation, execution, and user-centricity.
Engineering the Future: AI-Driven Platforms for Scalable Upskilling
As artificial intelligence continues to reshape the world around us, the real challenge isn't just deciding what skills people need—it's figuring out how to deliver those skills in ways that are scalable, ethical, and accessible to everyone. This session takes a deep dive into the technology powering the next generation of upskilling in higher education.
We’ll explore how AI is being used not only to teach but also to build the systems that support lifelong learning. From intelligent platforms that personalize learning paths to skill taxonomies that help standardize and align education with real-world needs, this session will highlight the technical foundations that make large-scale upskilling possible.
Key topics include:
AI-powered learning systems that adapt to individual learners and recommend the right content at the right time.
Skill graphs and taxonomies that help institutions and employers speak the same language when it comes to competencies.
Marketplaces for micro-credentials and modular learning that give learners more flexibility and control.
Aggregation tools that bring together fragmented learning resources, job data, and certifications into one unified experience.
Accessible technologies like voice interfaces, multilingual AI tutors, and low-bandwidth solutions that ensure no one is left behind.
Ecosystem design using APIs, cloud infrastructure, and large language models to connect universities, employers, and government programs.
We’ll also look at how AI is being used to teach AI itself—through simulations, virtual labs, and autonomous learning assistants—and how these innovations are helping to build a workforce that’s ready for the future.
With AI expected to contribute up to $13 trillion to the global economy by 2030, and more than 60% of jobs in advanced economies likely to be affected, the stakes couldn’t be higher. This session will show how thoughtful, well-designed technology can unlock massive economic and social value, while helping people everywhere build meaningful, future-ready careers.
Engineering the Future: AI-Driven Platforms for Scalable Upskilling
As artificial intelligence continues to reshape the world around us, the real challenge isn't just deciding what skills people need—it's figuring out how to deliver those skills in ways that are scalable, ethical, and accessible to everyone. This session takes a deep dive into the technology powering the next generation of upskilling in higher education.
We’ll explore how AI is being used not only to teach but also to build the systems that support lifelong learning. From intelligent platforms that personalize learning paths to skill taxonomies that help standardize and align education with real-world needs, this session will highlight the technical foundations that make large-scale upskilling possible.
Key topics include:
AI-powered learning systems that adapt to individual learners and recommend the right content at the right time.
Skill graphs and taxonomies that help institutions and employers speak the same language when it comes to competencies.
Marketplaces for micro-credentials and modular learning that give learners more flexibility and control.
Aggregation tools that bring together fragmented learning resources, job data, and certifications into one unified experience.
Accessible technologies like voice interfaces, multilingual AI tutors, and low-bandwidth solutions that ensure no one is left behind.
Ecosystem design using APIs, cloud infrastructure, and large language models to connect universities, employers, and government programs.
We’ll also look at how AI is being used to teach AI itself—through simulations, virtual labs, and autonomous learning assistants—and how these innovations are helping to build a workforce that’s ready for the future.
With AI expected to contribute up to $13 trillion to the global economy by 2030, and more than 60% of jobs in advanced economies likely to be affected, the stakes couldn’t be higher. This session will show how thoughtful, well-designed technology can unlock massive economic and social value, while helping people everywhere build meaningful, future-ready careers.
The Enterprise Brain: Agentic AI for Knowledge Management at Scale
Enterprises are drowning in documents, tribal knowledge, siloed systems, and fragmented tools. Traditional knowledge management solutions struggle with discoverability, context preservation, and continuous learning. This session introduces a bold new model—an "Enterprise Brain" built with Agentic AI. You’ll learn how autonomous agents can dynamically ingest, summarize, contextualize, and retrieve information from diverse sources (emails, tickets, docs, wikis), acting as reasoning intermediaries between employees and organizational knowledge. We’ll showcase design patterns for retrieval-augmented generation (RAG), memory persistence, knowledge graph construction, and agent alignment with enterprise policies.
Takeaways:
Designing agents for ingestion, summarization, and retrieval
Role of RAG, embedding stores, and fine-tuned models
Agent lifecycle management and continuous learning
Risks: hallucination, information governance, and access control
Scaling Platform Product Management in the Age of Cloud Native and AI
As cloud-native ecosystems mature and AI capabilities become integral to modern platforms, product management must evolve to balance innovation with developer experience, reliability, and scale. In this session, I will share actionable platform product management practices drawn from my experience leading cross-functional teams in big tech and the enterprise landscape. We’ll explore how to define and deliver platform value, prioritize internal user needs, drive adoption, and align roadmaps with both business and engineering goals—all within the context of cloud-native architectures.
From Chaos to Coordination: Multi-Agent Systems in Complex Enterprise Workflows
As enterprises evolve towards AI-first architectures, the complexity of managing workflows across departments, platforms, and tools increases exponentially. This talk will explore how Multi-Agent Systems (MAS) can be orchestrated to streamline enterprise workflows—from procurement to incident management, from HR automation to customer support. We'll discuss real-world patterns where agents act as planners, orchestrators, and collaborators, and how enterprises can transition from monolithic workflows to dynamic, agent-led execution. Attendees will gain practical insights into architecture, policy enforcement, human-in-the-loop design, and tools needed to safely scale agentic systems across business units.
Takeaways:
MAS patterns for real-world enterprise operations
Agent coordination and conflict resolution strategies
Integration with legacy systems and APIs
Governance, auditability, and safety measures
AI is not the Product session at SMU Dallas AI event
AI is evolving fast, but the fundamentals remain the same. Great products solve meaningful problems, and AI is one of the tools to get there. I had a great time speaking at the Dallas AI Summer Program at the SMU campus on “AI is not the product.”
The conversations that followed with team members I mentor and industry leaders were energizing. It was rewarding to hear feedback like, “..your talk was incredibly insightful and thought-provoking...” and “..appreciated your focus on prioritizing customer value over AI hype...
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