Daniel Novotny
2026 Speaker

Daniel Novotny

Sr. Manager, AI Delivery

Southwest Airlines

About

Daniel Novotny is a Senior Manager of Technology at Southwest Airlines, where he leads teams focused on delivering AI, analytics, and data-driven solutions that support operational decision-making across the airline. He works at the intersection of business strategy, technology, and innovation, helping identify and scale practical applications of artificial intelligence that drive measurable value. Throughout his career, Daniel has partnered with business and technology leaders to transform complex operational challenges into actionable insights and modern digital capabilities. He is passionate about building high-performing teams, fostering innovation, and helping organizations responsibly adopt emerging technologies. Daniel brings a practical perspective on turning AI concepts into real-world business outcomes.

2026 Sessions

Oct 144:20 PM5:05 PM
Main StagePanel

(Panel) Beyond Immersive: How AI is Reshaping Enterprise Today and Tomorrow

Atrium Ballroom A

AI adoption has accelerated across nearly every enterprise function, but moving from experimentation to production-grade systems requires disciplined execution. In this panel, industry innovators share how they’re deploying AI today, measuring value, and managing risk, cost, and complexity over time. Rather than surveying trends, the discussion focuses on practical approaches to cost-effective experimentation, safe implementation, and robust governance. Hear how organizations are integrating AI into existing systems and workflows, building internal capabilities, and balancing automation with human oversight. Topics include: Use case selection : High-impact opportunities aligned with data, talent, and business goals Types of AI : Choosing the right approach (predictive models, computer vision, NLP, generative, agentic, physical AI)  Data readiness : Preparing, labeling, and governing data to ensure quality, accessibility, and security Model development & integration : Build, buy, or hybrid; training models and embedding into workflows Lifecycle management : Versioning, monitoring, and continuous improvement Risk & safety : Implementing human-in-the-loop and managing bias, drift, errors, hallucinations, and harmful outputs through validation, monitoring, and guardrails Infrastructure & cost : Scaling inference across cloud, on-prem, edge; optimizing performance, energy use, and cost Governance & responsible AI : Establishing policies for traceability, accountability, and regulatory compliance; aligning with organizational values Workforce readiness : Upskilling employees, building trust, and embedding AI into daily operations Measuring impact : Linking model performance to business outcomes like productivity, cost savings, and quality Scaling adoption : Standardizing tools and processes for reliable, repeatable deployment Use cases : Automation, workflow optimization, customer support, predictive maintenance, quality inspection, personalization, software development, cybersecurity, and more What’s next : Convergence of AI with XR, robotics, and digital twins; early agentic systems and autonomous workflows