Tejas Gajjar
2026 Speaker

Tejas Gajjar

Technology Engineering Manager

Macy’s

About

Tejas Gajjar is a Technology Engineering Manager at Macy’s Technology, where he leads enterprise infrastructure, platform engineering, automation, observability, cloud migration, and production operations initiatives. His work focuses on building resilient and scalable technology platforms that support business-critical systems across hybrid enterprise environments. Tejas has led modernization efforts involving middleware, Linux operations, cloud platforms, disaster recovery, monitoring, CI/CD automation, and enterprise integration. He is also active in technology thought leadership, with published work and speaking engagements focused on AI, infrastructure modernization, and emerging technology adoption. His perspective centers on helping organizations move from experimentation to reliable, secure, and production-ready enterprise systems.

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