Why This Webinar Matters for Enterprises Right Now
Enterprise websites are becoming AI surfaces. Answers influence decisions long before users reach workflows, dashboards, or forms. When those answers come from generic AI, risk enters the system.
Most enterprises deploy AI on top of fragmented data, unclear ownership, and legacy governance. While this setup can appear workable in demonstrations, it often collapses in production, leading to inconsistent answers, compliance exposure, and declining trust across teams.
This session explains where generic AI collapses at enterprise scale and what actually changes when systems, data contracts, and decision paths align.
You will understand:
- Why accuracy degrades as complexity increases
- How governance gaps create operational risk
- What enterprise-grade AI architecture requires
- Where teams lose control and how to regain it
Topics Covered
- Why generic AI models break down when applied to complex enterprise websites
- How fragmented data, system dependencies, and unclear ownership distort AI outputs
- Where accuracy, consistency, and compliance fail between demo and production
- The role of data contracts, governance, and decision paths in reliable AI systems
- What enterprise-grade AI architecture requires beyond model selection
- How enterprises regain control over AI outputs without slowing teams down
Who Should Attend
Perfect for enterprise teams responsible for production AI and digital reliability:
- Enterprise Technology Leaders: owning AI systems that must work beyond demos.
- Digital & Web Platform Leaders: managing large, complex enterprise websites.
- Data & Architecture Teams: responsible for data integrity, contracts, and governance.
- Product & Experience Leaders: accountable for accuracy, trust, and user impact.
- Security, Risk & Compliance Teams: evaluating AI exposure and operational risk.
Speakers
Karthik Kalimuthu
Enterprise AI Strategy Lead
Karthik works with large organizations to adapt AI for complex websites, governance, and scale. He focuses on aligning content systems, data structures, and decision workflows so AI drives measurable business outcomes across enterprise digital platforms.
