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DESCRIPTION:Click for Latest Location Information: http://dgiq-aigov2026.da
 taversity.net/sessionPop.cfm?confid=166&proposalid=16779\nMany organization
 s focus on models, prompts, and technology when deploying AI. Yet projects 
 often stall because critical business context remains fragmented across tea
 ms, processes, systems, and documentation.\n\nThis session presents a real-
 world case study of an AI-powered quote automation initiative that faced ch
 allenges familiar to many organizations: inconsistent processes, undocument
 ed business rules, disconnected data sources, and knowledge concentrated in
  a small number of subject matter experts. Rather than starting with techno
 logy, the team first created a structured representation of the business co
 ntext surrounding the quote lifecycle, capturing relationships among people
 , processes, systems, data, decisions, and operational constraints.\n\nThe 
 resulting context foundation exposed hidden dependencies, clarified ownersh
 ip, surfaced process bottlenecks, and provided the structure needed for AI 
 solutions to operate consistently and at scale. The approach accelerated im
 plementation, improved quote quality, reduced rework, and created a repeata
 ble framework for future AI initiatives.\n\nAttendees will learn how a cont
 ext-driven approach can help organizations move beyond isolated AI pilots b
 y making business knowledge visible, governed, and reusable. The session wi
 ll demonstrate practical techniques for capturing institutional knowledge, 
 connecting data to business outcomes, and establishing the foundation requi
 red for trustworthy AI adoption.\n\nKey Takeaways\n\n
 Understand why missing business context is often the biggest barrier to suc
 cessful AI implementation.\n
 Learn a practical method for capturing and organizing relationships among p
 eople, processes, technology, and data.\n
 See how context mapping revealed process inefficiencies, ownership gaps, an
 d hidden dependencies within a quote automation program.\n
 Discover how a context foundation supports AI governance, data quality, exp
 lainability, and operational scalability.\n
 Leave with a repeatable approach for preparing business processes and data 
 for AI-enabled transformation.\n\nAudiences who would benefit from this ses
 sion: Data Governance Leaders, AI Governance Practitioners, Data Management
  Professionals, Business Transformation Leaders, Enterprise Architects, and
  Program Managers responsible for AI adoption.\n
DTSTART:20261118T113000
SUMMARY:Beyond the Model: How Context Turned a Quote Automation Pilot Into 
 a Scalable AI Capability
DTEND:20261118T121459
LOCATION: See Description
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