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Steve Novak
Steve Novak

Steve Novak

Data Engineering Practice Lead
Definian
Bio

Steve Novak has built his career on a contrarian premise: that organizations invest heavily in People, Process, and Technology while consistently neglecting Data—the Fourth Pillar that breaks first and gets fixed last. Leading Definian’s data engineering practice, he specializes in building the Fourth Pillar that enables transformation projects to go from proof-of-concept to production.

With 26 years serving Fortune 500 enterprises, Steve has developed pattern recognition around why analytically sound solutions fail operationally. His work focuses on the critical gap where organizations skip the essential work of embedding AI into workflows with clean, validated data foundations—jumping directly from standalone pilots to orchestration, producing low ROI and high failure rates.

Steve’s validation-first methodology works backward from production outcomes to build robust data foundations. Recent implementations demonstrate the approach’s power: reducing test cycle times from 3 days to 30 minutes, enabling 4 team members to manage what previously required 10+, and rescuing two stalled enterprise initiatives in the past year.

Current initiatives include AI Readiness Assessments identifying why pilots fail to scale, Pipeline Modernization for manufacturing companies with stalled implementations, and DataOps-as-a-Service bridging the gap between analytical promise and operational reality. His approach helps organizations distinguish individual productivity gains from enterprise transformation embedded in core business processes.


From Failure to Value: A Practitioner's Guide to Successful AI Transformation

Featuring: Vince Stuntebeck

Despite unprecedented investment in data and artificial intelligence, a portion of AI initiatives fail to deliver measurable business value. This session reveals why—and provides a proven framework for success.

Through analysis of AI implementations across manufacturing, healthcare, and financial services, we’ve identified that the primary barriers to AI success aren’t technological. Instead, data infrastructure gaps and organizational readiness issues account for the vast majority of failures. Organizations repeatedly attempt to deploy AI as technology initiatives, leading to stalled projects, wasted investment, and growing skepticism.

This presentation introduces a value first methodology that reverses the typical approach. Rather than starting with AI models and hoping data challenges resolve themselves, successful implementations follow a structured sequence: identify opportunity for value, assess organizational readiness, build data foundations, validate AI-readiness, then deploy and operationalize AI solutions.

Attendees will learn:

  • Root causes of AI project failure and how to diagnose them early
  • An Assessment Framework for identifying data, people, process, and technology gaps
  • A value-driven methodology for prioritizing use cases with measurable ROI
  • Real-world case studies spanning multiple industries and use cases
  • A practical checklist and warning signs to apply immediately

Whether you’re leading AI initiatives, supporting transformation efforts, or advising organizations on analytics strategy, you’ll leave with actionable frameworks to dramatically improve AI success rates in your organization.

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