Data-Driven AI: Building the Foundation for Scale

Thursday, March 19th · 12:30pm – 5:00pm · Frederik Meijer Gardens & Sculpture Park

Date & Time

Thursday, March 19th
12:30pm – 5:00pm
(Check-in begins at 12:00pm)

Location

Frederik Meijer Gardens & Sculpture Park
1000 East Beltline Avenue Northeast
Grand Rapids, MI 49525

Thank you to our sponsors

Why attend this event?

Many organizations are investing significantly in AI, from careful pilots to enterprise-wide deployments. Yet most face a common challenge: AI initiatives that don’t scale, don’t get adopted, or don’t deliver expected business value.

The real problem isn’t the AI technology. It’s the data foundation beneath it.

This half-day event cuts through the AI hype to address what actually determines success: building robust data and infrastructure systems that enable AI to scale sustainably. You’ll discover why most AI initiatives fail due to data quality, governance, and infrastructure gaps, and learn a practical operating model for addressing these foundational challenges.

Through real-world examples, deep-dive sessions on data ecosystems and technology infrastructure, and conversations with local business leaders at different stages of their journey, you’ll gain concrete strategies for building the foundation that makes AI scale possible.

Event agenda

12:00 PM - 12:30 PM

Arrival & check-in

12:30 PM - 1:00 PM

Session one - The AI Operating Model

Discover the 8 critical dimensions where AI initiatives commonly fail and why a systematic operating model is essential for success. Understand the evolution from individual GenAI adoption to enterprise AI challenges, and why organizations that don’t build strong foundations today will be left behind as multi-agent orchestration and advanced AI capabilities emerge. Through real-world examples of both failures and successes, learn how to address the challenges preventing AI from moving beyond pilots.

Key takeaways:

  • Learn why most AI initiatives fail due to data quality, governance, and infrastructure gaps
  • Discover the AI Operating Model and its 8 pillars built on people, process, and technology
  • Understand the evolution from GenAI adoption to enterprise AI scale and what’s coming next
  • Recognize why building data foundations today is critical for future AI capabilities like multi-agent orchestration

1:00 PM - 1:45 PM

Session two - Engineering reliable data

Explore what AI-ready data actually requires and the common data failures that derail AI initiatives. Learn the components of a robust data ecosystem, from engineering and pipelines to governance and analytics, and discover how these capabilities deliver immediate business value through improved analytics and decision-making, regardless of your AI timeline. Understand practical approaches to strengthen your data foundation for both current business intelligence needs and future AI ambitions.

Key takeaways:

  • Understand the data quality, governance, and pipeline requirements for production AI
  • Learn common data failure patterns that prevent AI scale and how to avoid them
  • Discover how data lakes and master data management improve business intelligence accuracy and reliability today
  • Build a roadmap to strengthen your data ecosystem for current analytics and future AI needs

1:45 PM - 2:00 PM

Break

2:00 PM - 2:30 PM

Session three - Modern data storage for AI

Everpure (formerly Pure Storage) will explore modern data storage solutions designed to support AI workloads at scale. Learn how storage architecture decisions impact AI performance, when to deploy on-premises versus hybrid versus cloud infrastructure, and how to balance cost with capability. Discover practical approaches to building storage systems that support both current operations and future AI demands.

Key takeaways:

  • Learn modern data storage approaches from Everpure that support AI performance and scale requirements
  • Evaluate cloud, hybrid, and on-premises deployment strategies for different workload phases
  • Understand cost considerations and tradeoffs across different storage architectures

2:30 PM - 3:15 PM

Session four - AI agent orchestration

Learn why disconnected AI tools, shadow usage, and fragmented data access create risk and inefficiency at scale. Discover the concept of an internal AI platform that acts as a centralized control plane, connecting enterprise knowledge, enforcing governance, and enabling scalable AI capabilities across teams. See a live demonstration showing the evolution from knowledge-aware chat to multi-step AI agents to purpose-built AI applications, all powered by unified, governed infrastructure.

Key takeaways:

  • Understand why an enterprise AI platform enables coordination and governance that individual tools cannot provide
  • Learn how to standardize AI across teams while connecting to enterprise knowledge securely
  • See a demonstration of AI evolving from chat to agents to workflow automation
  • Discover the architectural components required for scalable, governed AI adoption

3:15 PM - 4:00 PM

Panel discussion - Building data & AI practices in West Michigan

Hear from local business leaders at different stages of data and AI maturity as they share practical insights from building capabilities in their own organizations. Engage in conversation about the challenges, lessons learned, and strategies that work in real-world implementations.

What you’ll gain:

  • Learn from local examples at different maturity levels
  • Understand common challenges and how others have addressed them
  • Ask questions relevant to your specific situation
  • Connect with peers navigating similar journeys

4:00 PM - 5:00 PM

Happy hour & hands-on demonstrations

Enjoy heavy appetizers and an open bar while connecting with fellow attendees and our engineering team while exploring live demonstrations of AI solutions in action. Experience firsthand the technologies and approaches discussed in the sessions, ask detailed questions about implementation, and discover how these solutions might apply to your specific challenges. This interactive session bridges the gap between learning and application, giving you practical exposure to working AI systems and the opportunity to build valuable connections with peers facing similar challenges.

Meet the panelists

Rob Campbell

Director of Applied AI

Corewell health

Rob is a data science, machine learning, and AI leader with a background in engineering and analytics. He began his career designing nuclear submarines for the US Navy, where he developed a passion for solving unique and complex problems with data.

Rob has extensive experience in applying data science, machine learning, and AI in highly regulated industries, such as defense and healthcare. He enjoys exploring innovative ways to apply artificial intelligence in both a personal and professional capacity.

Rob heads the Artificial Intelligence Center of Excellence at Corewell Health, a centralized unit overseeing the ethical and effective adoption of AI across the organization. He is a strong advocate for transforming healthcare for the better with data science and AI.

Adam Niemur

Director of Data & Analytics

Gordon Food Service

As Director of Data & Analytics at Gordon Food Service, Adam is responsible for devising and executing enterprise-wide data engineering, reporting, analytics, and artificial intelligence (AI) initiatives. His passion lies in harnessing data to drive strategic decision-making within the food service industry, thereby facilitating innovative solutions and transformative results.

The food and foodservice industries are currently at a crucial turning point, influenced by significant changes such as rapid technological advancements, the emergence of new players, increased emphasis on food traceability and safety, and rising costs. Adam firmly believes that data and AI will revolutionize the foodservice industry, and he feels honored and excited to lead the development of innovative programs that will deliver tailored and integrated experiences to individual foodservice operators.

Brett Quada

Director, Business Analytics

Aspen Surgical

Brett Quada leads Analytics at Aspen Surgical, a mid-market medical device company based in Grand Rapids. With 15 years of non-linear experience winding through FP&A, sales support, manufacturing operations, and IT, he brings a cross-functional perspective to driving performance. He has succeeded in fast-paced, acquisition-driven environments by modernizing systems and data platforms to unlock commercial and operational value.

As a Lean Six Sigma Black Belt, Brett combines continuous-improvement rigor with a practical focus on building modern data solutions and thoughtfully leveraging AI to convert complex data into actionable insights, automation, and measurable business outcomes.