Riley Bonnesen

Senior IT Data, AI, & ML Manager JE Dunn Construction

Riley Bonnesen is the Senior IT Data Manager at JE Dunn, where he defines and executes the corporate data strategy driving business intelligence and AI solutions across the organization. As the Data Analytics team leader, he has spearheaded the development of a robust Fabric environment with semantic models and lakehouse architecture. His team’s work directly influenced organizational decisions on project selection, workforce changes, and financial strategies. He championed the enhancement of data-driven decision-making processes within the organization, implementing Agile methodologies to deliver enterprise solutions that align with stakeholder requirements.

Riley holds a B.S. in Economics with a minor in Cognitive Science from Truman State University, and this background has shaped his systems thinking approach to team building and problem-solving. He has pioneered the implementation of an ecosystem for power users that integrates emerging low-code technologies, with a particular focus on Power Platform, Power BI, and CoPilot. In this role, he serves as a strategic advisor to the Executive Leadership Team, translating complex data insights into actionable business strategies.

Riley is interested in AI integration and technology diffusion, complex adaptive systems, and mechanistic interpretability. He enjoys traveling with his wife and playing sports in his free time.

Seminars

Tuesday 21st April 2026
Restructuring Your Legacy Systems to Be Fit for Modern Analytics
11:00 am
  • Discussing how decades-old architectures are being restructured through warehouses and lakes
  • Revealing how flexible architecture frameworks enable firms to evolve as emerging technologies mature
  • Selecting foundational models and hyperscalers to build your future-ready architecture

CASE STUDY

Wednesday 22nd April 2026
Panel Discussion: Creating AI-Ready Data Ecosystems That Generate Meaningful, Trustworthy Insights
3:45 pm
  • Learning how to focus AI applications on operationally critical areas that have fit-for-purpose data, rather than implementing for novelty alone
  • Uncovering how to identify and diagnose AI biases and untrustworthiness to drive accurate, objective decision making
  • Reducing the bias of AI outputs through effective data governance and standards
Riley Bonnesen