From Digital & AI Strategy to Organizational Adoption
Bridging the gap between pilot and production for organizations ready to scale.
A decade leading marketing and operations technology across multi-location retail and hospitality portfolios.
I'm Jason Leinart — a digital transformation leader and systems architect focused on turning complex operational bottlenecks into high-velocity working systems.
With a background managing $100M+ operational portfolios across marketing operations and data systems, I bridge the gap between technical research and production-ready implementation. I specialize in architecting the frameworks that allow mid-market and enterprise organizations to move beyond the AI hype and into measurable, scalable adoption.
In Motion
- Speaking: Breakout session at PMI Great Lakes Spring Symposium 2026 on AI-native project management.
- Architecting Intelligent Workflows: Developing autonomous agent systems and automation frameworks to eliminate operational bottlenecks.
- Domain-Specific Transformation Research: Synthesizing playbooks for Finance, Healthcare, and Manufacturing to navigate industry-specific regulatory and deployment timelines.
- Platform Implementation: Executing production-ready builds on AWS Bedrock and Google Cloud to demonstrate technical feasibility for enterprise-scale adoption.
- Strategic Advisory: Partnering with select clients to bridge the gap between AI capability and organizational readiness.
Previously: Over a decade leading operations for high-growth organizations. My career has been defined by the intersection of technical infrastructure and business growth—building the pipelines and integrations that scale with revenue.
Recent Analysis
View allThe Deployment Gap: What Peer-Reviewed Research Reveals About Healthcare AI's Readiness Problem
A systematic analysis of peer-reviewed healthcare AI papers reveals that deployment failures are organizational readiness problems, not technology problems. Evidence-based framework for healthcare AI leaders.
Enterprise AI Transformation
Why 61% of AI initiatives fail to deliver EBIT impact—and the 10-20-70 framework that separates high performers from the rest.
Recent Notes
View allWhat 510 Contracts Taught Me About Training Data
My first ML model predicted the same 9 labels for every input. The fix wasn't a better model — it was a better data pipeline. The difference between 510 training examples and 15,700.
When Consistency Beats Intelligence
A 70,000-person field experiment proved AI doesn't need to be smarter than humans to outperform them. It just needs to be more consistent. Better inputs, not better decisions.
Problems I Think About
Why enterprise AI adoption stalls at the vendor integration layer • Bridging the gap between legacy marketing automation and autonomous agentic workflows • The trust-building progression from data analytics to full automation • Managing disparate deployment timelines across regulated industries (Healthcare vs. Retail) • Architecting lean, cloud-native systems that provide mid-market firms with enterprise-grade capabilities.
Let's Scale What Works
I partner with organizations to bridge the gap between strategic AI research and production-ready implementation. Whether you are looking to architect intelligent workflows, navigate vendor AI adoption, or hire a leader who understands the intersection of operations and technology—let's start a conversation.