I put enterprise AI to work — and build the trust that lets it ship.

The through-line

Most enterprises can now describe an AI ambition. Far fewer can deploy AI into a regulated business and keep it there — because the hard part isn't the model, it's the trust: the data quality, lineage, validation, and governance that let a risk committee say yes. That gap is where I've spent my career.

As SVP of Data, Analytics, AI & Governance for a $150B+ wealth management business at Regions Financial, I lead enterprise AI and data strategy across AI/ML, advanced analytics, data engineering, business intelligence, and governance. I directed the deployment and lifecycle of AI/ML models powering hyper-personalization and next-best-action, and established the model validation, monitoring, and periodic-review practices that keep them accurate, reliable, and compliant. As Group Data Officer and a voting member of the Enterprise Data Advisory Council, I'm accountable for how the enterprise governs and trusts the data its AI depends on.

In regulated industries, governance isn't what slows AI down — it's what makes AI shippable.

Building the foundation AI stands on

Before AI can create value, the data underneath it has to be trustworthy. I architected an enterprise Customer 360 ecosystem that unified intelligence across retail, commercial, and wealth channels, and led the modernization of the enterprise data warehouse from Cloudera to Snowflake — building the AI-ready cloud platform that accelerates every downstream analytic and model. Collectively, these capabilities enabled 10–20% year-over-year growth in new client relationships and double-digit revenue expansion.

Scaling data organizations through change

Earlier, as VP of Enterprise Data, Analytics & Cloud Strategy at Renasant Bank, I built the bank's foundational data and analytics ecosystem and chaired its first Enterprise Data Governance Office as the institution scaled from roughly $4B to $20B in assets through organic growth and multiple mergers. I've led data integration across M&A, established stewardship and quality standards, and built the high-performing, cross-functional teams that turn data into a strategic asset.

How I work

I'm a translator between the business and the technology — a trusted partner across product, risk, compliance, and operations who aligns data and AI investment with the priorities that actually drive growth. I lead with outcomes, insist on governance as an enabler rather than a brake, and build teams that outlast any single initiative.