Overview
Alex White leads CLA's Artificial Intelligence and Automation practice, where his teams take AI through its full lifecycle: advising leadership on strategy and governance, engineering the infrastructure and applications that deliver the intelligence, supporting them in production, and measuring the value they return. Application development, cloud, and DevOps sit alongside data science in his practice, so the same group that recommends a direction is accountable for building it and running it.
Across that lifecycle, his work with clients covers four things:
- Advise. Deciding where AI belongs in the business and where it does not, prioritizing use cases against real economics, and defining the governance and controls that let organizations move quickly without getting ahead of their risk posture.
- Build. Engineering AI-ready foundations and the products on top of them: cloud architecture, modern application development, automation, DevOps, and the data platforms that make intelligence usable.
- Support. Running and improving what his teams deliver, from workforce enablement and adoption through the operating model that sustains results after go-live.
- Measure. Holding the work to the business case, reporting realized value, and retiring what does not earn its keep.
Alex holds a Ph.D. in biostatistics from Indiana University and a B.S. in physics from Rose-Hulman, and his work spans large language model architecture, retrieval-augmented generation, and high-dimensional statistical modeling. Before joining CLA, he built and deployed AI systems within big tech and global pharmaceutical organizations, giving him a practitioner’s perspective on the gap between a compelling vendor demonstration and reliable performance at enterprise scale.
A frequent speaker at industry conferences and board and executive sessions, he advises organizations across a breadth of industires.
Practice Scope
- Enterprise AI strategy and use-case prioritization
- AI governance, risk, and responsible-use frameworks
- AI-ready infrastructure, cloud, and DevOps
- Application development and intelligent automation
- Data foundations and analytics maturity
- Adoption, enablement, and managed support
- Value realization and measurement of AI investment
Education
Bachelor of Science, Physics, Rose-Hulman Institute of Technology
Ph.D., Biostatistics, Indiana University
Technical Depth
- Large language model architecture, fine-tuning, and efficiency
- Retrieval-augmented generation and AI-driven search
- Machine learning and predictive analytics
- High-dimensional statistics and sparse latent-space modeling
- AI product development, deployment, and MLOps
- Cloud architecture and modern application engineering