Establishing Foundational Engineering for Artificial Intelligence

With AI-driven solutions rapidly expanding across their value chain, a Fortune-ranked consumer packaged goods company knew they needed more than their current mix of capabilities. They wanted a true AI engineering function—one that could support immediate needs and scale for tomorrow.

Whether AI was developed internally or integrated into their existing tech stack, they recognized a solid foundation of knowledge and shared practices was critical to innovating and delivering AI solutions that create real business value across the organization.

Business leaders saw promise in AI, but faced uncertainty around how to deploy models responsibly, handle the intricacies of machine learning operations (MLOps), and manage risk. Their digital transformation was well under way. But siloed experimentation had led to duplication, and the absence of a robust process, exposed the company to regulatory and reputational risks.

Fragmented online resources and internal workshops had contributed to uneven outcomes across teams, with inconsistent standards and confidence in deploying, monitoring, and governing models in production.

Elder Research designed and facilitated a 32-hour, four-week AI Engineering University—a hands-on enablement program that focused on empowering engineers in the enterprise data science organization to transform data science prototypes into production-grade, self-monitoring ML pipelines. The curriculum included the AI lifecycle from data ingestion to model retraining triggers, introducing ethical and operational risk controls. It taught how real pipelines can monitor model drift, trigger alerts, and protect customers from unintended harm.

Using the client’s preferred platforms, the course demystified critical concepts and equipped teams with the knowledge to design, build and sustain robust, production-grade AI systems.

• The success of AI Engineering University gave rise to a shared operating process that placed team members on a unified standard path to production. Graduates can now operationalize sophisticated and trustworthy models, with automated model retraining and robust monitoring

• Incident response times have improved

• Data lineage tracking has increased confidence in policy and regulatory compliance

Today, these capabilities underpin company-wide efforts such as new customer personalization, fraud detection, and supply chain forecasting.

Leadership now cites this program as a turning point in the company’s journey toward operational excellence.