Scaling AI Across the Business

In 2025, scaling AI is no longer a research and development side project; it’s an operational imperative. And yet, many organizations struggle to see a meaningful return on investment (ROI) at the speed the business demands.

Successfully scaling AI across the enterprise requires a return to the foundational principles of high-performing technology organizations. These principles were first popularized through DevOps and have evolved into DevOps’ AI-focused successor: machine learning operations (MLOps). Today’s leaders face a dual mandate: industrializing a pipeline of promising AI use cases while ensuring trust, quality, and demonstrable value.

The urgency to deliver speed to market has never been greater. Capital investments in AI are enormous both in funding and in time and expertise of top talent across departments. Still, fear of falling behind isn’t justification for hasty or misaligned efforts.

After attending this session, you should be able to:

• Assess your organization’s current AI maturity and identify the biggest bottlenecks to scale
• Design an enterprise AI operating model that balances speed, safety, and cross-functional ownership
• Implement fit-for-purpose governance controls, including data lineage, prompt risk scoring, and large language model gateways
• Apply MLOps automation and monitoring to sustain quality in nondeterministic, multimodel environments