Here are three takeaways to help you get started:

1. Start with a high-impact, low-lift AI use case

Don’t boil the ocean. Don’t start with the flashiest idea. The most successful AI programs start with a single use case that checks three boxes:

1. It supports a strategic priority (like customer service or fraud prevention)
2. The data to support it already exists
3. The expected ROI is measurable

For example:
One retail bank used AI to power a virtual assistant that deflected 30% of inbound service calls—freeing up staff and improving customer satisfaction. That single project laid the foundation for broader AI adoption.

2. Use a structured AI framework to align stakeholders

Most AI projects stall because they lack internal alignment. Our Five Pillars of AI Discovery help teams move forward with clarity:

1. Objective definition: What’s the use case? What does success look like?
2. Data assessment: Is the data accessible, high-quality, and usable?
3. Responsible AI: Are ethical, regulatory, and security risks addressed?
4. Solution design: Is the architecture production-ready, not just a prototype?
5. AI Ops strategy: Who owns deployment, monitoring, and iteration?

This framework turns AI from a vague idea into a business initiative with structure and accountability.

3. Focus on data, not just models

AI tools are evolving fast, but the underlying truth hasn’t changed: Your outcomes are only as good as your data. Ali Ghodsi, CEO of Databricks, puts it plainly: “AI is becoming essential, but the true value lies in the data that fuels these models.”

Before investing in another platform or model, ask:

• Do we have the right data infrastructure in place?
• Can we access and activate that data across teams and systems?
• Have we modernized beyond legacy warehouses?

The workshop includes a data readiness assessment to help teams answer these questions honestly—and plan next steps.