AI WORKSHOP RECAP
AI is everywhere—but value isn’t. According to BCG, only 4% of organizations have developed AI capabilities that drive significant impact. The other 96%? Still navigating the same roadblocks: unclear strategy, scattered initiatives, data silos, and no defined path to ROI.
If that sounds familiar, you’re not alone, and you’re not behind. But you do need a plan.
That’s why Zennify and Terazo hosted a virtual AI workshop to help financial institutions cut through the noise and move from AI conversations to real outcomes.
Watch the full AI workshop here >
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:
- It supports a strategic priority (like customer service or fraud prevention)
- The data to support it already exists
- 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:
- Objective definition: What’s the use case? What does success look like?
- Data assessment: Is the data accessible, high-quality, and usable?
- Responsible AI: Are ethical, regulatory, and security risks addressed?
- Solution design: Is the architecture production-ready, not just a prototype?
- 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.
Watch the full AI Workshop
If you’re exploring AI at your institution but need a clearer roadmap—this session is for you. You’ll get expert guidance, real use cases, and a repeatable approach to unlock value quickly.
