The AI readiness test: Is your practice built to withstand the pressure?

AI readiness framework

Zennify's AI Readiness Model helps financial institutions pinpoint their starting point. It centers on three core areas: People, Process, and Technology. Each plays a critical role in whether AI becomes a value driver or just more noise.

• People: Are leaders aligned? Is there a sponsor who owns outcomes? Do teams understand how AI fits into their work?
• Process: Are workflows clearly defined and repeatable? Can you measure performance or pinpoint where things break down?
• Technology: Is your data current, connected, and governed? Do your systems support automation and integration?

‍

‍

Assign a red, yellow, or green rating to each category. Focus your early initiatives where confidence is highest, and build toward longer-term improvements in weaker areas.

This exercise helps identify bottlenecks and pain points, reduce false starts and brings structure to your adoption roadmap. With a strategy, you can drive AI adoption, strategically.

Let your processes point the way

When AI feels overwhelming, start with the work itself. You don’t need to guess where to begin. Your processes already hold the answer.

The best way to identify high-value use cases is by analyzing how work actually gets done. At Zennify, we use a method called process mining to visualize the flow of tasks, pinpoint where delays occur, and surface hidden inefficiencies. It gives teams a clear view of what’s slowing them down—and where AI can provide immediate relief.

You don’t need to map everything. Reviewing just a few high-effort workflows is enough to uncover strong starting points. Look for patterns in areas like:

• Recurring bottlenecks
• Slow, repetitive, manual steps
• Frequent handoffs between teams

These signals often point to low-complexity, high-volume work that is ideal for automation. That could mean automating meeting prep, document intake, or lead prioritization. It depends on where your process strain is most visible.

Fix the friction before you scale AI

When AI efforts stall, the problem isn’t usually technical, it’s structural. The most common breakdowns come from five areas that have little to do with platforms and everything to do with how your practice operates.

1. No clear outcome: AI adoption often starts with hype instead of purpose. If you can’t tie the effort to a measurable goal, it won’t gain traction. Success starts with defining the outcome, not chasing the tool.
2. Lack of sponsorship: Without support from senior leadership, AI stays stuck in experimentation mode. You need visible commitment, aligned funding, and one person who owns results. A scattered approach rarely scales.
3. Disconnected systems and weak data: AI needs clean, accessible, and integrated data. If your architecture is fragmented or your data is outdated, automation will only make the cracks more visible. Good data is a prerequisite, not a bonus.
4. Low trust in the data itself: Even when systems are connected, teams won’t use AI if they don’t trust what it’s built on. That trust has to be earned with clear governance, quality standards, and transparency.
5. Workflow resistance and lack of enablement: AI doesn't replace your team, it changes how they work. If they’re not part of the process from the start, adoption fails. You need to tackle workflow friction and involve users early, often, and honestly.

These issues can’t be fixed overnight, but they can be surfaced and addressed if you’re willing to look. That’s the point of a readiness test. Not to get a perfect score, but to understand where you are and what needs to happen next.