Delivering AI projects with purpose: real lessons from the trenches

AI isn’t some futuristic toy we’re tinkering with on the side. It’s already woven into the way we get work done at Kiandra. Whether it's helping sift through mountains of invoices or modernising stubborn legacy code, we’re using AI to tackle the headaches that come with real-world software delivery.  

We’ve experienced that AI delivery isn’t neat or predictable. It’s messy and iterative and full of trade-offs and lessons and failures. The best moments are watching someone use a system we built and say, “This just made my job easier.” To us, that’s success.

AI in the real world? Here's what that looks like

When you're leading a delivery team, complexity is part of the job description and AI helps us cut through it.  

We’ve used AI to make a tangible difference for clients, including the refactoring of older codebases in a fraction of the time it would’ve taken us the traditional way. We’ve trained models to spot performance anomalies in customer-facing apps, before they spiral into outages and lost user trust.

Tools like GitHub Copilot and Cursor have earned their place in our workflow. They won’t replace an expert developer, but they do help us work smarter with cleaner code, fewer bugs, and more time to think deeply about design, UX, and maintainability. At Kiandra, AI isn’t about replacing people, it’s about giving them back their time to focus on what really matters.

Why governance is the backbone of everything

Governance isn’t a checklist you tick off at the end. It’s the groundwork.

Before we even open a code editor, we’re asking these questions in our discovery workshops:  

• Do we have the right data?
• Can we use it?
• Is it complete and fit for purpose?
• Is it ethical and fair?
• Can we maintain privacy and security?  

These aren’t just compliance questions, they’re essentially design questions and part of our architectural decisions. They shape the entire direction of a project. We lean on ethical frameworks to help us think beyond just building functional systems, to building trustworthy ones.

Measuring impact beyond the accuracy score

Accuracy is important, but alone and siloed, it’s shallow. What matters is how models behave under real pressure, in real environments.

Take one of our projects where we built a model to classify invoice attachments from emails. We weren’t just gunning for a flashy “high accuracy” stat. We needed to know what happened when it made a mistake. A false negative might let a risky invoice slip through. A false positive? It could delay legitimate payments and frustrate everyone involved.

That’s why we dug deep into metrics like:

• Precision – Did the model flag the right risks?
• ‍Recall – Did it catch all the ones that mattered?
• ‍F1 Score – How well did it strike a balance?

These numbers gave us a grounded view of model performance and helped set realistic expectations with stakeholders.

If you'd like to learn about how AI can help solve real delivery challenges for your business, let's talk. We're always happy to show you what practical, secure, people-focused AI looks like in action.