Here's what I keep hearing from leaders across industries: "We know AI works. We're just not sure we can trust it at scale."

That gap between what's possible and what's actually deployable is where most transformation efforts die. We can build impressive pilots, but production stalls. We can automate individual tasks, but complexity compounds faster than value. We produce brilliant research in labs, yet barely 5% ever reaches the real world.

The pattern is everywhere. It's not an innovation problem anymore. It's a translation problem.

This edition digs into what translation actually requires and why it breaks down at the same points. The articles here come from very different perspectives, but they're all wrestling with the same core challenge: how do you move from proof-of-concept to sustainable scale?

What strikes me most is how often the breakdown happens not because the technology fails, but because we're trying to scale the wrong thing. We pursue growth when we need scale. We add tools when we need to redesign work. We automate when we need to orchestrate.

I'm particularly grateful to Professor Shonali Krishnaswamy, Director of the Monash AI Institute and Associate Dean (Innovation) at Monash's Faculty of Information Technology, for contributing her perspective on the innovation value chain. 

Our partnership with Monash is built on exactly this problem: how do you bridge world-class AI research with real-world deployment? Shonali's article captures something we see every day: breakthrough innovations stalling not because they don't work, but because the connective tissue between lab and market doesn't exist.

That's what translation infrastructure looks like: governance that works at execution speed, workflows designed for AI from the ground up, and partnerships that connect capability to deployment.

So, as you read, ask yourself: Are we building for translation, or just for innovation? Do we have the infrastructure that lets intelligence travel across our systems? Can we move from brilliant to deployed, or are we still just admiring the research?