When Jamie Pompu, CIO of Tressl, spoke at IEEE Smart World Congress Industry Day in Calgary, he posed a challenge: everyone wants intelligence, but few are ready for it. These are the highlights from his talk on what it really takes to prepare for AI.

Everywhere you look today, the conversation is about artificial intelligence. It’s in every keynote, every product roadmap, every pitch deck. And for good reason: AI is no longer a future concept, it’s here, and it’s advancing at remarkable speed.

But here’s the truth: intelligence can’t be bolted on. The only way to achieve it is through great data and a solid foundation. Without that, AI becomes just another layer of complexity on top of broken systems.

The Hype vs. the Reality

Smart cities. Digital twins. Automated workflows. These aren’t just buzzwords anymore, they’re reshaping industries at unprecedented speed.

And yet, when you pull back the curtain, the picture looks very different. Many organizations are still running on spreadsheets, email chains, manual approvals, and even clipboards in the field. These chaotic, unstructured processes are the same systems we expect AI to make better. But pouring AI into that environment doesn’t solve the problem, it simply magnifies it.

The Hidden Problem: Garbage In, Garbage Out

Just like people, it can only learn from the quality of what it’s taught.

Imagine being in a classroom with a disorganized teacher: the lessons are inconsistent, riddled with mistakes, and sometimes skipped altogether. How much would you actually learn? That’s exactly what happens when AI is fed incomplete, unstructured, or unreliable data.

The saying holds true: garbage in, garbage out. If the inputs are broken, the outputs will be too.

The Mandate Trap

Executives often get the directive: “Bring in AI.” It feels urgent, especially as competitors announce their AI strategies. But this isn’t the first time we’ve seen this cycle.

In the 2000s, Agile was the savior of software development. In the 2010s, Cloud was the answer to every IT challenge. Both brought incredible advances but when layered on top of shaky foundations, they created as many problems as they solved.

The same is true for AI. You can’t build a skyscraper on sand. It might look impressive on day one, but under real-world pressure it will crack and collapse.

The Data Story That Proves the Point

Recently, Jamie worked with a client who wanted to predict equipment wear using years of sensor data. They had what looked like a dream dataset: millions of rows, well-structured and complete.

But because of a strong preventative maintenance program, parts were being replaced long before they failed. The result? The model didn’t have enough examples of failure to learn from. Despite all that great data, accuracy topped out in the mid-60s.

The system wasn’t broken, but the foundation wasn’t right. Even with millions of data points, they didn’t have the right data.

What Actually Moves the Needle

At Tressl, we work with clients who operate critical infrastructure, the backbone that makes AI possible. What we’ve found is this: most organizations don’t need AI first.

They need clarity and control.

• Clarity in how applications are submitted.
• Control in how decisions are made.
• The ability to coordinate across organizations while keeping everything auditable and trackable.

That’s the real work. It’s not flashy, but it’s the foundation that makes intelligence possible.

Proof It Works

By focusing on clarity and structure first, Tressl has helped clients:

• Clear 200+ backlog requests in just 3 days (Telecom)
• Achieve 7.5x fater reviews (Telecom)
• Absorb 125% growth with no new hires (Energy)
• Cut turnaround times from 8 weeks to minutes (Utility)

These results didn’t come from layering AI on top of broken workflows. They came from fixing the foundation and creating an environment where AI can thrive, while delivering real value today.

Looking Ahead: The Work Before the Intelligence

The unspoken truth is that it’s the boring stuff; the approvals, the billing workflows, the version control, that makes intelligence possible. Everyone wants skyscrapers; no one wants to talk about the plumbing. But intelligence isn’t built at the top. It’s built at the root.

At Tressl, we made a conscious decision: not to build just another infrastructure tool, but to build the foundation for clarity, control, and systems that actually work. Because when the groundwork is right, intelligence isn’t just possible, it’s powerful.

Final Thought

The organizations that win with AI will be the ones that invest in readiness, creating the clarity and control that make intelligence work. Intelligence starts with clarity and that’s where true transformation begins.

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