AI transformation isn’t a technology project, it’s a leadership challenge. Success depends not just on models or infrastructure, but on mindset: the ability to work with what you have, move fast, and empower people to innovate responsibly.
Few leaders embody this more than Andy MacMillan, CEO of Alteryx, a company redefining how enterprises harness data and AI. On a recent episode of Data & AI Mastery, hosted by Dr. Raoul-Gabriel Urma, Andy shared his playbook for building AI that works in the real world, where data is messy, systems are old, and time is short.
The conversation offers a refreshing reminder that the future of AI isn’t about waiting for the perfect foundation. It’s about building alongside what already exists.
For many organisations, the instinct is to “bolt AI onto” existing systems, hoping to modernise through integration. But as Andy explains, that approach rarely works.
“Your legacy apps are not expecting to feed AI,” he says. “Instead of trying to build everything on top of them, start building alongside, create agents that complement what you already have.”
This idea of building alongside reflects a critical mindset shift. Rather than forcing new technologies into rigid architectures, companies can design lightweight AI agents that use existing data pipelines to deliver fast, measurable value.
It’s a crawl–walk–run approach: start with small, well-defined problems where AI can deliver insight or automation, then expand gradually. This is how innovation scales sustainably, by layering intelligence next to existing systems, not inside their constraints.
A recurring theme throughout the episode is Andy’s realism. He knows that no company has perfect data. But waiting for data perfection, he argues, is a recipe for inertia.
“Don’t wait for all your data challenges to be solved, that’ll take a long time,” Andy says. “Start solving discrete problems. Work with what you have.”
Alteryx has built its reputation on helping “everyday analysts” unlock the power of data without needing to be engineers. That ethos extends into AI. The key is empowering people who understand the business to automate repetitive work, clean data on the fly, and generate insights that drive outcomes.
Whether it’s reconciling budgets, optimising marketing pipelines, or surfacing operational risks, the goal is to start where value already exists and let AI scale from there.
For Andy, the future of AI isn’t about replacing people, it’s about augmenting them. One of the biggest opportunities he sees is conversational AI making analytics accessible to everyone.
Many employees aren’t “data people.” They don’t write SQL queries or build dashboards. But they ask questions, questions that data can answer.
“If AI can use data to provide an informed answer,” Andy notes, “that’s incredibly powerful. It helps people who might never have gone to a BI dashboard get the insight they need.”
This is the democratisation of analytics in action: removing barriers so that data fluency spreads beyond specialists to every corner of the organisation.
Empowerment doesn’t mean chaos. As AI becomes more embedded in business processes, responsibility must scale alongside it.
At Alteryx, Andy and his team developed what they call the AI Data Clearinghouse, a governance framework that ensures all AI initiatives use data ethically, securely, and transparently.
Before any internal data is used with AI systems, it passes through a review process led by leaders from finance, legal, privacy, and information security. The goal isn’t to slow down innovation, but to channel it responsibly.
“We want everyone in the company to think about how to use AI,” Andy explains. “But not everyone gets to decide what data goes where. There’s a process for that.”
This balanced approach, encouraging experimentation while maintaining guardrails, is what distinguishes sustainable AI adoption from short-lived hype.
The conversation also highlights how data and AI translate into tangible results. One standout example: McLaren Racing, a long-term Alteryx partner, uses data automation to optimise car performance, reliability, and supply chain decisions.
With over 1.8 billion data points informing every race, McLaren’s data-driven mindset exemplifies what’s possible when analytics meets real-time execution. It’s not just about dashboards, it’s about competitive advantage.
For other organisations, ROI might mean faster financial reporting, more efficient operations, or freeing employees from manual tasks. But the underlying principle remains the same: data + AI = better outcomes, faster.
As AI accelerates, upskilling becomes non-negotiable. Andy encourages leaders to start small, learn quickly, and help their teams experiment.
He leads by example, still coding on weekends, building GPTs to analyse sales pipelines, and exploring tools firsthand. It’s a powerful signal to employees that AI literacy starts at the top.
Leadership isn’t about knowing every technical detail; it’s about curiosity, courage, and enabling teams to grow with the technology.
Andy MacMillan’s message is clear: the future of AI isn’t about chasing perfection, it’s about progress. Build alongside, not on top. Work with the data you have. Empower people. Govern responsibly.
AI mastery begins with action.
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