Turning AI Ambition Into Operational Reality

Albert Yuen, Director - National Data & Analytics, Aldi Stores Australia

Albert Yuen, Director - National Data & Analytics, Aldi Stores Australia

Albert Yuen is a senior data and analytics leader at ALDI Stores Australia with over two decades of experience. He drives data-driven culture, oversees national analytics strategy and enables cross-functional teams to deliver insights, innovation and measurable business performance outcomes. Enterprise AI success will be determined less by model sophistication than by the operational discipline leaders bring to data, governance and organizational change. This perspective reinforces that lasting competitive advantage comes from treating data as enterprise infrastructure while earning the trust needed to scale transformation across the business. Underestimated Technology and Operating Shift I believe most organizations are held back in their AI, Analytics & BI efforts due to poor data governance and underinvestment in data engineering. There’s a strong focus right now on generative AI and new tools, but in many cases these are being layered on top of inconsistent data, limited governance, and underinvested data engineering. When that happens, you don’t just limit the value—you can actually accelerate the spread of low-quality data and reduce trust in the outputs. This becomes more important as we move toward more automated and agent-driven workflows. These systems rely on well-structured, clearly defined data. Without that, you simply automate bad decision-making. This is often the case, as it is so hard to explain to C-suite leaders (outside of banking, insurance and FAANG companies) what data governance is, what properly constructed datasets and data monitoring looks like and why we need it. All C-suite leaders should increase their awareness and sensitivity to the conditions under which the well-publicized benefits of AI can actually be reaped. More often than not, strategy and purpose are confused into a narrow definition of value delivery, and that leads to a familiar pattern—strong intent, a number of successful pilots, but challenges when it comes to scaling consistently across the organization. If there’s one shift I’d suggest, it’s to treat data as critical infrastructure or strategic enabler and dedicate the right focus to it. The organizations that do this will be much more effective at turning AI investments into sustainable, enterprise-wide outcomes. Momentum versus Resistance in Change Programs The short answer is people. If I reflect on the change programs I’ve been part of — both successful and less successful — the ones that built momentum versus those that ran into resistance, it comes down to a few things: how trusted you are in listening to stakeholders, how credible your value claims are, and how well people understand and buy into the change. Every program needs a different combination of navigating the organization and aligning the right people. There isn’t a standard formula, which is why it’s often more art than science. I’ve seen this play out firsthand. In one case, a strategy shift was led by a leader who said all the right things, involved the right stakeholders, and aligned to strategy and values. The work was done, but it never quite landed and struggled to build momentum. Some months later, the same message was picked up by a different leader. The content hadn’t changed, but this person had more history with the business and stronger personal charisma — and the change was adopted. It’s often difficult to pinpoint exactly why, but it reinforces the point: momentum is rarely just about the message. It’s about who delivers it and how much trust they carry. Prioritizing Competing Investment Demands

This is a constant in all executive roles, and age-old sayings remain true for data & analytics: 'if you want to go quickly, go alone. If you want to go far, go together'.

Treat data as critical infrastructure or strategic enabler and dedicate the right focus to it. The organizations that do this will be much more effective at turning ai investments into sustainable, enterprise-wide outcomes.

Truly mission-critical risks notwithstanding, everything else is a trade-off in capacity and priority. My mindset is that I am a leader of a grocery retailer and data, analytics and AI are simply the tools or mechanisms through which I contribute. What's important is to stay connected to the business and prioritize in line with what the business wants to achieve. This starts with our Data & Analytics strategy that is purposely aligned to what we want to achieve in country on one hand and balanced with functional priorities around platforms or AI on the other. Leadership during Uncertainty I have learned that especially during uncertainty, it is the leader's role to see beyond the immediate threats and keep the team calm, settled and focused. The really difficult part is understanding whether the uncertainty affects any assumptions that underpin your strategy or direction. Where it does, tactfully make adjustments while aligning with the business and keeping the team informed without panic. In this case, the 'how' looks somewhat easy, but the 'what' in the moment, in deciding what to change, if anything, and to sequence tactfully, is very difficult to do. Building Credibility beyond Job Title The work I do and the experience I have are focused around grocery retail and FMCG, so my advice will likely not apply if the career one wants is in a FAANG or if you want to lead frontier projects in Google Labs, for example. I learned recently of this term 'T-shaped professional ', and without knowing it, I had modeled my career and my mindset around it. The idea is that everyone needs deep expertise in something, whether data & analytics, data science, software engineering or, in my case, finance and commercial strategy. This is the deep vein of expertise that gets you in the room and makes you valuable to those around you. But as you progress, it is about broadening one's understanding beyond one's field to understand the operating constraints and practical considerations of other parts of the organization. This is where the cliché 'learning the business' makes sense, but does not negate that one needs to start with that deep vein of expertise. The transition from deep to broad is a tricky one and differs role by role, but if you seek to lead cross-functional projects and enterprise deliverables, broadening is the answer. I've met many business leaders, and the feedback towards technology leaders is largely the same: 'I trust them within the technology domain, but I'm hesitant for them to help solve my strategic or commercial challenges'. I've also seen many examples of marketing leaders transitioning to operations, CFOs taking on COO or CEO roles, operations leaders branching out into commercial domains, but rarely do I see technology leaders trusted enough to step beyond their remit - and this does not have to be the case. I believe GenAI has closed the gap between technology and the business and will demand something different from technology leaders moving forward - an integrated view of how technology shapes the business in strategic and operational aspects, and the best of us will be asked to lead from the front and use the tools we have to solve for the business's most pressing challenges. So for tech leaders who want to build credibility and influence beyond their title, for me it's simple: learn the business; when opportunities come up, work on the warehouse project in the warehouse; experience operations; experience for yourself how solutions you implement are used by the team on the ground.

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Turning AI Ambition Into Operational Reality

Treat data as critical infrastructure or strategic enabler and dedicate the right focus to it. The organizations that do this will be much more effective at turning ai investments into sustainable, enterprise-wide outcomes.

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Critical Infrastructure

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