I recently had the privilege of attending the Cisco Partner Technical Exchange Conference 2025 in Bangkok, and it truly was an unforgettable experience! The event brought together Cisco representatives and partners from across the Australia and Asia Pacific regions, providing a unique opportunity to connect, collaborate, and learn from one another.

Throughout this series, we’ve explored how AI is reshaping business, not just through technology, but through how organisations work, compete, and deliver value. Yet, in many organisations, AI is still seen as an IT initiative. A digital transformation project. A technical capability that can be “rolled out” by a team somewhere downstream. In this blog, Adrian Alatsas highlights why AI is much more than a technology solution; it’s a leadership decision.

When it comes to AI, most organisations are asking the same question: “Where do we start?” The answer? Start with what’s feasible, not just what’s exciting. Your first AI initiative is more than a project — it’s a tone-setter. A successful start builds trust, momentum, and capability. But failure, over-engineering, or underwhelming results can stall progress before it even begins.

While many organisations are hesitant to adopt AI due to perceived risks and uncertainties, the greater threat may lie in doing nothing. This blog explores how delaying AI adoption can result in lost productivity, missed opportunities, and falling behind competitors who are actively building AI capabilities.

When organisations launch AI initiatives, the first instinct is often to hire a data scientist. But what they really need — and often lack — is a business translator. This critical role connects business needs with data capabilities, ensuring AI efforts are aligned with real-world problems and lead to tangible outcomes. Without this bridge between business and technical teams, even the best AI models risk becoming unused dashboards. A business translator understands strategy, speaks the language of data, and ensures outputs are actionable. While the role often exists informally today, formalising it is essential as AI becomes core to operations.

The traditional AI Proof-of-Concept (PoC) model, focused on technical validation in isolated environments, is no longer fit for purpose. Real AI value comes from solving real business problems, with real data, in real-world conditions. This blog unpacks why so many AI pilots stall and introduces a smarter approach: Business-in-Concept. By aligning AI initiatives with genuine business needs from day one, involving stakeholders early, and focusing on measurable outcomes, organisations can close the value gap and build the internal muscle required to scale AI. The takeaway? Don’t just prove the tech works — prove it matters.

Traditional ROI metrics like headcount reduction and cost savings fall short when it comes to AI. The true value of AI lies in augmenting people, accelerating insight, and enabling faster, smarter decisions. But that value is only unlocked when organisations have strong data foundations; clear, structured, and well-governed. This blog challenges outdated thinking around ROI and calls for a shift in mindset: from efficiency metrics to strategic impact measures like time-to-insight, decision speed, and risk mitigation. The path to real ROI isn’t a massive overhaul — it’s small, focused steps backed by good information management.

By Adrian Alatsas, Head of Consulting & Advisory, Logicalis Asia Pacific AI initiatives don’t fail because the models are flawed—they fail because organisations aren’t ready. This blog dives into the real blockers to AI success: poor data governance, unclear ownership, and disjointed business alignment. While many strategies start with grand roadmaps, they often skip the foundational question: Is our data even usable? The answer, more often than not, is no. But the solution isn’t to overhaul everything—it’s to start small, prioritise based on real use cases, and build “just enough” governance to create momentum. At Logicalis APAC, we focus on reality, not perfection—because your AI potential is only as strong as your data foundation.

By Adrian Alatsas, Head of Consulting & Advisory, Logicalis Asia Pacific In boardrooms and executive meetings around the world, the question “Can you show me a case study?” is often seen as due diligence—but in the realm of AI, it can become a subtle blocker of innovation. This blog explores how the demand for proven examples, while seemingly wise, may actually stall progress in one of the most transformative technological shifts of our time. It challenges leaders to rethink risk, embrace experimentation, and act decisively in an era where waiting could mean missing out.