With a wealth of experience evaluating cloud providers and their applications across industries, Maryanne Baines has become a leading authority on cloud technology. She joins us to demystify one of the biggest challenges facing modern enterprises: transforming their complex IT infrastructure into a launchpad for artificial intelligence. As businesses race to innovate, Maryanne provides a crucial perspective on how strategic partnerships are the key to navigating this high-stakes modernization journey.
Today, we’ll explore the common pitfalls and strategic imperatives of preparing for an AI-driven future. We’ll touch on the foundational role of a unified data strategy in overcoming silos, the importance of establishing meaningful KPIs that connect IT efforts to business outcomes, and the non-negotiable necessity of embedding compliance and security into the heart of every modernization initiative.
IDC research shows 25-30% of IT budgets go to modernization for AI readiness. From your experience, where do these funds often get misallocated? Please share some examples of hidden problems, like outdated governance, that partners can help clients identify and resolve before they derail the ROI.
It’s a fascinating and critical point. That 25-30% figure represents a massive commitment, but I’ve seen it time and again where the money flows towards the exciting, cutting-edge AI applications without first reinforcing the foundation. The misallocation isn’t about buying the wrong tool; it’s about buying it at the wrong time. A prime example is outdated governance. An enterprise might have governance procedures built for a world of monolithic mainframes, but now they operate in a hybrid environment with data spread across clouds and distributed systems. The old rules simply can’t keep up, creating bottlenecks and compliance risks that choke an AI initiative before it can even access the data it needs. A skilled partner can come in, audit these invisible structures, and highlight how a governance model from a decade ago is the real barrier to progress, not a lack of AI software.
The article notes fragmented data across mainframes and clouds is a top barrier. Can you share an anecdote about a client with significant data silos? Please walk us through the step-by-step data strategy you implemented to synchronize their data and improve visibility for their AI applications.
I remember a large financial services client that was completely stuck. They had decades of invaluable, secure transaction data locked away on a mainframe, their customer relationship data was in a modern cloud platform, and their real-time market data was in a distributed environment. Their AI team was tasked with creating a predictive analytics model, but they were flying blind, unable to get a holistic view. Our first step was simply to map everything. We worked with their teams to create a complete picture of where every piece of critical data lived. From there, we developed a tailored data management strategy—because it’s never a one-size-fits-all solution. We then guided them to a vendor whose technology was specifically designed to bridge these disparate worlds, transforming their rigid data pipelines. The result was a synchronized, secure, and accessible data ecosystem that finally allowed their AI applications to see the whole picture, dramatically improving the accuracy of their predictive models.
The content states that mature organizations focus on data cleanliness as a key KPI, seeing twice the improvement. Beyond this, what are some other high-priority metrics you advise clients to set early on? Could you provide a concrete example of how you tie these IT goals to larger business KPIs?
Data cleanliness is absolutely paramount, and that statistic about mature organizations seeing double the improvement is one I quote often because it’s so powerful. But cleanliness is just the start. I always push clients to also establish KPIs around data accessibility and data lineage. It’s not enough for data to be clean if your AI models can’t get to it quickly and reliably. It’s also useless if you can’t prove its origin for compliance audits. Here’s a concrete example: an IT team might set a goal to “reduce data access latency from our distributed systems by 50%.” That’s a good technical goal. A partner helps translate that into a business KPI: “By achieving this IT goal, our AI-powered fraud detection system can analyze transactions in real-time, which we project will reduce fraudulent losses by 15% next quarter.” Suddenly, the modernization effort isn’t just an IT cost center; it’s a direct contributor to the bottom line, making future budget discussions much easier.
Regulations like DORA and the EU AI Act are now critical. How do you guide global clients through differing regional compliance needs, especially around data lineage? Can you describe a scenario where you helped a company implement guardrails to protect sensitive data for its AI projects?
Navigating the global compliance landscape feels like a minefield for many executives. A solution that’s perfectly compliant in one region could be a major liability in another. With regulations like DORA and the EU AI Act, the stakes are incredibly high, especially for sectors like healthcare. I worked with a global healthcare provider that wanted to use AI to analyze patient data for clinical trial matching. The data was incredibly sensitive and subject to different privacy laws everywhere they operated. Our role was to help them build the “guardrails” for their AI. This involved more than just security; we implemented a robust data management system that provided full visibility into data lineage and provenance. We could trace exactly where a piece of data came from, who accessed it, and for what purpose, creating an auditable trail that satisfied multiple regulators. This wasn’t about restricting the AI; it was about enabling it to function safely and building trust with both patients and authorities.
Do you have any advice for our readers?
My advice is to resist the temptation to chase the AI hype and instead focus on your foundation. Before you invest in another powerful AI platform, ask the hard questions: Is our data house in order? Is our data clean, contextualized, and truly accessible to the applications that need it? Is it secure and compliant? Don’t go it alone. A trusted channel partner isn’t just a technology reseller; they are an architect who can help you assess your current infrastructure, draw up the blueprints for a resilient and modern data strategy, and guide you to the right solutions for your specific needs. Build the data reality first, and you will be in a powerful position to unlock the true, transformative power of AI.
