Keeping up with tech means facing a constant influx of AI. AI tools can accelerate customer support, automate workflows, and even generate predictive analytics with efficiency, boosting innovation to maintain a competitive edge. There’s enormous potential in AI, but without a strong foundational layer, it remains just that: potential.
Many organizations are caught in the AI hype without addressing the fundamental issues in their tech stack. Enter: the cloud.
The temptation to leapfrog to advanced tools is understandable, and many IT leaders are facing increasing pressure from higher-ups seeking competitive advantage. Every headline proliferates the promise of AI, and conference keynotes hint at existential risk for those that don’t implement it fast enough.
But the reality is more sobering: you can’t shortcut your way to transformation. If you’re still stitching together siloed data from multiple tools, or waiting days to generate usable reports, AI won’t save you. It could, in fact, magnify those inefficiencies.
Not a backend upgrade. Not an IT checkbox. Cloud adoption, when done strategically, empowers transformation like the all-coveted AI tools and can fundamentally rewire your business. Bringing your business on the cloud breaks down silos, accelerates decision-making, and gives teams what they need to adapt in real time.
In short: the cloud sets the stage for AI to actually matter.
We often think of the cloud as a means to an end — a way to reduce server costs or shift to SaaS applications. But the organizations seeing the biggest payoffs have recognized the cloud for what it is: a strategic enabler of new ways of working. A cloud-first enterprise isn’t just one that’s modernized its tech stack, but one that’s rethought how it delivers value across every layer of the business.
Cloud-first is business-first
Any transformation comes at a cost, be it fiscal or operational. While migration might have an upfront price tag, a study from IDC found organizations migrating to cloud infrastructure reported a 318% five-year ROI. Beyond that, Forrester reported a 25% reduction in annual IT costs. These cost savings enable agility, scalability, and flexibility for investments in beneficial AI tooling.
Importantly, those savings often come with greater transparency and control. In traditional environments, costs are buried in capital expenditures or sprawling maintenance contracts. In the cloud, spend is tied to usage, meaning CIOs and CFOs alike can make more informed decisions, faster.
And improvements aren’t limited to costs. McKinsey found that 70% of businesses experienced increased speed and responsiveness after moving to the cloud. With the cloud, companies can launch new workflows and scale existing ones to future-proof their businesses and speed up time to value. As business units across an enterprise adopt the same tools and migrate to a centralized system, processes and workflows become more streamlined and teams collaborate more easily. Teams can align on shared goals to meet business objectives, instead of being stalled by a lack of communication and disconnected tools.
Take the example of a global financial services firm that recently migrated to a cloud-native environment. Within 18 months, they reduced the number of redundant tools by 40%, improved their go-to-market timelines by several weeks, and empowered teams in six countries to access real-time reporting dashboards. No additional AI was required, just infrastructure that worked together.
Houses aren’t built by starting with the roof; nothing gets built without a strong foundation. It’s the same for digital transformation: the promise of AI only becomes real if it’s built on the speed, scale, and flexibility of a cloud-native environment.
Transformation and not just tech
Cloud transformation does far more than just shift off legacy systems. It rethinks how your business operates to create lasting change.
This shift is cultural as much as it is technical. It demands new ways of working fast-paced, feedback-driven and collaboratively. Organizations that succeed with cloud don’t treat it as an “IT project,” but as a company-wide opportunity to rethink how decisions are made, how success is measured, and how customers are served.
With rapid reporting, continued updates, the ability to tailor solutions, and improved cross-company communication, cloud-based systems can reduce recurring enterprise headaches and tool sprawl. Manufacturers use it to reduce sign-off delays across time zones. Product teams use it to improve launch velocity. Leaders use it to align faster around changing market conditions.
In one case, a large healthcare provider migrated its internal systems to the cloud and restructured its workflows accordingly. The result? Faster response times, better patient outcomes, and a dramatic reduction in the manual processes that had previously bogged down operations.
This is the shift that matters: not just “IT in the cloud,” but the entire business to unlock the next wave of value.
AI is the destination — not the journey
Investing in AI is one of the last steps in the overall journey, not the entirety of it. Prosaic as it is, AI tools don’t work well without clean data pipelines, interoperable systems, a clear vision, and the cloud’s agility.
Because of this, most AI pilots stall out. Even with the insurmountable benefits, many companies remain on-prem — data silos, and disconnected systems included. If your infrastructure can’t scale dynamically, then your AI strategy is already compromised.
The warning signs are easy to spot: dashboards that never load, systems that fail under pressure, AI models trained on stale or incomplete data. The root cause isn’t bad AI, it’s infrastructure that wasn’t built to support it.
No one is saying not to invest in AI. But the smartest organizations know that cloud transformation is key to capitalizing on that investment.
And it’s not just a matter of plugging AI into a cloud environment. The cloud makes it easier to orchestrate data, automate updates, and respond to customer needs in real-time which are all essential ingredients for AI to function effectively. Cloud-native architecture also enables iterative experimentation, a vital aspect of AI development that traditional infrastructure simply cannot support.
AI is the destination, and cloud is the thoroughfare.
By Emmnauel Benoit