What AI in the cloud really looks like and why it is not as glamorous as you might think

AI in the cloud is everywhere in the headlines. Futuristic case studies, bold promises, and endless hype make it seem as if every business is just one project away from transformation. But in reality, most organizations experience something much less glamorous and much more practical.

For most companies, AI in the cloud does not begin with custom models or massive GPU clusters. It starts with embedding intelligence into existing systems. That could mean improving demand forecasts, detecting anomalies in operations, or making customer support more efficient. It is not science fiction. It is incremental improvements that compound over time.

What rarely makes it into the headlines is the infrastructure underneath. Running AI workloads requires scalable compute, fast storage, and reliable networking. This is where open infrastructure matters. Platforms like OpenStack and Ceph give companies the flexibility to scale AI workloads on their own terms, while keeping control of data and avoiding vendor lock in. The success of AI in the cloud often depends less on the algorithms themselves and more on the infrastructure that supports them.

When speaking with IT leaders, the focus is often less on futuristic breakthroughs and more on everyday challenges:

• Data that is fragmented, unstructured, or locked away
• The cost of running intensive workloads at scale
• The struggle to find and keep people with the right skills
• The risk of getting tied to platforms that limit long term flexibility

These are not the headline grabbing parts of AI, but they strongly influence which projects succeed and which stall. Addressing them usually requires not only smarter models, but also an open, adaptable infrastructure that can evolve as needs change.

The organizations that often make real progress with AI are those that start small. They focus on clear business cases, build on open technologies, and rely on managed services to handle the operational heavy lifting. That balance lets their teams spend less time on maintenance and more time on innovation.

While AI today often looks modest and practical, it is not the end of the story. AI technologies are improving quickly, with better models, more efficient infrastructure, and new tools arriving every year. What seems incremental now can become transformative in the near future, especially for organizations that already have a strong foundation in place. Open source ecosystems will be critical in this evolution, because they allow companies to experiment and adopt new capabilities without being locked into rigid platforms. Preparing for that is just as important as addressing today’s challenges.

AI in the cloud is not about instant transformation. It is about practical, often invisible improvements that create lasting value. The companies that recognize this and invest in strong, open infrastructure foundations are the ones turning the promise of AI into everyday results.