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The data center industry is advancing at an unprecedented pace, continuously adapting to shifting demands, emerging technologies, and evolving operational requirements. At Apolo, we see one transformative force leading the charge: Artificial Intelligence (AI). AI might not yet solve every challenge, but its capacity to deliver real-world value is becoming clear. The question is no longer if AI will reshape industries, but how.
Conversations about AI in data centers often center around power, density, cooling solutions, and new architectures—vital topics for data center operations. As demand for AI-ready infrastructure grows, data centers must evolve to handle complex GPU configurations and more efficient cooling systems. Sustainability is also key, with increasing pressure to reduce carbon footprints across the board.
But while the physical infrastructure of AI is important, a key element often goes under-discussed: the businesses relying on this infrastructure and, most critically, how they’re turning it into revenue.
Data centers and AI infrastructure represent major investments. Without a clear return on investment (ROI), there’s little incentive for data centers to commit to AI-ready architecture. That’s why we at Apolo look beyond the physical infrastructure, focusing on real-world use cases that showcase the powerful impact of AI today. With a front-row view into innovative AI workloads through partnerships with Scott Data Center, NovoServe, iMHPC, Cato.Digital, WWT, and others, Apolo sees how AI is already generating tangible revenue. Here are two compelling examples.
Use Case #1: Community Banking – Revolutionizing HR with AI
One of our clients, a community bank, faced a common challenge in regulated industries: the cloud wasn’t an option. Privacy concerns and high costs meant the bank needed to keep its operations on-premises. Using Scott Data Center’s GPUaaS (GPU as a Service) architecture and Apolo’s MLOps platform, the bank established a secure, private AI-ready environment to build and deploy advanced AI models.
The bank applied this infrastructure to transform its HR operations. Previously, employees needed to navigate multiple internal portals to retrieve documents or check their PTO—an inefficient process. By implementing a PrivateGPT architecture with Retrieval-Augmented Generation (RAG), the bank integrated AI into HR, creating a seamless, responsive experience.
Now, employees simply ask the HR AI engine for their PTO balance or request documents, receiving immediate responses. This shift didn’t just enhance efficiency; it redefined the department. HR became more agile and intelligent, allowing employees to focus on their core work instead of administrative tasks, boosting both productivity and satisfaction.
This example demonstrates how AI, when strategically deployed, can streamline business processes in unexpected areas. And this transformation is happening today, not in a distant future.
Use Case #2: Agricultural Innovation – Spotting Disease Faster with AI
Another Apolo client, a major agricultural company in Nebraska, illustrated how AI can solve critical real-world challenges. In agriculture, early disease detection is paramount, especially given that a single cow can be worth thousands of dollars. A disease outbreak can spread quickly and jeopardize millions in assets.
The company already had monitoring cameras and data collection tools, but running disease detection models still took weeks—far too long. By migrating part of their dataset and AI models to Scott Data Center and leveraging GPUaaS for accelerated processing, the company reduced model run times from weeks to mere hours. Now, they can detect potential outbreaks almost immediately, safeguarding both herd health and financial stability.
The ROI was profound. With rapid, AI-driven insights, the company could act within hours rather than weeks, preserving livestock health and protecting their bottom line. This same approach is now being applied to monitor potato crops, enabling real-time response to potential issues.
These aren’t theoretical use cases; they are mission-critical applications directly impacting revenue.
AI in Action: Driving Real-World Revenue and Impact
These examples are part of a larger trend. Increasingly, companies are adopting AI to address practical, revenue-generating challenges. From transforming HR to safeguarding agricultural assets, AI is becoming a core tool in the modern business toolkit.
At Apolo, we help data centers capture this growing AI market. As the industry’s only true multi-tenant MLOps platform, Apolo empowers data scientists with the tools to manage and scale AI workloads efficiently. By enabling data centers to support revenue-generating AI applications, Apolo ensures that infrastructure investments translate into measurable business outcomes.
The takeaway: AI is not just a futuristic concept; it’s here, making a real impact. If you’re in the data center or AI infrastructure space—or simply exploring how AI can enhance your business—now is the time to take notice.
We’re excited to lead this journey. If you’re interested in learning more about our work or discovering how clients are leveraging AI, don’t hesitate to reach out. Let’s connect!
