AI’s Promise to Profits: How IBM is Maximizing Business ROI with Accelerated Computing

Artificial intelligence (AI) is rapidly transforming industries, but its true potential lies in how well businesses can integrate it into their infrastructure. At NVIDIA’s GTC 2025, the session “AI Promise to Profits: Maximize Business ROI with Accelerated Computing” (Session S74483) shared some solid insights on how businesses can tap into the potential of AI using hybrid cloud architecture and better infrastructure. Hillery Hunter, CTO of IBM Infrastructure, Briana Frank, VP of IBM Cloud PaaS and Platform Product Management and Design, and Chandra Ravi, Senior Director of Distributed Systems Engineering at Visa, led the discussion. They talked about how businesses can boost ROI by improving efficiency, managing data better, and keeping things secure with good governance. Key points included making the most of GPU resources, weighing the pros and cons of open-source vs. proprietary AI models, and dealing with AI ethics. The session also looked at how AI is making an impact in areas like customer support and HR, with a focus on saving costs and speeding up AI adoption while keeping everything compliant and secure.

Keys to Business Outcomes with AI

For AI to make a real impact on business results, companies need to create a hybrid platform that strikes the right balance between flexibility and customization. When choosing AI models, businesses face a choice between open-source models, which allow more flexibility, and proprietary models, which are fine-tuned for specific tasks. But often, smaller, more specialized models can actually deliver better results than large, general models, providing more precise outcomes.

The real power of AI comes in how it’s customized. Companies need to integrate their data into AI frameworks while ensuring proper governance. This helps keep models accurate, reduces bias, and ensures everything aligns with the company’s goals. Without solid governance, AI projects can suffer from inefficiency, inaccuracies, or even security risks.

Infrastructure’s Role in Realizing AI Outcomes

For AI to work well at scale, scalability is key. To get the best performance, making good use of GPUs is essential. Creating a direct link between GPU memory and storage can speed up the process of training AI models, helping businesses get better results faster.

Managing large datasets is also crucial. Known as data fusion, this process allows businesses to handle both structured and unstructured data more effectively. AI solutions need to be able to process and analyze these different kinds of data to draw meaningful insights. A flexible infrastructure makes it easier to use AI across various IT setups.

The Benefits of a Hybrid Cloud Approach

Hybrid cloud solutions provide businesses with the flexibility they need while keeping security and data sovereignty in check. Since AI often works with sensitive data, it’s important to manage where and how that data is stored. Hybrid cloud frameworks offer a good balance between performance and compliance, making it easier and safer to adopt AI.

Using a hybrid cloud approach can speed up AI productivity, lower operational costs, and still maintain strong performance. Tools like OpenShift AI and RHEL AI create a solid foundation for deploying AI solutions across different environments. This means companies can deploy AI models at scale without sacrificing security or performance.

Red Hat AI InstructLab: Simplifying AI for Enterprises

Red Hat AI InstructLab helps align large language models (LLMs) with the specific needs of businesses. Adopting AI can be tricky, especially when it comes to security and the risk of “catastrophic forgetting,” where models lose what they’ve learned over time. InstructLab helps by ensuring businesses have full control over their data and models, addressing these risks.

As a service, InstructLab ensures security, availability, and continuous updates, making it easier to train and deploy AI models. This allows companies to scale AI securely and efficiently.

A Practical Use Case: AI for Marathon Running Performance

To show what InstructLab can do, a practical experiment was done focusing on marathon running performance. The study explored how LLMs could grasp specialized terms related to running, shoes, and individual training data. The AI model was trained with running-related terminology, generating synthetic data to personalize its recommendations. For instance, it could suggest the best shoes for tempo runs based on a person’s performance, demonstrating how AI can go beyond generic answers to provide tailored advice.

Model Alignment: The Key to Personalized AI

The process of model alignment separates basic AI models from more specialized ones. General AI models provide broad, generic answers, while aligned models offer specific, context-aware responses. For example, an aligned model could recommend running shoes based on a runner’s performance, whereas a general model would just list all available options.

Synthetic Data Generation (SDG) plays an important role in this. By generating and validating training examples, SDG helps refine the AI model’s understanding of specific areas. This allows businesses to fine-tune their AI even when real-world data is scarce or difficult to gather.

IBM Consulting: Helping Businesses Navigate AI Adoption

As AI continues to change industries, many businesses look to consulting services for help with integrating AI into their operations. AI adoption comes with challenges, like workforce training, strategy development, and deployment. Many companies turn to experts for advice on how to tackle these challenges and make the most out of AI.

Consulting services offer support from strategy development to governance management and model deployment. As AI projects keep growing, the need for expert guidance in these areas will only increase.

Looking Ahead: AI Innovations and Future Plans

There are plenty of exciting AI innovations lined up for future events, aimed at helping businesses drive AI-powered transformation. These advancements are focused on scaling infrastructure, adopting hybrid cloud architectures, and fine-tuning model alignment to achieve real business results.

Research and development efforts in AI will continue pushing boundaries, ensuring that AI keeps evolving and making an impact across industries.

AI as a Business Accelerator

The right mix of hybrid cloud solutions, scalable infrastructure, and model alignment is key to unlocking AI’s full potential. These technologies let businesses use AI effectively while keeping data secure and under control.

As AI continues to evolve, companies that strategically implement these technologies will be in a stronger position to innovate and improve business outcomes. By focusing on AI’s technical strengths and aligning it with their specific needs, businesses can harness its transformative power to improve their operations.

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Author

Bernice Lottering