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This blog series demystifies enterprise generative AI (gen AI) for business and technology leaders. It provides simple frameworks and guiding principles for your transformative artificial intelligence (AI) journey. In the previous blog, we discussed the differentiated approach by IBM to delivering enterprise-grade models. In this blog, we delve into why foundation model choices matter and how they empower businesses to scale gen AI with confidence.

Why are model choices important?

In the dynamic world of gen AI, one-size-fits-all approaches are inadequate. As businesses strive to harness the power of AI, having a spectrum of model choices at their disposal is necessary to:

• Spur innovation: A diverse palette of models not only fosters innovation by bringing distinct strengths to tackle a wide array of problems but also enables teams to adapt to evolving business needs and customer expectations.
• Customize for competitive advantage: A range of models allows companies to tailor AI applications for niche requirements, providing a competitive edge. Gen AI can be fine-tuned to specific tasks, whether it’s question-answering chat applications or writing code to generate quick summaries.
• Accelerate time to market: In today’s fast-paced business environment, time is of the essence. A diverse portfolio of models can expedite the development process, allowing companies to introduce AI-powered offerings rapidly. This is especially crucial in gen AI, where access to the latest innovations provides a pivotal competitive advantage.
• Stay flexible in the face of change: Market conditions and business strategies constantly evolve. Various model choices allow businesses to pivot quickly and effectively. Access to multiple options enables rapid adaptation when new trends or strategic shifts occur, maintaining agility and resilience.
• Optimize costs across use cases: Different models have varying cost implications. By accessing a range of models, businesses can select the most cost-effective option for each application. While some tasks might require the precision of high-cost models, others can be addressed with more affordable alternatives without sacrificing quality. For instance, in customer care, throughput and latency might be more critical than accuracy, whereas in resource and development, accuracy matters more.
• Mitigate risks: Relying on a single model or a limited selection can be risky. A diverse portfolio of models helps mitigate concentration risks, helping to ensure that businesses remain resilient to the shortcomings or failure of one specific approach. This strategy allows for risk distribution and provides alternative solutions if challenges arise.
• Comply with regulations:The regulatory landscape for AI is still evolving, with ethical considerations at the forefront. Different models can have varied implications for fairness, privacy and compliance. A broad selection allows businesses to navigate this complex terrain and choose models that meet legal and ethical standards.

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