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  "documentTitle": "2025 The CEO s Guide to Generative AI",
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      "text": "A typical organization uses 11 gen AI models today—and expects to grow its model portfolio by ~50% within three years.",
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      "text": "ChatGPT made everyone feel like an AI expert. But its simplicity is deceptive. It masks the complexity of the generative AI landscape that CEOs must consider when building their AI model portfolio.",
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      "text": "Why so many? Because every use case comes with its own requirements and constraints. And different business problems demand different types of models.",
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      "text": "While CEOs should have teams that understand all the details about what sets different models apart, you do need to know that picking the right model for each task—each application of generative AI—matters. Knowing what drives cost, environmental impact, and business value will help you optimize the performance of your AI portfolio—and give your teams the tools they need to beat the competition.",
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      "text": "The answer is, they need to do both. And many already are. A typical organization uses 11 gen AI models today—and expects to grow its model portfolio by ~50% within three years.",
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      "text": "For example, tasks that are highly specialized, such as image editing or data analysis, need gen AI models that are trained on smaller, niche datasets. Work that is sensitive or proprietary requires gen AI models that can be kept confidential—and close to the vest. More general tasks, such as text generation, may call for gen AI models trained on the largest datasets possible.",
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      "text": "Gen AI models come in many flavors. What they can do, how well they work—and how much they cost—varies widely. Who owns the model, how it was developed, and the size of its training dataset are just a few of the variables that influence when and how different models should be used.",
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      "text": "With the massive amount of data and resources it takes to train a single large language model (LLM), the question of size is monopolizing many conversations about gen AI. As a result, many CEOs wonder whether they should scale large gen AI models for their business. Or if they should develop smaller, niche models for specific purposes.",
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      "text": "We have more than 40 proprietary AI models that we train and fine-tune for revenue teams with our customer interaction data. The results are more accurate and meaningful.",
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      "text": "We have more than 40 proprietary AI models that we train and fine-tune for revenue teams with our customer interaction data. The results are more accurate and meaningful. — Amit Bendov, CEO and Co-founder, Gong",
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      "text": "Research methodology: The statistics informing the insights in this chapter are sourced from a proprietary survey conducted by the IBM Institute for Business Value in collaboration with Oxford Economics. The survey queried 200 US-based executives across 15 industries on their perspectives regarding AI model optimization in June 2024.",
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      "text": "There's a gen AI model for that",
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      "text": "AI model optimization + generative AI",
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