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  "documentTitle": "Insights from the leading edge of generative AI adoption",
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      "text": "How can our organization use generative AI to create strategic differentiation and a competitive edge?",
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      "text": "As generative AI adoption rises and the technology becomes a standard commodity—with increased integration into common enterprise software, broader availability of specialized tools and models, and standardized data requirements—will first movers lose their advantage? To maximize the value of the technology, organizations should consciously focus on innovation and differentiation—customizing their generative AI solutions to fit their unique needs and data assets, with the goal of building capabilities that create sustainable competitive advantage. Pursuing easy opportunities and quick wins is smart, but not to the exclusion of more strategic opportunities (even though the latter will require more time and money to achieve and may take longer to achieve ROI).",
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      "text": "When developing and deploying generative AI solutions, should you buy or build? The answer depends on many factors, including your overall goals and the scale, complexity and uniqueness of your solution and use case. Are you looking to monetize your model? What is your approach to open source? How much control over training datasets do you want? Questions like these will help you choose from the broad spectrum of approaches, which include: building large language models or LLMs from scratch, fine-tuning vendor-provided models with your own data, or using enterprise software with generative AI built in. Each approach has its benefits and drawbacks, and you might end up choosing more than one. When deciding, be sure to consider your business strategy, desired investment level, risk tolerance and data readiness.",
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      "text": "Next: Looking ahead",
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