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      "text": "A deliberate reinvestment strategy for generative AI dividends will help lay the path for continued success.",
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      "text": "Experimentation is essential when deploying generative AI. But if you can't scale up your efforts, the high expectations for transformation revealed by our survey likely won't be met. It's fine to focus on a few use cases at first. However, the most valuable use cases will likely change over time, so it's important to focus on improving end-to-end processes, not just narrow tasks. Also, follow the example of organizations that report high generative AI expertise and consider deploying AI broadly across your enterprise as part of a holistic strategy, rather than focusing narrowly on point solutions and silos. Strive to build platform capabilities that can enable multiple use cases, accommodate new and improved generative AI models, and provide consistent governance and risk management to ensure models produce safe and trustworthy outputs and content.",
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      "text": "Generative AI projects can potentially be expensive, so leaders are naturally looking for rapid ways to achieve a compelling return on investment (ROI). Benefits might at first arrive slowly, but then ramp up quickly as your organization reaches a critical mass of experience and proficiency. Most generative AI efforts are currently focused on improving efficiency and productivity, and reducing costs. Once you achieve those goals, what will you do with the time and money saved? Will you strategically reinvest in more generative AI projects? Will you invest in training and reskilling your workforce? Will you improve your technology infrastructure? Or will you simply boost your bottom line? A deliberate reinvestment strategy for generative AI dividends will help lay the path for continued success.",
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