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  "documentTitle": "Now decides next: Getting real about Generative AI",
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      "text": "A key to value creation, scaling increases Generative AI's impact on the business and expands its user base—both of which have a strong multiplier effect on Generative AI's benefits.",
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      "text": "Off-the-shelf Generative AI solutions for common use cases such as office productivity are arguably the easiest to deploy at scale, but they still require substantial investment, effort and training. For unique and/or more strategic Generative AI solutions and use cases, the complexity and challenges increase by leaps and bounds, along with the potential for greater returns.",
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      "text": "A key to value creation, scaling increases Generative AI's impact on the business and expands its user base—both of which have a strong multiplier effect on Generative AI's benefits. Yet, many organizations find it challenging to make the leap from pilots and proofs of concept to large-scale deployment.",
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      "text": "Scaling is complex and requires effort across a variety of interrelated elements spanning strategy, process, people, data and technology. Although the challenges associated with scaling Generative AI are common to many digital transformation initiatives, issues such as risk management and governance, workforce transformation, trust and data management take on even greater importance. What worked well in the past might not work the same way with this new technology.",
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      "text": "The scaling phase is when potential benefits are converted into real-world value. It is also, however, when potential issues become real-world barriers. And with Generative AI, many of those barriers are still being identified and understood.",
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      "text": "There are always issues that emerge through the adoption and scaling transition that aren't expected—the question we have to consider is how hard are they to overcome, said a chief technology officer we interviewed. For example, [one of our] use cases had some technical, policy and cybersecurity issues, but they were relatively easy to overcome, so we scaled. Conversely, for [two other] use cases more issues emerged linked to the skill level to work with the outputs of the AI solution. These have been harder to address, so scaling has been slower.",
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      "text": "A public sector chief information officer outlined another approach: [For us, successful scaling is] building on previous successes and then taking those initiatives to another level. Expanding to other areas of the organization, incorporating more datasets, expanding the user base (internal and external) to improve upon existing results, and refining the current solution for more value. This phased approach gives us a sense of assurance the investment is worthwhile before we commit significantly more resources.",
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