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  "documentTitle": "The front-runners’ guide to scaling AI Lessons from industry leaders",
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      "text": "For example, building and maintaining multi-disciplinary teams is, on average, the greatest challenge for both front-runners and companies that are experimenting with AI, while it’s the second-greatest challenge for companies that are progressing with AI and the third greatest for fast-followers. On the other hand, building an end-to-end data foundation with quality data is the greatest challenge for companies that are progressing with AI—and the second-greatest challenge for everyone else.",
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      "text": "What are the biggest challenges that companies at different levels of AI maturity face? When we asked respondents to rank their top AI-related challenges, we discovered that there was both overlap and variation (in Figure 7, the smaller the number, the greater the challenge).",
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      "text": "Front-runners clearly offer much to emulate. Still, the fact remains that no company in our survey has scaled more than 60% of its industry-specific strategic bets. This means that there’s plenty of room for progress, all around.",
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      "text": "Source: Accenture Research. The smaller the number, the greater the challenge. “Multi-disciplinary teams” refers to the challenge of building and maintaining multi-disciplinary teams. “Data foundation” refers to the challenge of building an end-to-end data foundation with quality data. “Foundation model customization” refers to the challenge of customizing foundation models for specific purposes. “Business value” refers to the challenge of showing concrete use cases, with proven ROI. “Security risk” refers to the challenge of managing security and privacy risks.",
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