The PDF is viewable here [0]; the link above doesn't take you there.
And from that PDF, I'm not seeing anything that is incongruent with what is stated in TFA:
From TFA:
> To be precise, the report states: "The GenAI Divide is starkest in deployment rates, only 5 percent of custom enterprise AI tools reach production." It's not that people aren't using AI tools. They are. There's a whole shadow world of people using AI at work. They're just not using them "for" serious work. Instead, outside of IT's purview, they use ChatGPT and the like "for simple work, 70 percent prefer AI for drafting emails, 65 percent for basic analysis. But for anything complex or long-term, humans dominate by 9-to-1 margins."
From PDF:
> Tools like ChatGPT and Copilot are widely adopted. Over 80 percent of organizations have explored or piloted them, and nearly 40 percent report deployment. But these tools primarily enhance individual productivity, not P&L performance. Meanwhile, enterprise-grade systems, custom or vendor-sold, are being quietly rejected. Sixty percent of organizations evaluated such tools, but only 20 percent reached pilot stage and just 5 percent reached production. Most fail due to brittle workflows, lack of contextual learning, and misalignment with day-to-day operations.
And from that PDF, I'm not seeing anything that is incongruent with what is stated in TFA:
From TFA:
> To be precise, the report states: "The GenAI Divide is starkest in deployment rates, only 5 percent of custom enterprise AI tools reach production." It's not that people aren't using AI tools. They are. There's a whole shadow world of people using AI at work. They're just not using them "for" serious work. Instead, outside of IT's purview, they use ChatGPT and the like "for simple work, 70 percent prefer AI for drafting emails, 65 percent for basic analysis. But for anything complex or long-term, humans dominate by 9-to-1 margins."
From PDF:
> Tools like ChatGPT and Copilot are widely adopted. Over 80 percent of organizations have explored or piloted them, and nearly 40 percent report deployment. But these tools primarily enhance individual productivity, not P&L performance. Meanwhile, enterprise-grade systems, custom or vendor-sold, are being quietly rejected. Sixty percent of organizations evaluated such tools, but only 20 percent reached pilot stage and just 5 percent reached production. Most fail due to brittle workflows, lack of contextual learning, and misalignment with day-to-day operations.
0: https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Bus...