Okay, got the report. I would read it yourself. I am not sure how generalizable the findings are based on the methodology (52 interviews, convenience sampled, failed apparently means no sustained P&L impact within six months but no coding explanation). https://
mlq.ai/media/quarterl
y_decks/v0.1_State_of_AI_in_Business_2025_Report.pd
…
ENTERPRISE AI
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Questioning AI Business Report Methodology and Generalizability
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Methodological Concerns in 2025 AI Business Report Analysis
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Okay, got the report. I would read it yourself. I am not sure how generalizable the findings are based on the methodology (52 interviews, convenience sampled, failed apparently means no sustained P&L impact within six months but no coding explanation). https://
mlq.ai/media/quarterl
y_decks/v0.1_State_of_AI_in_Business_2025_Report.pd
… -
Project NANDA Report: Understanding 95% AI Pilot Failure Rate
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Has anyone seen a copy of the Project NANDA report on 95% of AI pilots failing? It has been reported about widely, but I haven’t been able to actually read it, though I filled out a form to get access. I want to understand what they found and how they measured it. Any links?
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Think SMART Framework Balances AI Accuracy Latency ROI
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The 𝗧𝗵𝗶𝗻𝗸 𝗦𝗠𝗔𝗥𝗧 framework helps enterprises strike the right balance of accuracy, latency and ROI when deploying AI at AI factory scale. Learn more: https://
nvda.ws/41MTqTc -
Enterprise AI Infrastructure: Scaling Models and Inference Demands
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𝗦cale and complexity Bigger models = greater inference. From quick queries to million-token reasoning, infra demands during inference are soaring. Enterprises are building new AI factories with partners like @CoreWeave
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NVIDIA Inference Platform: Balancing Accuracy, Latency, and Cost
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𝗠ulti-dimensional performance Optimal Inference is a trade-off: accuracy, latency, and cost. Some tasks need ultra-low latency (real-time translation), while others prioritize throughput (multi-million-token queries). The NVIDIA Inference Platform accelerates models
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Smart Inference Deployment: Scaling AI Across Enterprise Systems
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Deploying AI at scale requires you to 𝗧𝗵𝗶𝗻𝗸 𝗦𝗠𝗔𝗥𝗧 about inference: 𝗦cale and complexity 𝗠ulti-dimensional performance
𝗔rchitecture & software
𝗥OI driven by performance
𝗧echnology ecosystem & install base Let’s break it down -
UBS Invests in Domino Data Lab for Enterprise AI
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@UBS and @DominoDataLabData are expanding our partnership, backed by a UBS equity investment, to accelerate this transformation with AI that’s both fast and safe. https://
domino.buzz/45HpMQu #EnterpriseAI #AIInnovation #AIGovernance #FinancialServicesAI -
Enterprise AI Infrastructure Scaling for Large Model Inference
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𝗦cale and complexity Bigger models = greater inference. From quick queries to million-token reasoning, infra demands during inference are soaring. Enterprises are building new AI factories with partners like @CoreWeave
, @Dell
, @googlecloud and more. -
Command A: Secure Agentic AI Platform for Daily Work
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Command A Reasoning powers North, our secure agentic AI platform. It excels in daily work like drafting and summarizing content, research and data analysis, scheduling tasks, and more.