{
  "schema_version": "1.1",
  "id": "archive:https://openai.com/index/how-enterprises-put-ai-to-work",
  "slug": "how-enterprises-put-ai-to-work-0p0rqih",
  "url": "https://feed7.dev/p/how-enterprises-put-ai-to-work-0p0rqih",
  "title": "From assistance to execution: How enterprises put AI to work",
  "why_included": "OpenAI’s research frames enterprise AI adoption as a shift from assistance toward agent execution, with ChatGPT and Codex as examples rather than implementation guidance.",
  "summary": "OpenAI reports that enterprises are adopting **agentic AI** through products including **ChatGPT and Codex**, and says a group it calls frontier firms is moving ahead in adoption.",
  "practical_implication": "Builders should treat this as evidence of organizational demand for agents that execute work, then look for the underlying research before changing product or deployment plans.",
  "agent_context": "OpenAI reports that enterprises are adopting **agentic AI** through products including **ChatGPT and Codex**, and says a group it calls frontier firms is moving ahead in adoption.\n\nBuilders should treat this as evidence of organizational demand for agents that execute work, then look for the underlying research before changing product or deployment plans.\n\nThe supplied material provides no sample size, methodology, adoption rates, workflow examples, or definition of a frontier firm, so the size of the claimed gap is unclear.",
  "source": {
    "name": "OpenAI",
    "url": "https://openai.com/index/how-enterprises-put-ai-to-work",
    "published_at": "2026-08-12T06:00:00.000Z"
  },
  "source_class": "blog_post",
  "content_type": "Official Release",
  "layer": "industry",
  "domains": [
    "coding"
  ],
  "topics": [
    "adoption",
    "enterprise"
  ],
  "verification": {
    "status": "official_source",
    "label": "Official Source",
    "method": "source_feed",
    "verified_at": null
  },
  "uncertainty": [
    "The supplied material provides no sample size, methodology, adoption rates, workflow examples, or definition of a frontier firm, so the size of the claimed gap is unclear."
  ],
  "connected_context": {
    "meaning": "This broadens the adoption picture from individual case studies to a claimed enterprise shift from assistance to execution, but its missing definitions and methodology prevent judging the scale or lead of “frontier firms.” The prior candidates supply concrete deployments and operating practices, so this signal is best treated as demand direction rather than transferable proof of adoption or ROI.",
    "corpus_size": 461,
    "generated_at": "2026-08-15T10:04:01.775Z",
    "connections": [
      {
        "title": "NTT DATA Group cuts incident analysis to 30 minutes with Codex",
        "source_name": "OpenAI",
        "source_url": "https://openai.com/index/ntt-data",
        "feed7_url": "https://feed7.dev/p/ntt-data-1sgidqg",
        "reason": "NTT DATA gives the broad adoption claim a concrete deployment scale and incident-analysis outcome, though as another OpenAI case it does not supply the missing research methodology."
      },
      {
        "title": "How RingCentral builds AI-native work from engineering to ops",
        "source_name": "OpenAI",
        "source_url": "https://openai.com/index/ringcentral",
        "feed7_url": "https://feed7.dev/p/ringcentral-0bxujuk",
        "reason": "RingCentral illustrates the signal’s cross-functional adoption theme by spanning engineering and operations, while likewise withholding measured outcomes and rollout detail."
      },
      {
        "title": "Forward Deployed Engineering at Cursor — Pauline Brunet",
        "source_name": "YouTube",
        "source_url": "https://www.youtube.com/watch?v=APqXGyCoGW4",
        "feed7_url": "https://feed7.dev/p/forward-deployed-engineering-at-cursor-pauline-brunet-1wkd3r1",
        "reason": "Cursor’s playbook supplies implementation conditions absent from the research claim: measurable use cases, customer ownership, co-development, and documented handoff."
      },
      {
        "title": "CFOs and the new economics of AI",
        "source_name": "Cursor",
        "source_url": "https://cursor.com/blog/cfo-council",
        "feed7_url": "https://feed7.dev/p/cfo-council-10ctxbn",
        "reason": "The reported variation in request cost and multi-model use shows why broad enterprise adoption does not by itself resolve ROI or model-selection decisions."
      }
    ]
  },
  "lifecycle": "Current",
  "published_at": "2026-08-12T06:00:00.000Z",
  "modified_at": "2026-08-12T06:00:00.000Z",
  "supersedes": [],
  "expires_at": null,
  "formats": {
    "html": "https://feed7.dev/p/how-enterprises-put-ai-to-work-0p0rqih",
    "json": "https://feed7.dev/p/how-enterprises-put-ai-to-work-0p0rqih.json",
    "markdown": "https://feed7.dev/p/how-enterprises-put-ai-to-work-0p0rqih.md"
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}