{
  "slug": "be-the-ai-person-your-company-can-rely-on",
  "title": "Be the AI Person Your Company Can Rely On",
  "date": "2026-10-02",
  "featuredClaim": "Own one small AI project, measure the whole job, and become the person your company relies on for AI.",
  "description": "Kamil Banc argues that becoming the person who owns AI adoption at your company matters more than chasing every new model release. The article outlines a practical approach: pick one recurring bottleneck, understand the full job, get owner approval, and measure the entire workflow including checking time. It emphasizes clear role division, proper controls, and leaving behind a process colleagues can run.",
  "keyPoints": [
    "Own one small AI project rather than trying to follow every model release",
    "Understand the full job by walking through real cases with the person who does the work",
    "Measure the whole job, including checking and correction time, not just the AI-assisted step",
    "Divide work clearly between systems, code, AI, and people, with humans resolving exceptions and approving outputs"
  ],
  "topics": [
    {
      "id": "implementation",
      "slug": "ai-implementation",
      "label": "Implementation",
      "description": "Hands-on implementation techniques and frameworks"
    },
    {
      "id": "strategy",
      "slug": "ai-strategy",
      "label": "AI Strategy",
      "description": "Strategic planning and implementation approaches for AI adoption"
    },
    {
      "id": "measurement",
      "slug": "measuring-ai-roi",
      "label": "ROI & Measurement",
      "description": "Measuring AI impact and return on investment"
    }
  ],
  "claims": [
    "METR's 2025 coding trial found perceived speed and measured completion time diverged among experienced developers.",
    "Anthropic's agent-building guidance recommends starting with the simplest solution and adding complexity when it earns its place.",
    "Anthropic's permission documentation explicitly distinguishes instructing an agent to ask before sending from approval controls.",
    "NIST's voluntary guidance recommends naming who reviews changes to the prompt, tool, or rules.",
    "Measure the whole job because drafting savings can be erased by added checking and correction time."
  ],
  "claimTitles": [
    "Perceived Speed Diverged",
    "Start Simple, Add Complexity",
    "Asking Isn't Approval",
    "Name a Change Reviewer",
    "Measure the Whole Job"
  ],
  "originalUrl": "https://aiadopters.club/p/be-the-ai-person-your-company-can",
  "claimProvenance": [
    "source-summary",
    "direct-quote",
    "source-summary",
    "source-summary",
    "author-interpretation"
  ],
  "primarySources": [
    {
      "title": "METR’s 2025 coding trial",
      "url": "https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/",
      "publisher": "metr.org",
      "claimIndices": [
        1
      ]
    },
    {
      "title": "Anthropic’s agent-building guidance",
      "url": "https://www.anthropic.com/engineering/building-effective-agents",
      "publisher": "anthropic.com",
      "claimIndices": [
        2
      ]
    },
    {
      "title": "Anthropic’s permission documentation",
      "url": "https://platform.claude.com/docs/en/managed-agents/permission-policies",
      "publisher": "platform.claude.com",
      "claimIndices": [
        3
      ]
    },
    {
      "title": "NIST’s voluntary guidance",
      "url": "https://airc.nist.gov/docs/AI_RMF_Playbook.pdf",
      "publisher": "airc.nist.gov",
      "claimIndices": [
        4
      ]
    }
  ],
  "quote": "Telling an agent to ask before sending doesn't establish an approval control.",
  "keyStatistics": [
    {
      "stat": "20 minutes saved drafting versus 25 minutes added checking",
      "context": "The author's illustration of why measuring the full job, not just the AI-assisted step, can reveal net added work of five minutes."
    },
    {
      "stat": "Ten representative historical test cases",
      "context": "The recommended sample for a first look at performance, explicitly framed as not proof of a company-wide gain."
    },
    {
      "stat": "Three years advising companies on AI",
      "context": "The author's stated practitioner experience underpinning his return to basics: understand the work, involve the right people, and measure."
    },
    {
      "stat": "2025 and 2026 METR studies on AI coding speed",
      "context": "The 2025 trial found perceived speed and measured completion time diverged; the 2026 follow-up encountered selection and time-tracking problems."
    }
  ],
  "supportingContext": "Kamil Banc's methodology is grounded in direct observation rather than tool comparison: he advises booking time with the person who actually does the work, walking through the last completed case with files open, and examining a recent failure to surface hidden rules, workarounds, or defects. Before any AI test, he insists on recording baseline metrics for the whole job, including turnaround time, active preparation time, checking, corrections, and recurring cost, with waiting time kept separate from active work. His practitioner framework divides responsibilities explicitly: existing systems supply approved data, code enforces calculations and validation, AI drafts commentary and flags discrepancies, and people resolve exceptions and authorize outputs. The approach is deliberately incremental, testing on authorized historical cases, piloting with a small group against agreed acceptance checks, logging failures as test cases, and naming who maintains the workflow if it earns its place.",
  "canonicalUrl": "https://kbanc.com/claims-library/be-the-ai-person-your-company-can-rely-on",
  "markdownUrl": "https://kbanc.com/md/claims-library/be-the-ai-person-your-company-can-rely-on.md",
  "jsonUrl": "https://kbanc.com/api/claims/be-the-ai-person-your-company-can-rely-on.json",
  "source": {
    "publisher": "AI Adopters Club",
    "title": "Be the AI Person Your Company Can Rely On",
    "url": "https://aiadopters.club/p/be-the-ai-person-your-company-can"
  }
}