Claims Library Entry
Be the AI Person Your Company Can Rely On
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.
Published October 2, 2026 by Kamil Banc
Lead claim
Own one small AI project, measure the whole job, and become the person your company relies on for AI.
Atomic Claims
What this article supports
Copy individual claims as needed.
Claim 1 · Source summary
Perceived Speed Diverged
METR's 2025 coding trial found perceived speed and measured completion time diverged among experienced developers.
Claim 2 · Direct quote
Start Simple, Add Complexity
Anthropic's agent-building guidance recommends starting with the simplest solution and adding complexity when it earns its place.
Claim 3 · Source summary
Asking Isn't Approval
Anthropic's permission documentation explicitly distinguishes instructing an agent to ask before sending from approval controls.
Claim 4 · Source summary
Name a Change Reviewer
NIST's voluntary guidance recommends naming who reviews changes to the prompt, tool, or rules.
Claim 5 · Kamil's interpretation
Measure the Whole Job
Measure the whole job because drafting savings can be erased by added checking and correction time.
Evidence
Context behind the claims
Quote
"Telling an agent to ask before sending doesn't establish an approval control."
Key statistics
20 minutes saved drafting versus 25 minutes added checking
The author's illustration of why measuring the full job, not just the AI-assisted step, can reveal net added work of five minutes.
Ten representative historical test cases
The recommended sample for a first look at performance, explicitly framed as not proof of a company-wide gain.
Three years advising companies on AI
The author's stated practitioner experience underpinning his return to basics: understand the work, involve the right people, and measure.
2025 and 2026 METR studies on AI coding speed
The 2025 trial found perceived speed and measured completion time diverged; the 2026 follow-up encountered selection and time-tracking problems.
Supporting context
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.
How to Cite
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Individual Claim
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"[claim text]" (Banc, Kamil, 2026, https://kbanc.com/claims-library/be-the-ai-person-your-company-can-rely-on)Original Article
Use this when you want to cite the full newsletter article at AI Adopters Club rather than the structured claims page.
Banc, Kamil (2026, October 2, 2026). Be the AI Person Your Company Can Rely On. AI Adopters Club. https://aiadopters.club/p/be-the-ai-person-your-company-canClaims Collection
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Banc, Kamil (2026). Be the AI Person Your Company Can Rely On [Structured Claims]. Retrieved from https://kbanc.com/claims-library/be-the-ai-person-your-company-can-rely-onAttribution Requirements
- Include the author name: Kamil Banc.
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