I Did Not Ask GPT to Do My Job. I Taught It How I Make Decisions.

2026-07-19 · Source: Artificial Intelligence on Medium · Field: Business & Management — Operations & Process Management, Customer Experience & Engagement, Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

The author describes a project utilizing GPT as a working partner to overhaul hundreds of customer-facing articles, content structures, and operational workflows. While achieving the productivity of six to seven people, the core insight was teaching the AI nuanced business judgment. This involved translating years of experience into explicit instructions, focusing on correcting the AI's decision-making logic rather than just its output. A seven-step method was developed: Observe, Explain, Test, Correct, Convert, Scale, and Verify. Key aspects included defining reusable rules, rigorous single-example testing, treating AI errors as incomplete instructions, and carefully segmenting tasks by automation authority. The initiative transformed into a customer-feedback engine, linking knowledge improvement directly to customer demand and operational evidence. This process highlights a shift in human value from content production to designing intelligent systems and defining quality standards.

Key takeaway

For operations leaders integrating AI into customer service or knowledge management, focus on teaching AI your organization's specific judgment. Instead of merely automating tasks, you should systematically convert business context and decision-making logic into explicit, reusable rules. This approach shifts your team's value upstream, from execution to designing robust, customer-centric AI systems that embody organizational accountability and improve knowledge infrastructure.

Key insights

Teaching AI business judgment by correcting its decision-making logic, not just output, transforms productivity into systemic knowledge infrastructure.

Principles

• Challenge AI output as a model, not a conclusion.
• Treat AI mistakes as missing rules, not forgotten facts.
• Convert repeated corrections into reusable operating standards.

Method

The method involves observing current situations, explaining correction reasons, testing on single examples, treating mistakes as missing rules, separating automation from authority, converting corrections into standards, and verifying customer outcomes.

In practice

• Start with one bounded task and a specific prompt.
• Define when a rule applies, its exceptions, and uncertainty.
• Verify customer-facing results, not just system changes.

Topics

• AI Training
• Customer Experience
• Knowledge Management
• Operational Workflows
• Automation Strategy
• Business Judgment

Best for: Director of AI/ML, Operations Professional, Consultant

Related on AIssential

• Operationalizing Customer Service at Scale with Outcome-Driven Agentic AI - with Craig Walker of Dialpad — AI augments human agents, automates routine tasks, and provides real-time coaching to transform customer service.
• Transcript: ‘We Automated Everything With AI and Tripled Our Headcount’ — AI automation, by making expert competence cheap, paradoxically increases demand for human experts to refine outputs and innovate.
• AI Is Everywhere In GTM. Customer Value Isn’t. — AI is transforming GTM, necessitating a shift from efficiency to customer outcomes and cross-functional alignment.
• AI for Decision-Making— Prompt to Profit · Day 25 of 30 — AI serves as an analytical thinking partner for complex decisions, enhancing rigor without replacing human judgment.
• You Spent Your Whole Life Getting Good at the Wrong Thing — Agentic AI shifts the bottleneck from "how" to execute to "what" to desire, demanding a new focus on ideation.
• Daniel Fallmann on democratizing expertise, dynamic interfaces, judgment amplification, and organizational intelligence (AC Ep48) — AI's true potential lies in augmenting human expertise and judgment through contextualized, accessible organizational intelligence.

Counsel's verdict on this

AIssential's Counsel cites this article in its editorial verdict on the decision it informs:

• Our best people's know-how isn't written down — can AI even use it? — 95% of generative AI pilots are failing because uncodified expert decision-making causes customer-facing AI agents to fail, triggering correction cascades that degrade performance and consume tokens.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence on Medium.