// Model
Problem to production.
The AC model is a repeatable operating cycle with explicit AI roles, escalation points, and handoff logic.
// Problem to Production
One scenario, one owner, one continuous chain.
The model becomes legible when you watch the same operator hold context from the first signal through the production result.
// Example Scenario
Recover onboarding conversion without spinning up three departments.
Activation drops after an onboarding change. Nobody owns the full fix because product, design, engineering, and analytics all hold different pieces.
Audience
Founders and product leaders evaluating whether the AC model creates speed without chaos.
Outcome
A real production improvement with a measured result, documented learning, and no lost context between strategy and execution.
01
Frame the problem
The AC inspects the drop, identifies the customer segment affected, and confirms the problem is worth solving.
02
Validate with evidence
They combine analytics, market signals, support data, and company context to confirm a fix is aligned with strategy and mandate.
03
Design and build
They orchestrate AI agents for UX, implementation, testing, and instrumentation while keeping final judgment on scope and quality.
04
Ship through the platform
The AC delivers a working change that fits the platform contract, pushes it live, and measures the result against agreed success criteria.
05
Retrospect and hand off
They log the decision, capture what the agents got right or wrong, and hand the stable outcome to the platform team.
Proof Style
Example scenario used to make the model concrete. It illustrates the operating pattern and is not presented as a public case study.
// Operating Cycle
Six steps. Full ownership.
The important design choice is not just the steps. It is that the same accountable operator stays with the outcome through all of them.
Problem or Opportunity Identification
The AC doesn't receive a brief. They look for places where the company is losing value — or where it could create it.
They rely on the company context (vision, mission, personas, do's & don'ts), which they have available at all times.
Research and Validation
Before execution, mandatory validation of the idea takes place.
Market data, persona research, competitive analysis. Impact and ROI estimation. Verification against the project registry. AI validation evaluates the idea against company context and the log of previous decisions.
Execution
The AC builds, tests, and iterates. They compensate for gaps with specialized AI agents that serve as domain experts.
In the initial phase, agents are supervised by human specialists. Where an AI agent is insufficient — e.g., legal review, security audit, UX research with real users — the AC escalates to a human specialist.
Shipping to Production
The AC doesn't deliver a presentation or a prototype. They deliver a working thing that meets the contract with the platform team.
Before deployment to production, a review gate defined by the company may take place.
Retrospective
After each completed project, the AC conducts a retrospective.
Feedback to AI agents. Filling in the retrospective record in the log. Success/failure attribution analysis — explicit separation of internal factors, external factors, and chance.
Handoff and Moving On
Once the outcome is stable, the AC hands it off to the platform team for maintenance and operations.
The AC then moves on to the next opportunity.
// AI Agent Roles
Three modes of AI support.
AI is useful in the model for different reasons at different moments. The AC must know which mode they are operating in.
Tool
AI Executes
AI generates output based on the AC's assignment: code, text, analyses, visuals, data transformations.
Control
The AC must always verify the output. For code — tests, review. For text — factual accuracy, tone.
Best For
Code generation, copywriting, data analysis, visual design, routine transformations.
Advisor
AI Recommends
AI suggests approaches, flags risks, offers alternatives with reasoning.
Control
The AC must critically evaluate reasoning — AI can sound convincing even when wrong.
Best For
Brainstorming, trade-off analysis, risk identification, approach comparison.
Decision Support
AI Prepares Materials
AI gathers data, prepares comparisons, models scenarios. It doesn't produce output or recommendations.
Control
The AC must verify the quality and completeness of materials.
Best For
Research, competitive analysis, data summarization, scenario modeling.
// Human Escalation
Where AI is not enough.
The model is rigorous about one rule: convincing output is not the same thing as verifiable output. Where the AC cannot verify the result, they escalate.
- — Legal review — AI can prepare materials, but legal conclusions must be verified by a lawyer.
- — Security audit — AI can scan, but security architecture requires a human expert.
- — UX research with real users — AI can analyze data, but the research itself requires human interaction.
- — Regulatory compliance — AI can identify relevant regulations, but interpretation requires a specialist.
- — Financial and tax decisions — AI can model, but a specialist bears responsibility.
// AI Maturity
Reliability improves. Responsibility does not move.
Different agents may sit in different maturity phases. The AC's job is to know how much review each one needs before a decision can be trusted.
Supervised
The AI agent is new or uncalibrated. Every output is checked by the AC and ideally by a human expert. Used primarily as a tool.
Typical Review Time
~70%
Calibrated
The AI agent has gone through several cycles of feedback. The AC knows its strengths and weaknesses. Review continues — targeted at known weaknesses.
Typical Review Time
30–40%
Reliable
The AI agent consistently delivers quality outputs. The AC reviews outputs spot-check style. Periodic auditing ensures quality doesn't decline.
Typical Review Time
10–15%