I’ve spent 12 years in analytics and operations, supporting everything from mid-market due diligence to C-suite decision memos. Last month, I was working with a client who thought they could save money but ended up paying more.. In that time, I’ve learned one immutable truth: if your team (or your AI) always agrees with you, you’re missing the risks that will actually sink the ship. Most people use LLMs like GPT or Claude to generate summaries or draft emails. That is a waste of a massive resource.
I use AI to stress-test my logic. I keep a "hallucination log"—a literal spreadsheet where I track every time an AI model confidently lies or fails to account for a critical operational variable. By doing this, I’ve realized that the greatest weakness of LLMs isn't their intelligence; it's their "yes-man" bias. They are trained to be helpful, not to be a dissenting board member.
If you want to use AI for decision intelligence, you need to stop asking it to "summarize" and start asking it to "disagree." Here is how I use GPT and Claude to build better decision frameworks.
1. The Multi-Model Debate: Why You Need Two Perspectives
One of the biggest mistakes in prompt engineering is relying on a single model. GPT-4o and Claude 3.5 Sonnet have different training objectives and reasoning styles. When I’m working on a high-stakes decision—like a P&L restructuring or a vendor pivot—I run a "Debate Session."
I feed the same raw data into both models. I ask them to represent two distinct stakeholders. This turns disagreement into a product feature rather than a nuisance.
The "Triangulation" Workflow
- Input: Provide your project brief, current data, and intended strategy. GPT (The Logic Auditor): Task it with checking for mathematical errors and consistency with historical data. Claude (The Nuance Filter): Task it with evaluating the strategic implications, cultural impact, and long-term sustainability. The Conflict Layer: Ask them to review each other’s critiques and find the gaps in their own reasoning.
2. The "Hard Questions" Prompt: Forcing Dissent
Most prompts fail because they are too polite. If you ask, "What are the risks of this project?", the AI will give you a bulleted list of generic, safe risks like "market volatility" or "resource constraints." That’s useless.
To get value, you need a hard questions prompt that forces the AI to challenge your assumptions. I use the "Devil’s Advocate Protocol."
The Prompt Template:
"I am proposing [Project X] to achieve [Goal Y]. Before I proceed, I need you to act as a hostile board member who is skeptical of my assumptions. 1. Identify three specific cognitive biases I might be exhibiting in this proposal (e.g., sunk cost, survivorship bias). 2. Force counterarguments against each of my core assumptions. 3. Do not be polite. Point out where my data is thin or where my logic is circular. 4. List the 'unknown unknowns' that would destroy this plan if they were true."When you use this, watch for the AI’s tendency to qualify its own dissent. If it says, "However, this could be a good idea if...", stop it. Tell it: "Remove the hedge. State the case against this as strongly as possible."
3. Building the Risk Checklist
In due diligence, we rely on checklists because they prevent human error. I treat my AI interactions the same way. Before I present a memo to an exec team, I run my work through an automated risk checklist.
Risk Category AI Audit Question Data Integrity "Where could the source data for this calculation be biased or outdated?" Operational "If our current growth rate drops by 15%, what single point of failure collapses first?" Incentives "What incentives am I accidentally creating that would cause my team to act against the goal?" Execution "What is the most common reason a plan like this fails in mid-market tech environments?"By forcing the AI to evaluate these specific areas, you shift from "I hope this works" to "I have stress-tested this against these five failure modes."
4. Decision Intelligence: What Would Change My Mind?
Before I ever trust an answer from an AI, I apply a mental filter: "What would change my mind?"
This is the most critical step in decision intelligence. If you cannot define the signal that would prove your strategy wrong, you are not making a decision—you are engaging in confirmation bias. I add this prompt to every major output I receive from GPT or Claude:
"Based on the recommendation you just provided, what specific data point or event occurring in the next 30 days would prove this recommendation is incorrect? Define the 'stop-loss' criteria for this decision."
launchbuffThis forces the AI to move away from "the best possible scenario" and into "the reality of execution." If the model cannot provide a clear boundary condition for failure, I ignore the advice entirely. It means the model is hallucinating a sense of certainty that doesn't exist.
Why Most AI Advice Fails (And How to Fix It)
I see people using AI to "write" their strategy documents. That is a mistake. AI is an editor, a researcher, and a devil’s advocate. It should never be your author. When you rely on AI to write your narrative, you lose the nuance of the actual business problem. ...but anyway.
The "Hallucination Log" Principles
Never cite a stat without the primary source: If the AI can't tell you exactly where a number came from, treat it as a hallucination. Beware of the "Confidence Bias": Models sound most confident when they are wrong. If an answer sounds too smooth, ask for the counter-evidence immediately. Always ask "Why?": If the AI suggests a course of action, force it to decompose its logic. If the decomposition is shaky, the conclusion is worthless.Summary: The Mindset of an AI-Powered Ops Lead
The goal isn't to get the "correct" answer from the AI. The goal is to build a high-fidelity model of reality. Using GPT and Claude to stress-test your strategy, force counterarguments, and build rigorous risk checklists is the difference between a "gut feeling" presentation and a "data-backed" decision memo.
When you start treating disagreement as a product feature of your LLM workflow, you stop seeing hallucinations and start seeing blind spots. And in the world of high-stakes operations, spotting the blind spot before it hits you is the only competitive advantage that matters.
Check your work. Keep a log. And never, ever let the AI have the final word.