You asked ChatGPT whether to raise prices.
It said yes. It gave you three reasons. It sounded confident.
You raised prices. Revenue dropped 22%. Churn doubled.
ChatGPT was wrong. But you had no way to know that before you made the call.
This is not your fault. ChatGPT is built to sound confident, not to be right.
The Problem With Asking One AI
When you ask ChatGPT for business advice, you get one model’s opinion. That model has biases baked into its training data. It has blind spots. It cannot do math. It does not know your specific situation well enough to stress-test its recommendations.
Most importantly, it has no one challenging its logic.
Think about how real strategy works. You do not ask one person and do whatever they say. You get multiple perspectives. You challenge assumptions. You run the numbers before you commit.
ChatGPT skips all of that. It gives you an answer in 30 seconds and moves on.
That works fine for first drafts and brainstorming. It fails completely when money is on the line.
Three Ways ChatGPT Fails at Strategy
1. It Hallucinates Numbers
Large language models (LLMs) like ChatGPT construct responses word by word based on patterns in their training data. They do not calculate. They predict what number would fit the sentence.
Ask ChatGPT to estimate your customer acquisition cost based on ad spend and conversion rates. It will give you a number. That number is a guess dressed up as math.
This is dangerous when you are deciding whether to spend $50k on a new market or hire two people instead of one. Wrong numbers lead to wrong decisions. Wrong decisions cost real money.
2. It Gives Generic Advice
ChatGPT was trained on millions of business articles, most of which say the same things. “Focus on customer experience.” “Build relationships.” “Test and iterate.”
All true. None actionable.
When you ask ChatGPT whether to expand to Dallas or Fort Worth, it will tell you both are great markets. It will tell you to do market research and talk to customers. It will not tell you which city has better unit economics for your specific business model or when to quit if you are wrong.
You need specifics. ChatGPT gives you motivation.
3. It Has No Adversarial Pressure
Real strategic decisions get better when people argue. The CMO wants to expand. The CFO wants proof of ROI. The product lead warns about operational complexity. The debate surfaces blind spots and forces better thinking.
ChatGPT has no debate. It gives you one perspective and stops. If that perspective has a flaw, you will not find out until after you have committed money and time.
What Actually Works: Debate Plus Math
Strategic decisions need two things ChatGPT cannot provide: adversarial pressure and mathematical rigor.
Adversarial pressure means multiple perspectives challenging each other. This is how boards work. This is how good strategy gets built. You need someone saying “but what if churn goes up?” and someone else saying “what is the opportunity cost of not moving?”
Mathematical rigor means running real simulations with probability distributions. Not LLM-generated numbers. Actual code that stress-tests your assumptions 1,000 times and shows you the range of outcomes.
When you combine those two things, you get strategy that accounts for blind spots and is grounded in realistic financials.
How This Works in Practice
Let me show you what the difference looks like with a real decision: whether to expand into a second city.
ChatGPT approach:
“Both Dallas and Fort Worth are excellent markets with strong demographics! I recommend starting with market research and building local partnerships. Focus on delivering value and the results will follow.”
Encouraging. Completely useless.
Multi-model adversarial approach with financial modeling:
Six AI models debate the decision. Each model plays a specific business role based on strategy frameworks from Harvard Business School and McKinsey. The CMO argues for Dallas based on brand awareness data. The CFO models cash flow requirements for both. The risk manager stress-tests downside scenarios. The growth strategist looks at expansion sequencing.
They challenge each other. The CFO pushes back on the CMO’s revenue assumptions. The product lead warns about operational complexity in Fort Worth. The debate surfaces trade-offs ChatGPT would never mention.
Then Python takes over for financials. It runs 1,000 Monte Carlo simulations for both cities using realistic probability distributions for churn, customer acquisition cost, and growth rates.
The output: “Dallas shows $275k net present value (NPV) with 73% probability of profitability by year two. Fort Worth shows $180k NPV with 54% probability. Recommendation: Dallas. Exit trigger: If not cash-positive by month 18, pivot.”
That is a decision you can act on. You know which city. You know why. You know when to quit if you are wrong.
Why Different Models Matter
ChatGPT, Claude, Gemini, Grok, and Perplexity were all trained on different data with different architectures. They have different strengths and blind spots.
ChatGPT might be optimistic about growth based on its training data. Claude might be more conservative on risk. Gemini might catch operational issues the others miss.
When you force them to debate, you get a more complete picture. No single model dominates. Flawed assumptions get challenged before you act on them.
This is not perfect. AI still has limitations. But it is significantly better than asking one model and hoping it got it right.
When You Actually Need This
Not every decision needs this level of rigor. If you are choosing a new logo or testing a landing page headline, ChatGPT is fine.
You need adversarial strategy and financial modeling when:
The decision costs real money. Hiring decisions. Pricing changes. Market expansion. These have multi-month financial impacts. Getting them wrong is expensive.
You have competing options. Dallas or Fort Worth. Raise prices or add a tier. Hire two people or automate. When trade-offs exist, you need someone arguing both sides.
You need to know when to quit. Most strategies fail because people do not set exit triggers. They commit to a path and keep going even when the data says stop. You need clear metrics for when to pivot.
You have financial data. If you can provide revenue, costs, and conversion rates, Monte Carlo simulations show you the realistic range of outcomes. That is infinitely better than gut feel.
The Math Problem
LLMs cannot do arithmetic reliably. This is not a bug. It is how they work.
When you ask ChatGPT to calculate ROI, it is not running a formula. It is predicting what number would fit based on similar sentences in its training data.
Sometimes it gets close. Sometimes it hallucinates wildly. You have no way to know which until you check its work.
For financial decisions, this is unacceptable. You cannot bet your business on a number that might be made up.
Python does not have this problem. It runs actual calculations with actual formulas. When it tells you the expected value is $275k with a standard deviation of $80k, that number came from 1,000 simulations, not pattern matching.
This is why any serious strategic tool needs to separate LLM synthesis from mathematical computation. Let the AI do what it is good at: pattern recognition, synthesis, and challenging assumptions. Let code handle the math.
What This Looks Like
Here is what you get from a multi-model adversarial approach with financial modeling:
Strategic synthesis. Six perspectives debating trade-offs. Optimistic and pessimistic cases. Blind spots surfaced before you commit.
Financial rigor. Monte Carlo simulations showing realistic outcome ranges. NPV calculations. Probability distributions for key metrics.
Specific actions. Not “focus on relationships.” Actual next steps: “Hire a Dallas-based sales rep by Q1. Budget $12k for local ads. Target 15 customers by month six.”
Exit triggers. Clear metrics for when to pivot: “If customer acquisition cost exceeds $800 by month nine, pause Dallas and reassess.”
You can act on this immediately. You know what to do, why to do it, and when to stop if it is not working.
The Real Comparison
Traditional strategy consulting costs $10k to $50k per project. You get interviews, decks, and workshops over six to eight weeks. High touch. Expensive. Slow.
ChatGPT costs nothing. You get instant answers. Generic. Unverified. Often wrong when it matters.
Multi-model adversarial strategy with financial modeling costs $50. You get strategic debate, mathematical rigor, and specific actions in about 15 minutes. One-time payment. No retainer. No subscription.
This is not as good as spending three months with McKinsey. It is infinitely better than asking ChatGPT and hoping.
When ChatGPT Is Enough
ChatGPT is great for:
- Brainstorming
- First drafts
- Explaining concepts
- Quick research
- Content ideas
ChatGPT is terrible for:
- Financial decisions
- Trade-off analysis
- Risk assessment
- Specific recommendations
- Anything with real money at stake
Know the difference. Use the right tool for the job.
What to Do Next
If you are facing a strategic decision this month—pricing, expansion, operations, or hiring—do not ask ChatGPT and act on whatever it says.
Get multiple perspectives. Run the numbers. Set exit triggers.
If you do not have time to coordinate six experts and hire a data scientist, Surmado Solutions does this automatically. Six AI models debate your decision. Python runs 1,000 simulations. You get a strategic report with NPV, probability ranges, and exit triggers in about 15 minutes. $50. One-time payment.
It is not perfect. No strategy is. But it is a hell of a lot better than asking one AI and hoping you got lucky.
Related: How AI visibility affects your business | What your customers see when they ask AI for recommendations