The MBA has long been the gold standard for strategic business thinking. Graduates spend years honing their skills in market analysis, financial modeling, and competitive strategy. But in an era where technology is reshaping every industry, a new contender has emerged: the AI Agent.
This isn't just about asking a chatbot for ideas. We're talking about a new class of Cognitive Automation Engines designed for deep, complex reasoning. So we decided to put it to the test. Could an advanced AI agent, specifically our problem-solving API at thinking.do, create a viable strategic business plan that could rival one from a seasoned MBA?
Let the experiment begin.
To make this a fair fight, we needed a realistic business challenge that requires both market analysis and strategic formulation. Here’s the prompt:
"Analyze current market trends in renewable energy and formulate a three-point strategic plan for a new startup focused on residential solar panel installation."
This problem requires more than just spitting out facts. It demands analysis, synthesis, and the creation of an actionable plan—classic MBA territory.
An MBA graduate would tackle this by diving into weeks of research. Their process would likely involve:
Strength: Deep, framework-driven analysis and human intuition.
Weakness: Time-consuming (weeks or months), expensive, and potentially limited by the scope of research one person can reasonably conduct.
The AI agent takes a different approach. Instead of weeks of manual labor, we make a single API call.
At thinking.do, we provide Reasoning as a Service. Our platform isn't a standard LLM; it's an agentic system. This means when it receives a complex problem, it autonomously breaks it down into a logical sequence of sub-tasks, executes them, and synthesizes the findings into a single, coherent answer.
Here’s what the entire process looks like in code:
import { Do } from '@do-sdk';
const doClient = new Do({ apiKey: process.env.DO_API_KEY });
async function getStrategicPlan() {
const problem = 'Analyze current market trends in renewable energy and formulate a three-point strategic plan for a new startup focused on residential solar panel installation.';
const { result } = await doClient.agent('thinking').run({
prompt: problem,
complexity: 'expert', // Request a deep, thorough analysis
output_format: 'markdown_list' // Ask for a clearly structured output
});
console.log(result);
}
getStrategicPlan();
Strength: Blazing speed (minutes, not weeks), ability to synthesize vast amounts of real-time market data instantly, and tireless execution of strategic analysis.
Weakness: Relies on the quality of the prompt and the data it can access.
After running the API call, the thinking.do agent returned its three-point strategic plan. Here’s a summary of what it produced:
AI-Generated Strategic Plan: "SolarStart Inc."
The AI's plan was data-driven, actionable, and modern. The MBA’s plan would likely arrive at similar conclusions, but the AI's agentic workflow produced it in a fraction of the time and could even provide a log of its reasoning and the sources it consulted.
So, how is this different from just prompting an LLM?
So, can an AI agent write a better business plan than an MBA?
The answer is nuanced. The AI agent wins, hands down, on speed, data-processing capability, and efficiency. It can perform in minutes what takes a human expert weeks.
However, the true power isn't in replacement, but in augmentation. Imagine an MBA armed with this problem-solving API.
The future of strategy isn't Man vs. Machine. It's Man with Machine. The winner of our experiment is the professional who leverages the power of AI reasoning to amplify their own expertise.
Ready to integrate Reasoning as a Service into your application or workflow?
Explore the thinking.do API and start your first project today.