In May 2012, when I graduated from Concordia College as an eager, bright-eyed young professional, I started my career as an intern at Fairview Health - the third-largest healthcare system in the state at the time. I was working directly with and reporting to the president of the health system, a prestigious experience for a first-time intern.
My first few weeks on the job, I basically followed the president around to board meetings, community engagement events, and operational meetings. I thought to myself, "I can't believe I'm getting paid $25 an hour just to learn."
Eventually, I got my first real project to conduct a study on why patient experience and quality of care in the medical-surgical units were lagging behind peer healthcare systems. It took me about three months of patient interviews, shadowing doctors, poring through survey data and care practices, researching what other systems were doing, and analyzing historical data on care outcomes to finalize my report and recommendations.
I ended up getting a full-time offer based on that work. I was lucky my experience was unlike the typical intern experience where you basically serve as the errand person and do little of anything value-added. Reflecting on this experience got me thinking about how AI could potentially upend the traditional apprenticeship pipeline for young interns while also simultaneously giving small businesses access to intelligent interns at 1/100th of the cost of hiring a full-time intern.
How much more could a business do if the cost of knowledge ultimately approaches $0 and can current tools already approximate the work of some interns do such as basic market research.
AI Claim vs Reality
The core challenge that most small business owners are trying to solve is how to get more market share and wallet share from their customers. Many small business owners conduct competitive analysis on pricing to understand how competitors are pricing their products. Often they'll pay a consultant to handle this type of work. So the promise of a tool that can conduct deep market research at a fraction of the cost of hiring a consultant would be a game changer.
So how does AI work in the background to complete reasoning tasks like this where it needs to think for a while, just like a human would? Think of AI research tools as incredibly fast readers that have already read billions of web pages, reports, studies, and articles. When you ask "What are coffee shops in downtown Portland charging for lattes?" the AI doesn't just search like Google. Instead, it breaks down your question and starts making connections between different pieces of information it has seen before.
The AI looks for patterns across thousands of coffee shop websites, menu listings, review sites, and local business directories. It identifies which businesses are actually competitors, extracts their pricing information, and starts noticing trends—like whether shops near universities charge less or if organic coffee costs more across the board.
What's different from a simple search is that the AI can reason through the information. It might notice that three shops increased prices in the last six months and connect that to a news article about rising coffee bean costs. Or it might identify that shops with similar atmospheres tend to price similarly, giving you insight into positioning strategy.
The AI then packages all this analysis into a comprehensive report with the specific insights you need - competitor prices, market trends, and recommendations - completed in minutes instead of the weeks or months it would take a paid consultant or intern.
This week I put this claim to the test by using three AI tools (ChatGPT, Claude and Manus) to create a competitive pricing report for Playa Bowls – a healthy superfruit acai bowl, smoothie and fresh juice fast casual restaurant in Philadelphia.
In order to create this report, i constructed a prompt similar to what a business owner might do when providing requirements to an intern or paid consultant to run this analysis. I used the same prompt across all three AI tools
Prompt
Research competitive pricing for Playa Bowls (açaí bowls, smoothies, fresh fruit) in Philadelphia market to assess pricing strategy and identify optimization opportunities. Competitors to Research: Vitality Bowls, Jamba Juice, SoBol, Rush Bowls, local açaí shops: Sweetgreen, Freshii, CoreLife Eatery, local juice bars Adjacent: Tropical Smoothie Cafe, Smoothie King, Starbucks. Collect pricing for: Açaí bowls (all sizes, signature vs build-your-own, add-ons) Smoothies (all sizes, protein vs fruit-based, supplements) Other items (oatmeal bowls, toast, fresh fruit cups). For geography focus on Center City, University City, Northern Liberties, King of Prussia, Main Line suburbs. Your key Deliverables are: Comprehensive report with: Pricing comparison tables by product category Market positioning analysis (where Playa Bowls ranks) Geographic pricing variations Value proposition comparison (portion sizes, ingredients, quality) Strategic recommendations for pricing optimization.
Manus
Competitive pricing report from Manus AI
ChatGPT
Competitive pricing report from ChatGPT
Anthropic Claude
Competitive pricing report from Claude
The Verdict
So, is the claim that AI can help you complete market research using a simple English prompt accurate? Yes! I was impressed across the board by Manus, Claude, and ChatGPT's ability to reason and create market research reports.
Stylistically, there were few differences between the three tools. Manus' output looked the most like a structured report, even though the formatting (tables, line breaks, etc.) wasn't the best. But it was the only tool that clearly outlined its methodology and provided succinct summaries that made the information very digestible. It intuitively knew to not just look at different sources for pricing but also create an average across multiple channels, which showed that Playa Bowls was priced more competitively.
While ChatGPT's report was beautifully designed, the information wasn't as digestible for me. ChatGPT also didn't disclose its methodology, which made it hard to understand the basis for ranking Playa Bowls as a more premium offering. I did love that ChatGPT provided verifiable sources, though many of their pricing sources were from delivery apps like Uber. Claude's output was very similar to ChatGPT.
I personally preferred Manus' output because it was digestible and the methodology was transparent. One area for improvement is their report design template. Manus also transparently shows you in real time the pages they're visiting and provides a full transcript of every step they take. I didn't find ChatGPT and Claude to be as transparent.
Thirteen years ago, I was getting paid $25 an hour as an intern to conduct deep research for a hospital. Today, I paid the equivalent of $2 to build three competitive pricing research reports in a matter of minutes.
Pro Tip
When using AI tools to conduct market research, always ask the AI to disclose its methodology and sources when it is unclear and always run a sanity check on those sources to ensure the output and recommendations you are getting are grounded in truth.