The most expensive mistake a founder can make is building something the market doesn't actually need, or building for a need that's already well-served by existing solutions.

Market gap research has traditionally required either expensive research reports or weeks of manual analysis. AI hasn't eliminated that work, but it's made a serious gap analysis accessible to solo founders with limited time and budget.

Here's what we'll cover:

• What a market gap actually is and the three types worth finding

• How AI fits into gap identification (and where it doesn't)

• A step-by-step process using tools you already have access to

• The specific prompts that surface real gaps rather than obvious ones

• How to validate that a gap is worth building for

You ran the survey. Responses are in. Now you're staring at a spreadsheet of 200 rows wondering where to start.

Survey analysis has always been one of the most time-intensive parts of the research process — especially when you have open-ended responses. AI has changed this significantly. Pattern extraction that used to take days of manual coding can now happen in hours.

Here's what we'll cover:

• The difference between quantitative and qualitative survey analysis — and where each bottleneck used to be

• How AI accelerates open-ended response analysis specifically

• The workflow from raw data to actionable insight

• The prompts that produce real patterns rather than surface-level themes

• How to validate AI analysis against your raw data

At some point — in an investor meeting, a strategic planning session, or your own go/no-go decision — someone is going to ask you how big the market is. And the answer needs to be defensible, not directional.

Market sizing has traditionally required access to expensive syndicated research reports or a research firm. AI has meaningfully changed that. Not by generating the numbers for you (that's where founders get into trouble) but by dramatically accelerating the data gathering and synthesis that makes the analysis possible.

Here's what we'll cover:

• What TAM, SAM, and SOM actually mean and how to calculate each

• The data sources that make the numbers credible

• Where AI helps and where it will mislead you

• A step-by-step workflow for building a defensible analysis

• How to stress-test your numbers before you present them

AI tools have changed what's possible in UX research, but not always in the direction the hype suggests. Some tasks are genuinely faster and better with AI assistance. Others are worse, or produce confidently stated wrong answers. Knowing the difference is the professional skill that matters right now.

This guide reflects our practice at Praxia Insights. For a look at the full UX research service we offer, see our UX research services page.

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