AI-Powered Analysis
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Showing 1–15 of 18 articles
The Ethics of Letting AI Read Your Customers' or Employees' Words (2026)
Running open-ended feedback through an AI classifier is a practical, increasingly ordinary choice - and it's also a choice that involves someone else's words, often written under an assumption of who or what would actually be reading them. This guide covers the genuine ethical considerations worth thinking through before adopting AI-assisted analysis of customer or employee feedback: consent and expectation, anonymity, and what respondents were actually told.
How Many Human-Coded Responses Do You Need to Validate an AI Classifier? (2026)
Checking whether an AI classifier is trustworthy means hand-coding a sample and comparing it to the AI's output - and the obvious next question is how big that sample needs to be. Too small, and the check itself is unreliable; too large, and you've spent more effort validating than the original classification saved you. This guide covers what research on validation set sizing actually shows, and a practical range for everyday business use.
Prompt Engineering for Qualitative Research: A Non-Technical Introduction (2026)
\"Prompt engineering\" sounds like a technical skill for people who write code, and for the purposes of qualitative research, it's closer to a writing and thinking skill - the same instinct that makes someone a clear research brief writer translates almost directly into getting better results from an AI tool. This guide introduces the core ideas in plain language, for researchers and analysts who've never written a line of code and don't need to.
Sentiment Analysis and Thematic Analysis Are Not the Same Thing (2026)
\"We did sentiment analysis on the feedback\" and \"we did thematic analysis on the feedback\" get used almost interchangeably in casual conversation, and they describe two different questions with two different kinds of answers. One tells you how people felt. The other tells you what they were talking about. Confusing the two - or assuming one substitutes for the other - is a quietly common source of thin, unconvincing findings from open-ended data.
AI vs. Manual Coding: How to Decide Which One Your Project Needs (2026)
Neither AI-assisted coding nor fully manual coding is the universally correct choice - they trade off speed, cost, auditability, and nuance differently, and the right pick depends on what your specific project actually needs from its analysis. This guide covers a practical decision framework: the questions worth asking about your stakes, your timeline, and your audience before choosing a method, plus the hybrid approach most real projects actually end up using.
Where Bias Creeps Into AI-Assisted Thematic Analysis (2026)
AI-assisted analysis is often assumed to be more objective than a human reading the same data by hand, simply because it isn't a person with a personal stake in the outcome. That assumption skips over the several distinct points where bias can enter an AI-assisted analysis anyway - not personal bias in the human sense, but systematic distortion that shapes results in a consistent direction. This guide covers where it actually creeps in, and what to watch for.
Can You Trust AI to Analyze Qualitative Data? What the Research Says (2026)
Rather than answer \"can you trust AI with qualitative data\" with a marketing-driven yes or a reflexive no, a more useful answer comes from looking at what published research comparing AI and human coders has actually found - and the honest picture is more specific and more interesting than either extreme. This guide summarizes the research on AI-human coding agreement, where it holds up well, and where it reliably doesn't.
How AI Actually Reads Open-Ended Text: A Plain-Language Explanation (2026)
\"AI categorizes your survey responses\" is a sentence most people nod along to without a clear picture of what's actually happening underneath it - and the honest picture is less mysterious, and less magical, than either the hype or the skepticism around AI usually suggests. This guide explains, in plain language and without the marketing gloss, what a language model is actually doing when it sorts open-ended text into categories.
From Themes to Action: Turning AI-Categorized Feedback Into a Report People Trust (2026)
A clean category breakdown - 34% pricing, 28% support, 19% onboarding - is a finding, not yet a report. The gap between the two is everything that happens after the classification: crossing categories against segments, pairing percentages with real quotes, and being upfront about the method behind the numbers. This guide covers how to turn AI-categorized open-ended data into something a skeptical stakeholder actually acts on.
How Much Open-Ended Data Is Enough for AI Categorization to Be Reliable? (2026)
Sample-size guidance for surveys is almost always framed around statistical precision - margins of error, confidence intervals. Letting AI propose categories from your own open-ended data is a different kind of sizing question, closer to \"is there enough variety here for real patterns to emerge\" than \"is this number statistically precise.\" This guide covers that distinction, and gives practical thresholds for when AI-suggested categorization has enough to work with.
Common Mistakes When Defining Categories for AI to Follow (2026)
When you define your own categories rather than letting AI propose them, the quality of the classification is almost entirely downstream of how well you wrote the category definitions - and a handful of specific, recurring mistakes account for most of the disappointing results. This guide covers the ones worth checking for before you run a classification, not after.
Second-Pass Classification: Drilling Deeper Into One Category (2026)
A first classification pass tells you that 34% of responses are about pricing. It doesn't tell you whether those pricing complaints are about the price being too high in general, a specific plan being poorly positioned, or a competitor offering something similar for less - and that's a different, deeper question a second classification, scoped to just that one category, is built to answer. This guide covers when a second pass is worth running and how to scope it well.
Catching AI Miscategorization Before It Skews Your Report (2026)
An individual miscategorized response is a minor, forgettable error. A systematic pattern of miscategorization - the same kind of response landing in the wrong bucket over and over - is a different problem entirely, one that can quietly bend a whole finding in the wrong direction while looking, at a glance, like a normal, trustworthy breakdown. This guide covers how to catch the systematic kind before it reaches a report, not just the occasional individual mistake.
Multi-Label vs. Single-Label Classification: Choosing How Responses Get Sorted (2026)
Forcing every open-ended response into exactly one category is simpler to report on and quietly wrong for any response that genuinely touches more than one theme. Allowing multiple labels per response is more honest and comes with its own reporting complications - namely, percentages that no longer sum to 100%. This guide covers how to choose between the two, and how to read the results correctly once you have.\"
Building a Sentiment Classification Without a Dedicated Sentiment Tool (2026)
\"Sentiment analysis\" sounds like a specialized capability separate from ordinary theme categorization, and for most practical purposes it isn't - positive, neutral, and negative are just categories, built and applied the same way any other classification is. This guide covers how to define a sentiment classification that actually holds up, the specific ways sentiment is harder to categorize cleanly than topic, and when a plain positive/neutral/negative split isn't the right frame for what you're trying to learn.