I work out of a coworking space, and over the last few months I've ended up talking shop with a UX researcher at Amazon, an analyst at Evalueserve, and someone who runs operations at Nielsen. 

One of them walked me through how he builds a report. His company gives him NotebookLM, which he uses to visualise the report. Then he downloads the slides and rebuilds them in PowerPoint, since NotebookLM doesn't give you editable slides. That conversation stayed with me while we were building our own report writing feature at flowres.io. He was using a tool built for research, yet it made him do the work twice.

Search ‘best AI presentation tool,' and you'll find dozens of them. Most of those are articles published by competing presentation tools, reviewing each other (like this one, perhaps. Even the more truthful reviews test the same thing: What good a deck about a fictional coffee brand looks after one prompt. That's a reasonable test if you're a founder writing a pitch, or a student writing an assignment. However, it tells you almost nothing if you're holding twenty-four transcripts, a client's PowerPoint template, and a delivery date.

So, here are four questions I believe researchers would ask instead.

1. Can you control what goes in?

Most of these tools started as prompt-to-deck. Now, nearly all of them allow uploads:  transcripts, PDFs, audio, links. For instance: Gamma's agent flow takes multiple files, links, and pasted text in one session. Beautiful.ai accepts documents, PDFs and links as source material.

Yet, just Ingestion doesn't give researchers the control they need. Qualitative research analysis involves processing before analysis. For instance, fixing transcripts. Machine transcription gets brand names/ medical terms/ speaker labels wrong. In most of these tools, the file you uploaded is the file the model reads (errors included), and the error travels unnoticed onto a tool-generated slide.

Arguably, one way to tackle this is to use a separate transcription tool and a separate analysis tool. Even then, one challenge remains unresolved. As a researcher, you don't want a deck generated directly from transcripts, or even from your analysed data. You want to craft your own story: pick up the pieces of data that matter, bookmark what you like, add your thoughts, observations and interpretations. Uploading raw transcripts (or even your analysis) is like handing over a haystack to AI and asking it to guess which needles you care about.

2. Can a claim be traced back?

Most of these tools treat your document as raw material for a story. They compress it, paraphrase it, restyle it, and hand back a fluent-sounding slide, with no way to check what happened in between. Feed in 3000 words, and you'll get 12 confident-sounding slides, with no route back to a single transcript on who said any of it. However, in qual, a paraphrased verbatim is generally considered a fabricated verbatim.

3. Are the slides editable objects, or pictures of slides?

Click a text box and try to retype it. If you can't, it's an image the tool produced rather than an editable slide. Prompting slides one by one can fix this, but you can imagine how tedious that can get for a researcher working on a tight timeline.

4. Does the output land in your template?

We interviewed 10 flowres.io users about how they want slides generated. Two distinct wants emerged, and they pulled in opposite directions.

The first was to hand over the template to the tool and let the AI do the copy-pasting. The user has already decided what the deck should look like. The client's template and layouts are fixed. What they wanted automated was the tedious middle: moving analysed data into slides that already had a layout.

The second group wanted visualisation. Give the tool the data and let it find a way to visualise… hopefully, providing something they wouldn't have thought of themselves.

A tool that's good at the second is usually poor at the first, because generating a striking layout means owning the layout. You can't simultaneously honour someone's existing master and design a better-visualised version of it.

The five tools that we evaluated

1. Claude is two products, and conflating them causes most of the confusion. Claude for PowerPoint runs as a sidebar inside PowerPoint. It builds into your existing slide masters and heading styles, generates native charts and diagrams, and edits the object you've selected. It's available on all paid plans. Because it never leaves your file, it's the strongest general-purpose answer for questions 3 and 4, especially for the group that wants the template respected and the copy-pasting done. Claude Design (in beta) is the opposite bet: outline to finished deck, exporting PPTX, PDF or HTML, and it can ingest a real design system rather than making you pick a theme. However, most visualisations are too basic. More importantly, neither option satisfies criteria 1 and 2. You can’t really control what goes in.

2. Gamma has travelled further on data input than I expected. Its agent flow takes multiple files, links and pasted text in one session: PDF, Word, Google Docs, PowerPoint, websites, images and lets you shape an outline before it generates slides. Exports keep text, images and shapes adjustable, and theme fonts embedded. Gamma documents its own export caveats more openly than most: exports deliver ‘Present’ mode rather than ‘Edit’ mode, gradient headings can flatten to one colour, advanced effects fall back, italics emerge as a slanted regular weight. The gap for agency work is question 4. Gamma is very good at Gamma's design system - not at your client's master.

3. Beautiful.ai has some interesting features too. Smart Slides re-flow a layout automatically across a large library, and a team can set brand colours, fonts and logos once, with locked themes (on a Team plan). That helps with question 4, though it still requires setting everything up first. And it's additional work every time you have a new template.  It accepts documents, PDFs and links as source material. Editable PowerPoint export is the problem, similar to Gamma. The company has published a piece called "How to Export to PPT and Why You Shouldn't", recommending you design and present inside Beautiful.ai. That can work if you're willing to work in and learn Beautiful.ai.

4. NotebookLM is the only one of the five that’s grounded in original sources, which makes it the best answer to question 2. It handles up to 50 sources  - enough for a typical qual project. However, it fails question 3 architecturally. Decks render through an image model (Nano Banana Pro), so the PowerPoint export still arrives as images. Although the visualisation quality is possibly the best, editing anything in slides requires a prompt instead of retyping in a text box. You can't add or remove slides, and revisions don't reconsider your sources. That means rebuilding every slide again. Though announced in February, export to Google Slides is not released at the point of writing this article.

5. flowres.io’s SlideBuilder works from data already inside the platform. You can conduct interviews/ groups on on Zoom/ Teams/ Meet, get transcripts on flowres.io, get them corrected, get speakers labelled during analysis, analyse the data thoroughly and then bookmark the data pieces you want to include in the storyline you want. Slide generation is done from the bookmarks, rather than from raw data. Outlines are rendered first, and you can correct anything before generating the slides. flowres.io’s analysis gives text and video citations, which means all your analysis (and eventually your slides) traces back to the transcript or the video clip of the respondent saying it. Because analysis and slide-writing sit in the same place, there are no upload/ download steps involved.

flowres.io deliberately addresses question 4. The PowerPoint add-in lets you pull analysed data into your own template inside PowerPoint while Visualisation runs on the web within flowres.io (for those who want the tool to find suitable images). Our exportable slides preserve template fidelity. The distortion of text colour and formatting can still happen, like Gamma or Beautiful; although flowres.io visualisations still consider templates - unlike other tools.

One limitation worth mentioning is that all of this depends on the project living in flowres.io. If you don't use flowres.io as your analysis tool, you can't use it for PowerPoint either. And since flowres.io isn't a general-purpose tool, you can't use it for a conference keynote or any presentation that involves generating the content first.

Side-by-side comparison

Try this

Take a project you've already delivered. Run it through two of these tools. Within ten minutes, you'll know which of them was built for qualitative research and which was built primarily to visually impress its audience.

Jiten Madia is the founder of flowres.io, an online qualitative research platform covering project setup, data collection, transcription, AI-assisted analysis and reporting, built for market research agencies and insights teams running qual at scale.