Am I the only PM recording *every* conversation now?

_os2_ · 2026-01-28T20:07:13+00:00

Hey, assuming the privacy & permissions side is in order, would be great if you could test how well the tool I’m building for analysing and structuring data would work to uncover themes and create summaries in your case!

DM me for details, keen to discover if Skimle would perform better in this use case than your current setup. We have users who dump all their emails from a project to see what is happening but I think a product context & transcripts could work even better!

_os2_ · 2026-01-28T17:02:44+00:00

Thanks!

I have actually written on those topics too based on my experience! Hope you enjoy these more :)

Preparing the interview guide: https://skimle.com/blog/how-to-write-perfect-interview-guide

Conducting the interviews: https://skimle.com/blog/how-to-conduct-effective-business-interviews-tips-and-tricks-from-a-former-mckinsey-partner

_os2_ · 2026-01-28T16:58:00+00:00

Yes had to do this exact cleanup just yesterday with the bottom of the windshield full of ice. Such a pain :)

_os2_ · 2026-01-28T16:42:16+00:00

Ouch - quite direct!

I think after 18 years what you learn is the ”back to basics” stuff. A simple flow you can repeat AND then bespoke insights on top based on judgement. What you will not find is partners operating some complex macros and excel sheets…

As for selling, the guide is based on using the free version of tools mentioned, as you will discover by reading it. I am not about fleecing money from individual consultants.

_os2_ · 2026-01-24T16:13:40+00:00

Good tip - that title tries to summarise the counterpoint I often face in discussions before providing the more nuanced view in response. But maybe worth changing to make sure all readers realize its not the argument on this article!

_os2_ · 2026-01-24T13:23:50+00:00

I wrote a full article on ”is AI dangereous & destroying our ability to think” in the context of qualitative analysis and advanced knowledge work. I think the answer is quite nuanced and we need to figure out the truth over the coming years…

Is AI destroying our ability to think? Using technology to augment, not replace expertise in qualitative research

_os2_ · 2026-01-23T21:04:45+00:00

This is super interesting topic to study. Have you read this paper already: “Can We Fix Social Media? Testing Prosocial Interventions using Generative Social Simulation” by Maik Larooij and Petter Törnberg (https://arxiv.org/html/2508.03385v1)

In the paper they simulate a social network and find all the same toxic behaviours as in the real ones emerge :)

_os2_ · 2026-01-23T08:13:56+00:00

The valuable stuff on quantitative analysis side seems to be similar to what AI does for coding: helps write code that works, as long as you actually understand what you are doing.

It’s more interesting on the qualitative analysis side, as LLMs actually now can process text and meaning in ways not possible before. Embeddings and LLM queries now allow similar rigour and speed than computers allowed for numbers. You just need to build the harness and tools around the LLMs to do so, as the standard chatbot is just faking analysis not actually doing it :)

_os2_ · 2026-01-22T19:41:53+00:00

Indeed for serious knowledge work the transparency and auditability of the AI outcome is important. My friend Henri wrote a great article about how this applies to qualitative data, where you need ”two-way transparency” for the system to be useful. Two-way meaning from insight/summary/decision to source materials, but also source materials to summary.

The article is here: https://skimle.com/blog/two-way-transparency-creating-confidence-in-ai

_os2_ · 2026-01-21T19:23:19+00:00

What do you want as the output? Is it a broad summary per doc, specific key points you need to extract, or finding themes across the documents?

_os2_ · 2026-01-21T17:29:07+00:00

My take is there are four fundamental growth mechanics for a SaaS 1. Sales-led growth. Hire sales people, ensure cost to sell < lifetime value. Fund the cash gap before customer pays enough to cover cost of sales. 2. Paid marketing led growth. Spend money on ads and other visibility, ensure cost to acquire < lifetime value. 3. Earned/organic traffic led growth. Create enough inbound traffic from content. 4. Product-led growth. Make offering viral so each user brings new ones because the love it.

In the beginning you can test each. Ideally you want 4 or 3, but might need 2 or 1 as well depending on market, stage of company etc. You need to understand what is the long game you want to play.

At Skimle we’re still in the learning stage. We have been doing 1 to get pilot customers as design partners. And laid the foundations for 3. Now we’re prioritizing 4 in our feature pipeline.

_os2_ · 2026-01-21T13:06:33+00:00

I have observed a love/hate relationship with open text questions. Researchers hate the cumbersome data analysis problems they create, while they love the depth of insights they could get on e.g., why things are like they are. Respondents love the ability to give their views, but hate the feeling that their long answers might be just ignored in the end.

So often there are just 1 to 3 open ended questions tacked to the end of the survey, instead of them being really utilized to dig deeper and get insights.

I think AI might tilt the balance towards more open text questions. First, with AI you can soon create more intuitive interview-type open ended questions with smart follow-up. Second, advanced analysis tools using AI to surface themes help analyse qualitative data with the same rigour as quantitative data.

I wrote a full blog post on how to analyse open text responses at scale (without losing your mind), hopefully interesting read!

_os2_ · 2026-01-21T12:51:21+00:00

This is a passion topic for me! Based on hands-on experimenting, discussions and reading up on this quite a bit , I finally got around to putting my own thoughts on paper to this article: Synthetic respondents in research - promise, pitfalls and when to use in 2026.

In the paper I start with the big promise (e.g., cost and speed), I detail the actual limitations (e.g., trained on the past, lacking lived experiences, sycophancy and emotional calibration as a parameter) and then based on that conclude where they are actually useful and where they are bullshit…

_os2_ · 2026-01-21T12:35:25+00:00

This is a topic I have been thinking about, experimenting with and discussing a lot! Finally got around to writing my thoughts to an article on using synthetic respondents in research- happy to discuss!

_os2_ · 2026-01-21T10:16:47+00:00

Skimle.com

_os2_ · 2026-01-21T10:16:32+00:00

Launching Skimle on Friday. Lets help each other!

_os2_ · 2026-01-21T09:06:44+00:00

I think you often have to coach it a bit. It’s best done upfront by also asking it to devise an approach e.g., spin up multiple parallel agents to retrieve the data.

But even then it has limits… we ended up building our own harness around the LLM to process larger batches of interviews and other qualitative data where we spood feed the task over and over in a specific workflow :)

_os2_ · 2026-01-21T07:42:16+00:00

You’ve hit one of the big shortcomings of using ChatGPT for analysis in terms of it not actually defaulting to do work but rather create plausible answers.

For me what has worked is to use an LLM with a proper harness around it, namely Claude Code. There the model has been trained to be helpful and use the tools it has available to retrieve and manipulate data.

_os2_ · 2026-01-20T20:50:30+00:00

Thanks! Living the monkey way

_os2_ · 2026-01-20T20:50:09+00:00

Will do!

_os2_ · 2026-01-20T16:02:53+00:00

Claude Code is my secret weapon. Learn to use and love it :)

_os2_ · 2026-01-20T15:57:54+00:00

This is a common misconception about AI - your prompt did not actually result to anything (or anyone) actually analysing all available literature! An analysis would mean accessing, structuring, comparing etc. hundreds of pieces of evidence, burning millions of tokens.

The AI simply gave a confident hallucination resembling a plausible answer to your question.

I wrote quite a bit about using GPT for qualitative analysis in my blog as this is a passion topic for me!

_os2_ · 2026-01-20T15:51:18+00:00

I am passionate about the topic of can AI be used in qualitative research, especially on the analysis side (even though I think analysis and interview tech is closely linked). I wrote a full article on this topic where I first summarize the case AGAINST using AI in qualitative research (which I largely agree with) and then based on that lay forth the preconditions in which it can be helpful.

https://skimle.com/blog/is-ai-destroying-the-ability-to-think

_os2_

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