u/michalism

AI can now generate 100 ads before you've finished your morning coffee.

But here's the uncomfortable question:

If we can produce 100 times more content, are we making 100 times better decisions, are we getting 100 times more engagement?

Or are we simply flooding the internet with increasingly similar messages that turn customers off?

Perhaps the next competitive advantage isn't generating more content.

That said, I heard from contacts in Asia that L'Oreal has published 2 million organic social media posts in the span of 12 months just in one country? Any thoughts on why they would do such a thing. Apparently they now have an AI base content creation engine.

What do you think? Is AI improving marketing effectiveness or just increasing marketing volume?

We're using AI to market a book about AI.

Our provocative organic LinkedIn post reached more than twice as many people as our conventional promotional post.

But both received the same number of reactions.

Neither result tells us which would sell more books.

So our next experiment is to compare problem-led advertising with solution-led advertising.

Which do you think converts better: highlighting the customer's problem or presenting the solution?

We're using AI to market a book about AI—and it just told us to stop marketing the book.

Our least promotional LinkedIn post has generated nearly 3× the impressions of our conventional promotional post. Is this a surprise to anyone?

Synthetic consumers can answer questions. But can they predict what humans will actually do? Where should synthetic research stop and human validation begin?

Are you surrounded by endless data but still searching for actionable insights? The book AI Powered Insight, explains how to transform raw numbers into decision-ready intelligence. An AI based analytics platform connects your systems, contextualizes information, and delivers clear, practical insights—so you can lead with confidence. Discover how clarity can drive your business forward. https://www.routledge.com/.../Michae.../p/book/9781041024286

My co-author Nicos and I have worked together for 28 years across four insights businesses.

We recently published AI Powered Insight, largely about one problem: how AI can help turn insights into action rather than simply generate more analysis.

So we're trying an experiment.

For 30 days we're going to use AI/DataVinci to help market the book using the same process: listen → identify an insight → act → create → measure → adapt.

I'll share what works and what doesn't.

The question I'm most interested in:

Is AI actually making insights more actionable — or just making insight generation faster?

Suppose AI analyses thousands of customer comments and discovers that customers dislike your packaging.

Should it:

A) Report the insight
B) Recommend alternatives
C) Create and test alternatives
D) Actually change the campaign

We've spent years discussing whether AI can produce reliable insights.

I think the more interesting question is becoming: how much authority should it have to act on them?

Where would you draw the line?

My co-author Nicos and I have worked together for 28 years across four insights businesses.

We recently published AI Powered Insight, largely about one problem: how AI can help turn insights into action rather than simply generate more analysis.

So we're trying an experiment.

For 30 days we're going to use AI/DataVinci to help market the book using the same process: listen → identify an insight → act → create → measure → adapt.

I'll share what works and what doesn't.

The question I'm most interested in:

Is AI actually making insights more actionable — or just making insight generation faster?

My co-author Nicos and I have worked together for 28 years across four insights businesses.

We recently published AI Powered Insight, largely about one problem: how AI can help turn insights into action rather than simply generate more analysis.

So we're trying an experiment.

For 30 days we're going to use AI/DataVinci to help market the book using the same process: listen → identify an insight → act → create → measure → adapt.

I'll share what works and what doesn't.

The question I'm most interested in:

Is AI actually making insights more actionable — or just making insight generation faster?

You have $50,000 to understand why customers are leaving.

Would you spend it on:

A) Surveys and focus groups
B) Analysing thousands of real customer conversations with AI
C) Combining both
D) Something completely different

You only get one shot. What do you choose — and why?

When an AI feature recommends marketing copy, merchants often need to understand why the suggestion was made before they will trust it.

A pattern we have found useful separates the process into four layers:

1. Source evidence: the relevant public conversation, comment or competitor post, with its platform and collection date.

2. Structured insight: the recurring topic, customer emotion, purchasing concern or product benefit extracted from the evidence.

3. Creative brief: the proposed audience, message and channel, linked back to the supporting insights.

4. Generated output: the caption, advertisement or image, followed by an explicit merchant review step.

The merchant can move backwards from the generated creative to the insight and then to the supporting conversations. This makes the output easier to evaluate than a suggestion produced by an unexplained prompt.

For a Shopify implementation, useful controls include keeping Shopify product data separate from external conversation data; recording which evidence and brand-guideline version supported each output; preventing automatic publication before merchant approval; avoiding unsupported performance or product claims; and retaining only the store and customer data required for the feature.

One unresolved design question is what should happen when an original public source is subsequently removed. Should the derived insight remain with an 'evidence unavailable' notice, or should it be deleted with its source?

How are other Shopify developers handling provenance and source deletion in AI-assisted features?

A question for anyone working in insights, on either side of the buyer/supplier line.

When did something last change — a product, a price, a call-centre script, a piece of creative — because of research you ran? Not a deck that was well received. A decision that went differently than it would have.

I ask because I think this industry measures itself on delivery rather than consequence, and I do not have a clean answer for my own work either. Plenty of studies I have run over the years were read, praised and shelved.

So in the cases where something did change: what made the difference? Was it who was in the room, the format it arrived in, the timing, or the finding itself being impossible to ignore?

I have theories and no data, which is an uncomfortable position for someone in this trade.

Today, getting a good customer insight can require significant time and money.

Soon, AI might generate hundreds of them instantly.

When everyone has unlimited insights, the competitive advantage may shift from finding insights to knowing which ones deserve action.

Does that make insight more valuable — or less?

It can replace the boring parts.

Cleaning data.
Coding responses.
Finding patterns.
Summarising thousands of comments.

That could leave researchers with more time for the part that matters most:

figuring out what it actually means. Anyone disagrees or has a different take on the subject?

There's a lot of discussion about what AI can do.

Analyse data.
Find patterns.
Write reports.
Generate creative.
Predict behaviour.

But perhaps the more interesting question is:

What can humans still do better?

My current thinking is that some of the most valuable human capabilities may actually become more important as AI improves.

Things like:

• Asking the right question
• Understanding organisational politics
• Knowing when data doesn't make sense
• Recognising a cultural nuance
• Making a difficult strategic trade-off
• Taking responsibility for a decision

In other words, perhaps AI doesn't eliminate the need for human intelligence.

Perhaps it makes judgement more valuable.

Do you agree?

What is one thing you believe humans will still be significantly better at than AI in marketing or consumer insights 10 years from now?

We’ve just published AI Powered Insight: Marketing Research Reconfigured, with Routledge.

The main focus of the book is simple: how to use AI to turn insights into action.

We look at how AI is changing the way we collect, analyse and understand consumer data — but, more importantly, how organisations can move beyond producing insights and actually use them to create posts and ads that their customers care about. You know how we know they care? Because they say so on social media platforms every minute of every day.

It’s not a book about AI hype. It’s about making customer insights more useful, more actionable and more connected to what marketers actually need to do based on millions of online comments.

If you work in market research, consumer insights, marketing, advertising or AI, I’d love to hear what you think.

AI can turn messy data into a beautifully written insight in seconds.

But a convincing answer isn't necessarily a correct one.

In marketing and research, evidence matters more than eloquence.

The question I keep coming back to is:

How do we make AI show us why we should believe it?