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The AI-for-Insights Maturity Model

Founder of and CEO of Success Drivers
// Pioneering Causal AI for Insights since 2001 //
Author, Speaker, Father of two, a huge Metallica fan.

Author: Frank Buckler, Ph.D.
Published on: 29.09.2021 * 6 min read

“What else can I do with AI” is what I have been hearing in professional insights groups recently. The number of solutions is exponentially growing, but AI has not yet affected the insights process as this might indicate.

We all have heard of text analytics or facial recognition with AI. More and more applications pop up, and it can feel crowded ….

…if you don’t have a compass, take mine.

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AI Needs Us To Think Different

AI is like a magician buster. A magician does his tricks, and the outcomes are surprising. The audience doesn’t know how this could have happing. AI finds out by looking hundreds of times closely.

Whenever you have data about the input (e.g. text feedback) and data about the outcome (e.g. the themes the text fits into), you can let AI find out the missing formula that can predict outcomes with particular inputs.

The basic idea of AI is straightforward. This simple concept, however, is so different from what we are used to.

We are used to think “hypotheses”. We are used to think that we need to tell the computer what to do, how A may lead to B. We can’t imagine that algorithms can learn about complex behavioral mechanisms just without us.

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AID Framework

Maturity comes in stages. First, kids learn to eat, run, and speak. Later, when entering teen age they can do everything an adult can do. They can be so eloquent you may believe they are adults. But cognitive maturity needs more.

In the same way maturity of AI application for insights evolve.

First, you see that AI is used to automate what a human can do in insights. Humans can categorize verbatims or tell you whether a person looks sad or happy. But a machine can be trained to do this at scale.

The second stage is to use AI for automating marketing, service, or sales activities – fulled by stage one. Again, a human can do all this too, but deploying a machine has costs, speed and quality implications.

The ultimate stage, however, is to use AI do discover insights about the link between input and output, between cause and effect. This links the information from stage one to inform stage two. Here is where the ultimate AI-insights-Loop closes.

#1 Automate insights

Most AI you know is automating what’s already here. But automation does not just mean lower costs and higher speed. As a result whole new research procedures evolve.

  • Text analytics: helps us to categorizes customer feedback. It is the process to quantifying qualitative text-based information. Today it can have the same quality as manual humanmade categorization even in more complex B2B settings. AI can even categorize the emotional side of text. It can read for instance anger, sadness or surprise. It can spot the tonality beyond the actual meaning of the words.

  • Association AI: Words are unconsciously associated with each other, like Milk and Cow. There is AI that extracts the recent associations that are implicit in public text data. This astonishingly is very close to what you can measure in Implicit Association Tests. As such, the tool can serve as a proxy. Mainly it is used where it is economically not feasible to apply IAT. Recent applications use this AI to find words and language that better resonate with your audience.

  • Facial recognition:  Based on the research of Paul Eckman there is a validated theory of how mimics can be interpreted as emotions. It includes 7 base emotions happiness, sadness, surprise, disgust, anger, fear, and contempt, plus 12 styles of joy. The machine now can even more reliably as humans read mimics and minor mimical cues from pictures and videos.

  • Voice analytics: Analyzing voice can be done today with a variety of cloud-based services. You can transcribe it into text, and you can read emotional cues in the voice. It is even possible to detect a COVID infection just by analyzing the coughing sound of a person.

  • Eye-tracking AI: Thousands of eye-tracking studies had been used to train AI models with the intention to predict eye-tracking outcomes without actual eye-tracking. It can undoubtedly be dependent on the context and the target group to where audiences look at first. But research shows that up to 90% of fixations are independent of this and actually hardwired. With this, you can analyze ads, websites, popups, or even newsletter emails before launching. It enables to faster iterate and improves the impact of assets.

  • Visual analytics: Similar base technology is used to understand the content of pictures and videos. The same way AI can be trained to detect emotions or to predict eye-tracking results, visual analytics platforms today detect by default the object a picture has, like a table, skirt, a man or clouds. Those machines can even be trained to see more abstract concepts like “stylish interior”, “pop culture”, or “a teens room”.

While all this is amazing already, the true power of AI comes with combining it with the other two stages of AI maturity.

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#2 Insights into actions

There is no value in insights unless you do something useful with it. Here are some tech utilizing AI to improve marketing and sales actions

  • Chatbots: I don’t mean this cumbersome tree type question branching system that are mostly in use today. The basic use of AI in chatbots is to understand unstructured text and respond in a predefined manner. The more advanced chatbots use AI that actually mimic responses of actual humans. They are trained on hundreds of customer-agent chats and will respond to customer requests just like a typical agent. Sure this comes with limits, and it will not be able to chat on any off-topic comment.

  • Text synthesis: the GPT3 technology from Open-AI made it in the press in 2020 and led to a wave of text synthesizing services. AI today can write emails, poems and whole articles. In most cases, it still needs human review. But it has been proven to not just speed up the copywriting process but also help with ideas and inspirations. Leading applications that use GPT3 are now designed to not just produce well-written copy, but copy that works. This means emails that convert, slogans that people will remember and subject lines that make people open emails.

  • Picture and video synthesis:  Early versions of GPT4 now not only produce new texts but pictures and videos that are purely fictional. Besides the cost-saving effects (less photo and video shoots), the real power lies in the ability to teach the system to produce converting or convincing pictures.

  • Deepfake avatars videos: These are services that use a given video and change typically the face and let the person speak new arbitrary sentences. With this, you can produce very quickly explainer videos and even a video service interface, without the need to actually record a video.

Voice synthesizing: Computer-generated voice is actually a four decades-old discipline. So far it has always been rule-based and computer voice always was somehow recognizable as a computer. This changes now with AI as those flexible algorithms can inject those little imperfections that make a voice feel human.

#3 Discover insights

AI today can automate a variety of research processes, from text coding, facial reading to eye tracking. Then AI can help synthesize copy, pictures, videos, and one-on-one service interactions. 

But still, something fundamental is missing. 

Automating to read customer feedback still does not include 

Automating to read customer feedback still does not include understanding which of the customer feedback has to biggest impact when improving the matter. 

Also, to craft a good copy, I need to know what separates a “good” from a “not so good” copy. 

Any marketing and sales action relies on a simple assumption. The causal assumption is that an action will lead to a particular outcome.

AI now can help us to find those models and causal assumptions about the world that will be most impactful.

AI-powered operative learning loops

Imagine you run a weekly newsletter that drives traffic to your website and specific offers. AI can be used to optimize the conversion process in many stages. Image it increases open rates from 40 to 50%, click rates from 4 to 5%, and landing page conversion from 4 to 5%. This will result in a sales increase of 100%.

Based on enough examples, AI can not only predict which subject line, picture, and copy will convert better, it can also tell us why. 

Further, we can use AI to create subject lines, pictures, and copy at scale and use this to perform multivariate massive-scale experiments. Instead of sending all 10.000 recipients one or two versions, you can now send 500 different versions every week. 

Causal AI then can learn from the outcomes of these massive experimentations.

Causal-AI powered strategies

Besides the tactical optimization of marketing and sales process like the newsletter sent out, AI is used to understand winning strategies.

This is actually where Success Drivers has specialized in for more than 10 years. We helped T-Mobile to build a winning go-to-market strategy. We found the right product strategy for SONOS to foster growth. We enabled METLIFE to distill the winning DNA of successful advertising.

Now we are focusing with CX.AI on gaining strategic directives with AI from the customer feedback nearly every brand gathers today.

Its outcome is even used to enable proper organizational learning as feedback shouldn’t be just delivered to the frontline. Instead, it needs an AI-powered filter and sorting process to make customer-facing units draw the right conclusions.


AI is here to stay. Stage one of the maturity curve is slowly diffusing into the insights process for a few years already. New developments of AI solutions help to augment and automate customer interaction. 

All this makes the steps faster, cheaper, and more consistent. It is the ingredient to scale and to automate.

But a new quality level of insights is enabled by stage 3 of the maturity curve: applying Causal AI to understand the link between actions, context and results.

Technology for Causal AI is on the rise for more than 10 years. It is still waiting for its breakthrough simply because it is the third and final stage of AI maturity.

Research leaders who understood the enabling power of Causal AI are utilizing it already successfully today. 

If you want to dive into this field, the website of the pioneers, Success Drivers is a good start:, and of course, its core produce CX.AI 

Now I have a question: Was this article helpful? 

Please DM me directly with any comments or questions 


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