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Making sense of SenseMaking – the AI version

I refer to this post as the AI version because here is the human version: Making sense of SenseMaking. That was written in 2016. In the 7 years since, AI happened and is turning SenseMaking on its head, or so I think. At least we have to change how we think about it and what we do about it. As I write more about the new developments in generative AI and a trend report, it was time to revisit this fundamental and related concept.

Why AI changes everything

First to say, that some things stay the same and should do. Let me bring in my original doodle that I used to explain things – the link above provides the elaboration if needed. Just so you have the context readily available.

Back to my point: Take human judgement for instance – this is not going anywhere. In fact, when using tools like ChatGPT to help you get answers, it is automating so many of the things you see described in the doodle above, but your judgement remains critical, amongst other things.

The input side is what AI does so well, trawling all of the available data from all publicly available sources, organising it and presenting it to you to be able to make a judgement call on what is useful and what can be used by you on the output side.

Experiential input from AI is debatable. Lived experience is a depiction of a person’s experiences and decisions, as well as the knowledge gained from these experiences and choices. Using that knowledge in what guides your future actions is what I meant in my original thinking. AI can learn and get better so in that sense, you could call it a “lived experience”, where it gains knowledge from past experience and your input to improve its responses. But this may be a stretch.

Certainly it does fantastically well with data as mentioned and even ideas of others – as long as it is diverse and inclusive in the data and ideas it incorporates. Peer review is closely linked to this but happens at the point of SenseMaking. You could see AI performing the function when you ask it to change its response because on first try it was not accurate enough, but this is also probably stretching the concept a little.

The output side is arguably something AI is less useful for, after all, it is still mostly humans taking the decisions and actions. Unless you look at things like code generation Рgreat example here: GitHub Copilot · Your AI pair programmer.

How to SenseMake in this brave new world of AI

AI was purpose built for SenseMaking, it’s like the supreme SenseMaker, at least in terms of what current intentions are. Below are some areas you may want to explore to get better at using AI in your every day work. These do not cover some of the more technical skills needed, if you are a developer for example and are building chat BOTs with AI. They also do not cover outputs beyond text, like images and code. Let us say we are dealing with knowledge here.

1. Prompt Engineer

To get the right answers you have to ask the right questions. That is a little of what prompt engineering is. It’s already being called a skill for the future, a career even: How to Begin a Career in Prompt Engineering.

There’s a lot more to it than that but at the very least you have to know how to ask the right questions to get the best output. Always a good skill to have, now it becomes even more important.

2. Sense Checker

In order to work effectively with the answers you get, you cannot plead ignorance and say the machine knows it all. To properly excel, in co-operation with AI, you still need domain expertise. Even more so perhaps, in order to sense check responses.

In other words, it’s no good asking the right questions, when you cannot validate the answers.

3. Hypothesiser and agile problem solver

You could view working with AI as an experiment. You ask a question, after sense checking you find the answer is only partly right. You steer the tool to provide more answers in new directions, you improve but need to do more. Each stage gains you more but you have to do more to advance

This experimental approach requires you to have a clear view of what you are trying to prove or solve for – so begin with the end in mind and clearly state your problem or hypothesis against which you can constantly sense check.

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