Blog

Why is it so hard to get approval for sustainbility projects even when we know how important it for the planet that we take action?

We need to understand how managers make decisions:

New technologies enable us to do things that weren’t possible before.

I recently read “The Notebook: A history of thinking on paper” by Roland Allen.

It argues that there are types of thinking that we just can’t do without a technology like paper.

Complex maths. Detailed arguments. Scientific research.

So what does AI technology help us do that we couldn’t do earlier?

It takes a lot of time and effort to build deterministic systems – something that provides a predictable, reliable and repeatable output.

That’s because I have limited time, knowledge and capacity – and I have to work within those constraints.

AI has sped that up. I’ve rebuilt in two days something that took me six months to create the first time around.

The problem with AI is that it is stochastic – and works on probabilities rather than certainties.

As a result it mimics creative, expressive and structured work – creating text, images, videos – or ingesting your mess of meeting notes and coming up with a passable summary.

Using this output directly is problematic. It comes across as false, as if you haven’t put the work in, like you’re passing off something else as your own.

But its unpredictable output is fine in situations like a chatbot that helps you interrogate a knowledge base, or a shopping assistant.

Here’s where I’ve leaned into using AI

1. Generating code
2. Fixing code
3. Strategy and planning
4. Research
5. Drafts for compliance and boilerplate copy
6. Text to voice
7. Image animation
8. Avatars that narrate text

And where I wouldn’t use it (yet).

1. Writing posts / papers / books
2. Outreach
3. Decision support (without careful study)

Like most people, I’m still working through how to integrate these new technologies into my ways of working.

But I think it’s safe to say that using it will make it possible for you to do things that were pretty much impossible before.

How are you choosing to use or not use AI?

Will AI destroy my business?

If you are a creative professional, a writer, an artist, even a consultant or programmer, a reflex action is to distrust AI.

But it’s here. And it’s affecting the way we generate and produce everything.

So what will it do to our businesses?

A model that seems likely to repeat itself is what happened with commerce on the Internet.

Once upon a time you went to a showroom and a salesperson helped you choose a car. If you liked a salesperson you might be swayed towards one model rather than another.

Now that world has disappeared. You know what car you want, you’ve compared it to all the others, and you even know which location has it at the best price – and you just need to click and reserve it.

The world where you paid a bit more for a bit better service disappeared.

It’s the disappearing middle phenomenon, where the stuff that’s “ok”, that’s so so disappears, and you’re left with two extreme business models.

At one end is a low-cost, self-serve model. It doesn’t matter how much use you make of AI as long as you create something of value.

Free YouTube videos, low cost books, cheap courses. Products that people can access and use – but that you’ll need volume to profit from.

At the other end is complex stuff that requires judgement, accuracy, verification and trust. Work that needs people in the loop.

But clients won’t pay for time and money. They’ll pay for outputs and value.

This model was visible around ten years ago, and consultants who shifted to fixed price work rather than hourly rates saw it coming. AI just accelerates the shift.

I’m seeing terms that show these extraction mechanisms as as DIY, DWY and DFY – do it yourself, done with you, and done for you.

In a nutshell – no AI won’t destroy your business, but your business model will have to change, especially if it’s based on hourly billing, to stay competitive.

I avoid thinking about words like “ontology” and “epistemology” – unless I have to.

A recent paper argues that work by Churchman on the philosophy behind inquiring systems, practically applied by Checkland in soft inquiry suggests at “ontological models of organisational behaviour were inappropriate”, and only an epistemological perspective provides a “basis for understanding complex systems that characterise human society” (Stowell, 2024).

Wait, what?

A lot of people suggest that ontology – classification and explanation – sits at the top of a hierarchy. Get the ontology right and you’ve done the important work.

This is a critical component of AI – it’s the idea that the knowledge base or scaffolding that goes into an LLM gives you “truth”. It’s the structure that holds knowledge, that shows it in relation to other knowledge.

The implication is get the structure of the system right and you’ve solved the problem.

Take my field for example, carbon reporting. There are thousands of systems that help you organise and classify information.

Then why are so many people frustrated when they actually use these systems? Why do so many end up going back to spreadsheets?

It’s because there isn’t a single accepted model of knowledge when it comes to decarbonisation. The GHG protocol is a framework, a starting point. A tobacco company is going to have a different situation to a clean tech firm. When you actually work with a firm you quickly discover peculiarities, edge cases, complexities that don’t fit neatly into the system’s ontology.

And systems aren’t famous for being flexible. We learned from trying to use ERP software that it’s easier to change your business to fit a system than the other way around.

Here’s the takeaway.

Stop looking for a system to solve your problem.

Instead, think about what you want to know about your business and how you want it to change.

Then design and build a system that wraps around your business and helps you get that done.

Do you think having real-time information is more helpful than getting a single end of day report?

When I was studying computer systems they taught us about why computers hang – you know when everything just stops working.

This happens when you’ve got too many things open. Run enough processes, open enough windows, ask the computer to do too much – and it will freeze.

This is because computers do one thing at a time but switch between them quickly.

So, the computer switches to a task, loads everything into memory, and then runs out of time, so it stops, and loads the next thing in. And it keeps doing this – loading in information but never actually having the time to process it – so nothing gets done.

The way to solve this is by reducing the number of interrupts. Close things down. Reduce how much switching you need to do.

We don’t get letters through the post much, but it’s the difference between getting the post and checking your phone. You are on your phone constantly – but nothing really happens of much value. The post comes once a day, and you deal with it then – leaving the rest of the time free to focus on work.

Monitoring something like your carbon numbers once a day, for example, is enough to give you a sense of where you are and if you’re on track or need to do more.

The solution to overwhelm, is to do less – preferably one thing at a time.

Getting stuck is a necessary precondition for progress.

One of my favourite books is Robert Pirsig’s “Zen and the art of motorcycle maintenance.”

For an engineer, the test of something is “does it work?”

And an essential part of making something work is first getting stuck.

I’m reminded of Pirsig’s words every time I come across a problematic screw or bolt.

Nothing stops you in your tracks more effectively than being unable to get at the thing you want to fix.

Our christmas lights broke recently. The transformer that powers them seems to come in a solid block. There appears to be no way to get into it to repair it.

That sort of block in everyday organisational life can paralyse people. We just can’t get things done becuse of the other things that are in the way.

One way to try and solve such problems is to start writing things down.

This rather simple activity is at the heart of the research I’m writing up for my PhD – how listening to people and taking notes can help us understand situations and come up with plans of action to improve them.

It’s essentially the scientific method.

If you’re stuck – if something isn’t working, if a tool you’ve bought or a process you’ve implemented isn’t working the way you hoped, it’s easy to get frustrated and discouraged.

But really, it’s a learning opportunity. Getting stuck shows you there’s a problem. Working on getting unstuck helps you come up with a solution.

This is the way we’ve built every service line that’s added value over the last 20 years.

We make poor decisions under pressure, especially if we’re making them on our own.

The solution is to be able to call on a braintrust.

I’ve been skimming through Ed Catmull’s “Creativity Inc.”, a memoir about the culture at Pixar.

In the movie business directors and producers commonly get feedback in the form of extensive written notes.

At Pixar they did things differently, pulling experienced people into a room to give feedback on the product they were working on.

With candour.

Many organisations operate in a command and control mindset, where orders are given and expected to be followed – a way of operating that comes from military metaphors of structure.

This discourages feedback.

But if you get a group of smart, opinionated people to give you fearless feedback on where things are, you’ll learn more and make the product better.

But you have to get the right culture in place, one that permits openness and honesty rather than penalizing it.

This doesn’t come naturally and Catmull talks about ideally having a facilitator – someone that can help a group stay on track and talk through these issues in a constructive manner.

It’s not the easiest thing to do as anyone who has sat through a bad meeting will remember.

So, if you’re struggling with getting your teams aligned, maybe it’s time to look at how you can have better conversations.

A good place to start is by reading about problem structuring methods or PSMs.

Let me know if you have any other good resources or examples.

How can we build better businesses?

The only way to be create change is by asking difficult questions.

This is hard to do when you’re inside an institution.

Stafford Beer wrote that when the Emperor asks for dumplings, you give him dumplings.

When someone in a position of power asks you to do something, however foolish, you can do it. Or you can quit.

If you’re going to question powerful people it’s sometimes easier for them to remove you than answer them.

But in the long term, such actions result in weaker institutions and eventual decline.

That’s why wise leaders need people around them that are willing to speak truth to power.

If you want to build better businesses you have to be willing to question the taken-for-granted ways of doing things now.

You also have to question the products and strategies that are proposed to you to improve the situation.

That’s the Socratic method – assume nothing, and question everything in the pursuit of the truth.

It’s a risky strategy. The famous painting of Socrates shows him still challenging opinions while reaching out for the glass of hemlock that’s going to kill him.

But that’s the only way we can make things better – by putting our thinking to the test.

If you’ve ever presented to a decision maker you know what happens if they spot something’s off.

If one thing is wrong, the whole thing is wrong.

Go back and check it again.

I’ve recently been on the giving and receiving end of AI generated material.

For example, I ran some company reports through a couple of tools I’d built.

These tools used different approaches to analyse the content of a document and give me a report.

Both reports had issues, things that I could spot immediately.

I now had two choices – take the report to someone else and point out that there were some errors.

Or review the source information, and cross check what had been produced?

What would you do?

Well, when I was given information that was wrong recently – or more accurately – clearly hadn’t been reviewed, I didn’t go ahead with the deal.

I think we need to figure out where AI sits in workflows – and I’m starting to believe it’s not a solution.

It’s not something that’s going to replace all your people – although you might stop hiring for certain roles.

It’s a tool.

What you produce is better or worse depending on how you use it, and how much you put into validating what comes out of it.