Category: SSM

I came across a term recently that should guide how we use AI.

The “chain of understanding”.

If you watch cop shows, you’ll have heard of the chain of custody – the process that ensures evidence can’t be tampered with.

We need something similar for AI.

AI can generate huge amounts of content – but our ability to absorb and verify it hasn’t changed.

So do we really read and understand what it produces? Or do we trust that it’s right?

Yesterday, I asked two different AIs the same question. They gave two confident but contradictory answers.

That’s the risk.

In many contexts, choosing the wrong answer has an impact radius – affecting millions in investment and rippling through supply chains.

The issue isn’t speed.

It’s whether you understand the logic underpinning a decision or how a program actually works.

That’s the chain of understanding.

AI can generate answers. It can’t take responsibility for them.

If you can’t explain it, you probably shouldn’t act on it.

As an engineer, I want to solve problems. As a consultant, I’ve learned that’s not enough.

Life rarely gives us neat, well-defined problems.

It gives us messy situations, with argumentative stakeholders, unreliable data, and tensions over culture and power.

We don’t operate in a laboratory. We operate in a wicked messy swamp, requiring soft skills to address practical issues.

You can see the world as full of problems to solve, or as problem situations to improve.

Success looks different in the second view.

It’s not about the “right answer” but about getting stakeholders to commit to the next action.

Because without commitment, even the best solution goes nowhere.

That means:

• learning your way through the situation
• negotiating between perspectives
• agreeing a direction
• and committing real resources.

And here’s the twist:

When you focus on what people actually need and, you often end up with better solutions anyway.

References

John Mingers. 2011. Soft OR comes of age – but not everywhere

You know that question you ask your cat when it drops a mouse at your feet?

“I can see you’re very proud of yourself, but what do you want me to do with this now?”

That’s how I feel when someone presents me with work they’ve made using AI.

Here’s an example. Let’s say you ask a junior consultant to generate market research and a go-to-market plan for one of your clients.

You get given 20 pages of output.

You ask the junior to talk you through the material.

Ideally, they’ve read the 20 pages, validated the information, sense-checked against prior knowledge, and can confidently articulate the situation and explain what needs to be done next.

All too often, you’ll get the same blank look the cat gave you when you asked it the question.

AI does not give you less work. In fact, you’ll probably end up with more.

Here’s the takeaway.

AI creates output.

Professionals create accountability.

When you have the same conversation six times – something is going on.

“We know what we need to do – the challenge is getting it done with the resources we have.”

The instinctive response is to hire.

Build the team. Add capacity.

It’s 2026. The way we work has changed.

You need three things:

• Leaders: internal champions who move things through the organisation
• SMEs: internal and external experts to plan and execute jobs
• Systems: tools, automations, agents and processes that complete tasks

The real shift is deciding what people should do, and what they shouldn’t.

Five years ago, I hired teams to get data work done.

Now, we build processes.

Identify where the data lives. Connect to it. Speed up how tasks are executed.

We still need people – they just do different work.

• Negotiating data access.
• Ensuring data flows regularly.
• Providing quality control.

But this takes 90% less time than the old way.

That frees up time for what actually matters.

What is the data telling us? Can we trust it? How do we communicate insights? What does this mean for strategy? What do we do next?

Stop hiring for tasks.

Start building systems for transformation.

This extract is at the end of a Peter Checkland (1992) paper I was reading, and so, of course, I looked up Schopenhauer.

I’ve recently been studying “explanation” as a sense-making device, in the context of strategy making by organisations.

In essence, this is the idea that structure is not something external to people but something that they construct based on how they explain the way in which they see the world.

In practical terms, this makes the difference between arguing for investing in sustainable technology or waiting, between starting a war or compromising to keep the peace. Explanations that make or break the future.

So what are we trying to explain?

Schopenhaur argued that there are four kinds of objects and four corresponding types of explanations.

1. Material objects, explained with cause and effect reasoning
2. Abstract objects, explained with logic.
3. Mathematical and geometrical objects, explained with numbers and spaces.
4. Psychologically motivating objects, explained by motivation or moral reasoning.

Problems arise when we try and apply one style of explanation to a different type of object.

I see this problem all the time in my consulting practice.

Here’s an example. You have a leadership team that wants to build a decarbonized company. Should you therefore replace your gas boiler with an air-source heat pump?

The first problem is one of motivation. Do leadership believe in the case for decarbonisation? Are they forced to do it by supplier pressure? By regulation? What motivates them?

The second is a cause and effect problem. Will the ASHP meet heating demand in all situations? Are operating costs equivalent?

It’s when we mix modes of explanation that we end up with circular and stalled thinking.

Progress becomes easier when we use the right kind of explanation to match the problem we’re facing.

A good reminder when working on strategy.

Why do motivated teams struggle to make progress?

I’ve been reflecting on how we design processes that work.

It’s easy when you’re building a tool that you’re going to use, working directly with a client, or designing with a small, tightly-knit team.

It gets harder as groups get bigger.

Imagine crawling through a pipe.

It’s easy if the pipe is large enough.

But what if the pipe is too small, or filled with obstacles?

Suddenly every small movement becomes hard work.

It’s not what’s outside the pipe that slows us down – the weather, people shouting.

It’s the constraints that matter.

We often think that managing progress means working harder, putting more controls in place, or creating incentives.

But maybe the real task of management is simpler: removing obstacles that stop teams from getting work done.

We talk our way into our beliefs – and this has more impact on strategy than we realize.

I’m reading “Organizational Change” by Laurie K. Lewis (2011), which argues that there are weaknesses in how we currently approach change.

• We focus on the implementers – seeing others as passive onlookers
• We assume that the reactions people have to change are emotional or misguided
• We miss how stakeholders really influence each other
• We rarely ask if the proposed change is a good one

What should we do differently?

We need to see that stakeholders actively influence each other.

In this view, leadership is actually a function of attention.

Leaders gain influence because we pay them attention.

We listen to some people more than others – and their opinions and requests become policies and mandates.

And that doesn’t always turn out well.

Instead, we need communicative processes that engage stakeholders and allow shared understanding to emerge.

If we want change to succeed, we have to take the time to listen to each other.

I come across two kinds of leaders tasked with driving change.

The first sees change as imposed. It’s a top-down, command-and-control process.

People are audiences – they are given a message and expected to act.

The second sees change as participatory and negotiated.

Stakeholders are actors with purpose and agency, and the ability to cooperate or resist change.

What matters here is building consensus and dealing with the messy reality everyone faces.

Which approach is better?

There is no right answer – it depends on the situation.

I love this quote by Poul Anderson – “I have yet to see any problem, however complicated, which, when you looked at it in the right way, did not become still more complicated”.

Change is not easy.

But it is a process.

Success or failure depends on how you design and run that process.

There are four topical challenges managers are addressing right now.

1. Digital effectiveness

Which integrated systems and processes do we need to manage information?

1. Operational effectiveness

How should we configure our internal and external value chains and build resilience?

1. Governance

What roles do leaders play and how do they make good decisions?

1. Business model development

What is the business and financial case for taking action?

Each department has variations on these themes to deal with – take sustainability as an example.

• Digital: AI is pitched as a solution for dealing with overwhelming data confusion
• Operations: New technologies have to be evaluated and assessed against like-for-like replacements
• Governance: Leadership in sustainability requires regulatory fluency and agile political footwork.
• Business Model: Financial models often don’t recognize the long term benefits of sustainability.

These are complex things to manage. It’s not surprising that some teams are feeling stuck – wanting to make progress but lacking budgets or resources.

A possible remedy? Nemawashi.

Nemawashi is a Japanese term for “digging around the roots” – and tells us to have informal discussions with stakeholders to prepare the foundations for change.

It’s a core element of the Toyota Production Process (TPS), and Japanese corporate culture.

Progress in organisations doesn’t happen quickly.

It happens when managers and teams take the time to engage widely, construct narratives, and build enough consensus to take action.

Imagine a technology-enabled enterprise. What comes to mind is often different from day-to-day reality.

Is there a clear technology stack? Data at the bottom? A user interface at the top? Clean efficient connections between data and insight?

Or do we have a mess of systems built and procured over time? People as an intrinsic part of the process. Software tweaked and customised over time to get the job done.

In such situations, change is a challenge.

Go down the RFP route, and you can end up getting a large, bloated system that no one uses.

Pick an innovative new platform – and navigate risks and issues raised by procurement, IT, legal, and finance – delaying the start of projects.

Change imposes costs. Real ones.

Right now, we’re being told that budgets are tight, costs must be controlled, and there’s no money to spare.

Buying a system doesn’t mean the work gets done. Sometimes it creates more work.

The pragmatic solution? Orchestration: start by getting the most out of the systems and capabilities we already have.