Category: LinkedIn

“I have yet to see any problem, however complicated, which, when … looked … the right way, did not become still more complicated.” – Poul Anderson.

If you’re a manager trying to make decisions, the methods of half a century ago struggle to help with the complex world of today.

Set targets, objectives and goals. Make plans. Allocate resources. Make it happen.

That assumes a linear, predictable environment – the high ground – rather than the wicked, swampy lowlands that we deal with daily.

Complex problems are not solved by planning and control.

They need iterative sense-making and incremental action.

Take decarbonisation transition planning, for example – something many of our clients have to do.

There are many stakeholders involved, each with different perspectives, needs and reasoning.

They need methods designed for structured sense-making that supports negotiation, debate and trade-offs.

You don’t manage such complexity by trying to simplify it.

Instead, you identify the next action that stakeholders and leaders can support.

There are only two times when you should start work.

One – when you have customer demand. Two – when you’re curious.

Modern manufacturers start building a car only once a customer places an order.

We need the same discipline in consultancy.

It’s very easy to burn time working for free doing studies or pilots.

But we shouldn’t confuse delivery work with exploration.

I also spend a lot of time with customers and prospects – talking through their situations to understand what needs doing.

It looks like unpaid work. It isn’t. It’s an investment.

Time spent exploring a situation with a client shows us what needs to be done.

And that sometimes leads to paid delivery work.

Work should be pulled by demand, or by curiosity. Nothing else.

I triage information into three categories.

First, what do I need to know to get the job done?

That means understanding a situation in detail – how did we get here, and what needs to change?

One level up is management – talking about resources, timelines and monitoring.

At the edge is pure theatre.

It’s the post with a case study that falls apart when interrogated. A pitch that papers over the cracks. Cleverness that substitutes for clarity.

What I’m searching for is the equivalent of going to Gemba – to the place where work is done.

In consulting, Gemba isn’t a place. It’s a point of view.

It’s the way stakeholders perceive the situation, and the problems they believe need to be addressed.

As management consultants, we make a difference by cutting through to the core.

The value isn’t in talking around the work.

It’s in understanding it.

Did you know that managing costs can lead to higher costs?

Toyota’s Ohno was one of the few to realise that the way to cut costs was to focus on flow instead.

There are four flows that matter, and I came up with the mnemonic VIEW to remember them.

• V: is for value
• I: is for information
• E: is for energy
• W: is for materials

Value is about revenue – delivering what the customer actually wants.

The other three are about costs – the inputs into a business.

You reduce costs by improving how they flow through your business.

For example, we used to calculate a client’s carbon footprint once a year – at reporting time.

We redesigned our system to reduce manual work by 90% – reducing friction, delays and errors.

Now we can see our position daily, making reporting significantly easier.

This is improving information flow in practice – and we can drive down a client’s costs as a result.

Energy and materials can be improved in the same way.

Stop managing costs. Design better flows instead.

I don’t think AI is going to save you time.

But I believe it can help you produce better work.

Like many people, I use AI every day.

Recently, I’ve been experimenting with using AI as a feedback coach.

Give it a post, a paper, a presentation – and ask it to suggest improvements.

It doesn’t just catch typos.

It shows the holes in your logic, points out when the narrative arc breaks down, and suggests where to trim the fat.

I tell it to review my work. Not rewrite. That’s my job.

But it often takes longer to finish. A piece might take seven revisions, rather than three.

There’s a saying – “Art is never finished, only abandoned.”

AI makes you spend more time on the task before you abandon it, because it spots the problems you need to fix.

Using AI can make you better at what you do.

But only if you use it as a coach, rather than a substitute.

Are you using AI to do the work, or show you where it isn’t good enough yet?

I’ve been talking to managers recently about what really frustrates them.

It’s how much of their time is drained managing performance.

But the problem often isn’t the people. It’s the system.

For example, setting targets often results in gaming behaviour. The objective becomes hitting the target, rather than doing the work in a way that’s best for the customer.

We see this play out often:

• the surgeon that avoids cases that hurt their stats
• the salesperson that offers a ridiculous discount to get their bonus
• the CEO that uses layoffs to maintain quarterly EBITDA

People will behave in ways that are rational for the system that employs them.

If you want them to act differently, you have to change the way the system works.

That’s the insight Deming had.

“A bad system will beat a good person every time.”

Next time, try fixing the system before fixing the people.

Why is it so hard to find employees and clients when there is infinite visibility on LinkedIn?

Here’s the problem.

Visible markets are crowded. Opportunities sit in invisible ones.

LinkedIn makes it easy to apply for jobs, so thousands respond to an advert.

Thinking of hiring a consultant? A post will attract hundreds of suggestions.

The problem is baked into the design of the system, creating a mismatch between demand and supply.

Only a small fraction of total demand is visible.

But the supply side floods in, because people search where it’s easy to look.

So the visible market becomes the most competitive one.

Here’s the strategic takeaway.

If it’s easy to reach, it’s already saturated.

If you want an edge, you have to go where others aren’t looking.

You know what they say about luck, right?

Hint: It’s about making a decision at the right time.

In sustainability work, we often assume we can reduce emissions in a straight line.

But change happens in steps, not lines.

Our emissions are shaped by the current system – our assets, processes and ways of working.

If we want to reduce them permanently, we need to redesign the system.

Take a simple example.

If your employees travel a lot for work your emissions will stay in a range as long as you keep operating the same way.

Significant reductions require a step change.

Can we do some work remotely? Is low-carbon transport an option?

But those kinds of shifts aren’t always possible.

They tend to open up in specific windows – when assets reach end of life or when policy and economics make new options viable.

You don’t control when those windows appear, but you can be ready for them.

And that’s the thing about luck.

It’s when preparation meets opportunity.

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