Building Shared Understanding in a Complex World

This is the fifth in a series of reflections exploring capability, understanding, purpose, context and the human role in an age of AI.

The World Does Not Arrive in Neat Categories

In previous articles, I explored three questions.

The Wizard of Oz asked:

What lies behind the curtain?

King Lear asked:

Do we understand what matters?

Gulliver’s Travels asked:

Why are we doing this?

Together they led me towards a different question:

How do people develop a shared understanding of the world they are acting upon?

The more I think about it, the more I believe this is one of the defining challenges of our time.

Because the world refuses to fit neatly into the boxes we create for it.

We Like Simplicity

Organisations love boundaries.

Departments.

Budgets.

Systems.

Services.

Projects.

Responsibilities.

These structures are necessary.

They help us organise effort and allocate resources.

Yet people’s lives rarely follow the same structure.

A family experiencing poverty may simultaneously interact with:

● schools,

● health services,

● housing services,

● employers,

● community groups,

● transport systems,

● welfare support.

Each organisation sees part of the picture.

Nobody automatically sees the whole picture.

A Reflection

The challenge is rarely a lack of information.

The challenge is that information exists in fragments.

Four Things Keep Appearing

Across different projects, sectors and communities, I keep seeing the same four elements.

People

Individuals.

Families.

Citizens.

Practitioners.

Communities.

Organisations

Councils.

Businesses.

Health services.

Charities.

Government agencies.

Places

Neighbourhoods.

Towns.

Cities.

Rural communities.

Regions.

Context

History.

Relationships.

Needs.

Aspirations.

Services.

Opportunities.

Constraints.

These four elements constantly interact.

None makes sense in isolation.

Place Matters More Than We Often Realise

One lesson that repeatedly emerges from public services is the importance of place.

A place is not simply a location on a map.

It brings context.

Consider two households with similar incomes.

One lives in a city with:

● regular public transport,

● nearby services,

● multiple employment opportunities.

The other lives in a rural community with:

● infrequent transport,

● limited childcare,

● longer travel times,

● fewer local opportunities.

The data may appear similar.

The context is entirely different.

Without place, understanding becomes incomplete.

Organisations See Different Realities

Another challenge emerges when organisations attempt to work together.

Each organisation develops its own language.

Its own priorities.

Its own measures.

Its own systems.

Its own view of reality.

This is not a problem.

It is inevitable.

The difficulty comes when collective action is required.

Because coordinated action depends upon coordinated understanding.

A Question Worth Holding

How often are organisations trying to solve the same problem while describing it in completely different ways?

Shared Understanding Is Not Agreement

An important distinction.

Shared understanding does not require everyone to agree.

It does not require organisations to have identical goals.

It does not require people to hold the same opinions.

It requires something else.

It requires people to understand one another sufficiently well that they can act together.

This is true for:

● communities,

● organisations,

● partnerships,

● governments,

● and perhaps increasingly, intelligent systems.

The goal is not uniformity.

The goal is coherence.

Health, Communities and Complexity

Throughout my work I have encountered similar challenges in very different domains.

Health data.

Community planning.

Poverty reduction.

Digital transformation.

Education.

Sports performance.

At first glance, these appear to be separate worlds.

Yet beneath the surface they often share a common challenge.

Different actors hold different pieces of the same puzzle.

The difficulty is not merely collecting information.

The difficulty is connecting information in ways that create understanding.

The Missing Layer

For many years we focused heavily on data.

Data quality.

Standards.

Interoperability.

Integration.

These remain important.

Today we are increasingly focused on AI and automation.

Capability.

Agents.

Decision support.

Action.

But there is something sitting between data and action.

Something that often receives far less attention.

Understanding.

Not information.

Not technology.

Understanding.

The ability to make sense of reality together.

A Reflection

Perhaps the most important question is not:

What information do we have?

But:

What understanding are we creating?

A Different Way of Thinking

Perhaps understanding begins with a simple question:

What relationships matter?

People are connected to organisations.

Organisations operate in places.

Places shape experiences.

Experiences create context.

Context influences decisions.

Decisions create outcomes.

The challenge is not simply collecting facts.

It is understanding relationships.

The more complex the world becomes, the more important those relationships become.

Building Shared Understanding

Shared understanding is rarely delivered.

It is built.

Through:

● conversation,

● participation,

● evidence,

● experience,

● stories,

● data,

● reflection.

Each contributes something different.

Data helps us see patterns.

Stories help us understand experiences.

Context helps us interpret meaning.

Together they allow us to develop richer pictures of reality.

Looking Ahead

If people, organisations, places and context help create understanding, then another question naturally follows.

How do we maintain that understanding?

How do we share it?

How do we build it over time?

And perhaps most importantly:

What happens when some of the participants are no longer human?

That question takes us to the final article in this series.

One about shared understanding itself.

And why it may become the scarcest resource in an age of increasingly abundant intelligence.

Final Reflection

We often talk about AI transforming how we work.

Perhaps the deeper transformation will be in how we understand.

Because before people, organisations and intelligent systems can act together, they must first learn how to see the world together.

Next in the series:

Shared Understanding in an Age of AI

Why Understanding May Become More Valuable Than Intelligence

About the author

David Eccles is a co-founder of Wallscope, based in Edinburgh, Scotland. He writes about AI, organisational knowledge, interoperability, shared context and public-sector transformation, exploring how organisations can turn fragmented information into actionable knowledge.

🌐 https://wallscope.co.uk
🔗 https://www.linkedin.com/in/davidecclesedinburgh/

People, Organisations, Places and Context was originally published in Wallscope on Medium, where people are continuing the conversation by highlighting and responding to this story.

Human Purpose in an Age of Increasingly Capable AI

The Question I Keep Returning To

After all the discussions about AI, agents, automation and innovation, I find myself returning to a much simpler question.

Not:

What will AI become?

But:

What will we become?

Every week brings new examples of systems performing tasks that previously required significant human effort.

Writing.

Research.

Analysis.

Programming.

Planning.

Decision support.

Creative work.

The list continues to grow.

The question is no longer whether AI will become increasingly capable.

Increasingly, it already is.

The question is what role remains for us.

The Wrong Question

Much of the public conversation seems to focus on replacement.

Will AI replace jobs?

Will AI replace professions?

Will AI replace expertise?

These are understandable concerns.

But perhaps they are the wrong questions.

Technology has always changed the nature of work.

The more interesting question is often:

What becomes more valuable as technology evolves?

Not what disappears.

What emerges.

Humans Have Always Adapted

When calculators appeared, mathematics did not disappear.

When spreadsheets arrived, finance did not disappear.

When search engines emerged, knowledge did not disappear.

The nature of expertise changed.

Each time, humans moved further from routine processing and closer towards interpretation, judgement and application.

Perhaps AI represents a similar shift.

Not the end of human contribution.

But a change in where that contribution creates value.

A Question Worth Holding

If machines increasingly generate answers, what should humans become better at?

Asking Better Questions

Rudyard Kipling wrote:

I keep six honest serving-men
(They taught me all I knew);
Their names are What and Why and When
And How and Where and Who.

Increasingly I wonder whether our most valuable capability may lie in asking better questions.

Questions about:

• purpose,
• outcomes,
• assumptions,
• consequences,
• trade-offs,
• values.

Questions that no model can answer independently because they depend upon human priorities.

The quality of our future systems may depend less on the sophistication of our answers and more on the quality of our questions.

We Have Been Here Before

Looking back across my own experience, I am struck by how often the same challenge has appeared in different forms.

Publishing.

Communications.

Brand development.

Marketing.

Podcasts.

Health data.

Public services.

Digital transformation.

Information management.

At first glance these seem like different disciplines.

Yet each has involved the same underlying task:

Helping people make sense of complexity.

Sometimes through stories.

Sometimes through conversations.

Sometimes through data.

Sometimes through systems.

The tools differ.

The challenge remains remarkably similar.

Stories Matter

One lesson from communications and storytelling is that people rarely make sense of the world through information alone.

Facts matter.

Evidence matters.

Data matters.

But understanding often emerges through narrative.

Stories provide context.

Stories connect events.

Stories help us recognise relationships.

Stories help us understand consequences.

This may be one reason why literature continues to offer useful insights in an age of AI.

The Wizard of Oz.

King Lear.

Gulliver’s Travels.

They are not data sets.

They are ways of thinking.

Ways of understanding.

Ways of asking questions.

Human in the Loop

We often talk about keeping humans “in the loop”.

I sometimes wonder if that phrase understates the issue.

Humans do not simply supervise the system.

Humans provide many of the things that give the system meaning.

Purpose.

Context.

Judgement.

Values.

Accountability.

Responsibility.

The challenge is not merely ensuring that a human approves a decision.

The challenge is ensuring that human understanding remains connected to action.

Context as a Human Strength

One of the most important lessons I have learned through work involving communities, health, public services and organisations is that context changes everything.

A statistic can be accurate and still be misleading.

A recommendation can be logical and still be inappropriate.

A decision can be efficient and still be wrong.

Context explains why.

Humans remain remarkably good at:

• recognising nuance,
• understanding relationships,
• navigating ambiguity,
• appreciating history,
• interpreting circumstances.

These capabilities become more important, not less, as systems become more capable.

Meaning Matters

Artificial Intelligence can identify patterns.

It can recommend actions.

It can generate convincing outputs.

But it does not determine what matters.

Humans do.

We decide:

• what outcomes are desirable,
• what success looks like,
• what costs are acceptable,
• what risks we are willing to take,
• what responsibilities we hold towards one another.

These are not technical questions.

They are human questions.

A Different Kind of Expertise

Perhaps the future values a different kind of expertise.

Not expertise built around possessing information.

But expertise built around:

• making sense of complexity,
• bringing different perspectives together,
• creating shared understanding,
• connecting knowledge to action,
• helping others ask better questions.

The challenge may not be competing with increasingly capable systems.

The challenge may be becoming better stewards of meaning.

Looking Ahead

The first question in this series came from The Wizard of Oz:

What lies behind the curtain?

The second came from King Lear:

Do we understand what matters?

The third came from Gulliver’s Travels:

Why are we doing this?

And perhaps they all lead here.

Not to a question about AI.

But to a question about ourselves.

Final Reflection

As intelligence and agency become increasingly abundant, I find myself asking:

What should I learn?

What responsibilities should I hold?

What role should I play?

Perhaps the same questions apply to all of us.

Because the future may not belong to those who merely know the answers.

It may belong to those who ask the questions that matter.

Next in the series:

People, Organisations, Places and Context

Building Shared Understanding in a Complex World

About the author

David Eccles is a co-founder of Wallscope, based in Edinburgh, Scotland. He writes about AI, organisational knowledge, interoperability, shared context and public-sector transformation, exploring how organisations can turn fragmented information into actionable knowledge.

🌐 https://wallscope.co.uk
🔗 https://www.linkedin.com/in/davidecclesedinburgh/

What Is My Role? was originally published in Wallscope on Medium, where people are continuing the conversation by highlighting and responding to this story.

Why Data Is Not the Same as Understanding

This is the second in a series of reflections exploring capability, understanding, purpose, context and the human role in an age of AI.

We Have Never Had More Information

We live in an age of abundance.

More data.

More dashboards.

More reports.

More analytics.

More notifications.

More insights.

And increasingly, more AI-generated summaries of all of the above.

Yet despite this explosion of information, many organisations continue to struggle with decision-making.

Communities still face complex challenges.

Public services still wrestle with difficult trade-offs.

Leaders still make mistakes.

It is tempting to assume that the answer is more information.

But what if that isn’t the problem?

What if the real challenge is understanding?

King Lear’s Tragedy

In Shakespeare’s King Lear, the ageing king decides to divide his kingdom between his three daughters.

Before doing so, he asks each to declare how much they love him.

Two daughters provide extravagant declarations.

One daughter, Cordelia, refuses to flatter him.

Lear mistakes performance for truth.

He rewards those who tell him what he wants to hear and rejects the person who is speaking honestly.

The tragedy that follows is not caused by a lack of information.

Lear is surrounded by information.

Advice.

Evidence.

Opinions.

Declarations.

Warnings.

The tragedy emerges because he misunderstands what matters.

A Reflection

Having information and understanding information are not the same thing.

And understanding does not automatically lead to wisdom.

Data Is Rarely the Problem

One lesson I have learned repeatedly through work spanning public services, health data, communities and information management is that the problem is seldom a lack of data.

In many cases:

• the data already exists,
• the reports already exist,
• the systems already exist,
• the evidence already exists.

Yet people still struggle to develop a common understanding of what is actually happening.

Different teams see different parts of the picture.

Different organisations use different language.

Different systems describe the same issue in different ways.

Everyone may be looking at the same reality.

Yet drawing different conclusions.

A Lesson From Public Services

One of the most valuable lessons I have encountered through work involving children, families and communities was surprisingly simple.

The important question was not:

What data do we hold?

It was:

What matters?

That sounds subtle.

But it changes everything.

Instead of beginning with systems and datasets, the conversation begins with people.

Children.

Families.

Experiences.

Relationships.

Outcomes.

Only then does data become useful.

Because data gains meaning through context.

Context Changes Everything

Consider a simple statement:

Household income: £18,000

Useful information.

But incomplete.

Now add context.

A family of four.

A rural location.

Limited public transport.

Long travel distances.

High childcare costs.

Seasonal employment.

Restricted access to services.

The meaning changes entirely.

The number is the same.

The understanding is different.

Data tells us what.

Context helps us understand why.

Understanding helps us decide what to do next.

People, Organisations, Places and Context

Increasingly I find myself thinking about four interconnected elements.

People

Individuals.

Families.

Communities.

Citizens.

Practitioners.

Organisations

Councils.

Health Boards.

Charities.

Businesses.

Government agencies.

Places

Neighbourhoods.

Towns.

Cities.

Rural communities.

Regions.

Context

Relationships.

History.

Needs.

Services.

Constraints.

Opportunities.

Understanding emerges from the interaction between all four.

Remove one and the picture becomes distorted.

Information Without Understanding

King Lear’s mistake feels surprisingly modern.

It wasn’t a lack of information that caused the problem.

It was the inability to distinguish signal from noise.

Truth from performance.

Meaning from appearance.

Today’s organisations face similar risks.

We can become obsessed with:

• metrics,
• dashboards,
• reports,
• indicators,
• AI-generated summaries,

while forgetting to ask a more fundamental question:

Do we truly understand what we are looking at?

A Question Worth Holding

In an age when information is increasingly abundant, does understanding become more valuable?

Understanding Is a Shared Activity

Understanding rarely happens in isolation.

It emerges through conversation.

Through challenge.

Through different perspectives.

Through experience.

This is particularly true when problems cross organisational boundaries.

Health.

Poverty.

Housing.

Education.

Community wellbeing.

No single organisation owns the whole picture.

No single dataset explains it.

No single perspective resolves it.

Understanding becomes something we build together.

Looking Ahead

If The Wizard of Oz warns us not to mistake capability for wisdom…

then King Lear reminds us not to mistake information for understanding.

But understanding alone is not enough.

Another question remains.

Even if we understand a problem perfectly:

Why are we trying to solve it?

That question takes us to another traveller.

Jonathan Swift’s Gulliver’s Travels.

And the strange inventors of the Academy of Lagado.

Final Reflection

Perhaps the challenge of the AI age is not producing more information.

Perhaps it is developing better understanding.

Because without understanding, even abundant information can still lead us astray.

Next in the series:

Gulliver’s Travels and the Purpose of Innovation

Are We Solving Important Problems or Simply Building Clever Things?

About the author

David Eccles is a co-founder of Wallscope, based in Edinburgh, Scotland. He writes about AI, organisational knowledge, interoperability, shared context and public-sector transformation, exploring how organisations can turn fragmented information into actionable knowledge.

🌐 https://wallscope.co.uk
🔗 https://www.linkedin.com/in/davidecclesedinburgh/

King Lear and the Limits of Information was originally published in Wallscope on Medium, where people are continuing the conversation by highlighting and responding to this story.

What The Wizard of Oz Can Teach Us About AI, Capability and the Questions We Should Still Be Asking Ourselves

This is the first in a series of reflections exploring capability, understanding, purpose, context and the human role in an age of AI.

Three Stories Have Been On My Mind Recently

Three stories from very different times.

The Wizard of Oz.

King Lear.

Gulliver’s Travels.

At first glance, they have little in common.

Yet each explores a question that feels increasingly relevant as we enter an age of Artificial Intelligence.

The Wizard of Oz asks:

What lies behind the curtain?

King Lear asks:

Do we truly understand what matters?

Gulliver’s Travels asks:

Why are we doing this in the first place?

This article begins with the first of these questions.

The Wizard of Oz: Looking Behind the Curtain

In The Wizard of Oz, Dorothy and her companions travel in search of wisdom, courage and purpose.

The mysterious Wizard appears all-powerful.

Only later do they discover a figure operating machinery behind a curtain.

Smoke.

Lights.

Effects.

Amplification.

The Wizard is not what he appears to be.

And yet, despite this revelation, the story is not really about deception.

It is about something much more interesting.

The qualities the travellers were searching for did not originate with the Wizard at all.

The Scarecrow sought intelligence.

The Tin Man sought compassion.

The Lion sought courage.

Dorothy sought her way home.

What they eventually discovered was that these qualities were not hidden inside a machine.

They emerged through experience, relationships and understanding.

The Machinery Behind Today’s Curtain

Much of today’s conversation about AI feels strangely familiar.

We are fascinated by:

• larger models,
• increasingly capable agents,
• bigger context windows,
• more powerful computing infrastructure,
• automation at unprecedented scale.

And rightly so.

These are remarkable achievements.

Yet I sometimes wonder whether we risk becoming too fascinated by the machinery behind the curtain.

The question dominating many discussions is:

What can these systems do?

A fair question.

But perhaps not the most important one.

A Question Worth Holding

If increasingly capable systems can generate answers, recommendations and actions:

What questions should humans be asking?

We Were Told Data Was the New Oil

For years we were told:

Data is the new oil.

It’s a useful metaphor.

But perhaps not for the reasons people intended.

Oil has no intrinsic purpose.

Its value comes from what we choose to do with it.

We can use it to power transport.

Manufacture plastics.

Heat buildings.

Every choice produces different outcomes and different consequences.

Some benefits remain visible.

Some costs become externalised and borne elsewhere.

The important questions are therefore not about the oil itself.

They are about purpose.

What are we trying to achieve?

Who benefits?

Who bears the costs?

What consequences are we willing to accept?

The same may increasingly be true of AI.

More capability does not automatically create better outcomes.

It simply increases our capacity to act.

Artificial Intelligence Is a Curious Phrase

The phrase itself has always intrigued me.

Artificial Intelligence suggests that intelligence somehow originates within the machine.

Yet much of what today’s systems know comes from humans.

Human language.

Human experience.

Human institutions.

Human history.

Human knowledge.

Human conversations.

Human values.

The infrastructure is becoming increasingly capable.

The ability to process information is no longer the major constraint it once was.

But greater capability does not automatically create greater understanding.

And it certainly does not create wisdom.

Capability Is Not Wisdom

This, perhaps, is the lesson I take from The Wizard of Oz.

The Wizard represents capability.

Modern AI is becoming increasingly capable.

But capability alone cannot tell us:

• what matters,
• who matters,
• which outcomes should be prioritised,
• what constitutes success,
• what consequences should concern us.

These remain deeply human questions.

The story reminds us that the answers may not be hidden behind the curtain at all.

They may lie in our ability to ask better questions.

Looking Ahead

The Wizard of Oz leaves us with a useful challenge.

As AI systems become increasingly capable, are we becoming more fascinated with what machines can do than with what humans ought to do?

That question leads naturally to another story.

Because if The Wizard of Oz asks:

What lies behind the curtain?

Then Shakespeare’s King Lear asks something even more uncomfortable:

How can we be surrounded by information and still fail to understand what matters?

That is where I will go next.

Final Reflection

Perhaps the greatest challenge of the AI age is not intelligence.

Perhaps it is understanding.

And perhaps the first step is learning to look beyond the machinery behind the curtain.

Next in the series:

King Lear and the Limits of Information

Why Data Is Not the Same as Understanding

About the author

David Eccles is a co-founder of Wallscope, based in Edinburgh, Scotland. He writes about AI, organisational knowledge, interoperability, shared context and public-sector transformation, exploring how organisations can turn fragmented information into actionable knowledge.

🌐 https://wallscope.co.uk
🔗 https://www.linkedin.com/in/davidecclesedinburgh/

The Wizard Behind the Curtain was originally published in Wallscope on Medium, where people are continuing the conversation by highlighting and responding to this story.