The New Economics of Value

What will your customers pay for tomorrow?

What part of your business creates value today that customers may no longer be willing to pay the same amount for tomorrow?

It's a question I believe every established business should be asking.

AI is making work that once required considerable expertise, time and resources faster and more accessible. Reports, analysis, creative production, research and countless other activities are changing.

Much of the conversation is understandably about productivity. How many hours can we save? What can we automate? How many people will we need?

Those are legitimate commercial questions.

But I wonder whether we're starting in the wrong place.

Before asking what AI can save us, shouldn't we be asking what our customers will value next?

Because making the same work cheaper doesn't necessarily make a business more valuable.

And I suspect the greatest opportunities may not be in the work AI can take away, but in the value it can help us create that we previously couldn't see.

I've seen the economics change before

For more than twenty years, I built and led Icon Visual Marketing, a strategy-led business that helped other businesses develop markets, build customer relationships and pursue commercial growth.

During that time, we navigated several major transitions: the introduction of digital marketing, the Global Financial Crisis and COVID-19.

Each was different. Each challenged assumptions about how businesses competed, operated and created value.

Digital marketing was particularly revealing.

For decades, advertising had largely been a one-way message. Businesses developed campaigns, bought media and spoke to their audiences.

Digital changed that relationship.

Customers could respond, question, compare, comment and engage directly with businesses. What had largely been a one-way message became a two-way conversation.

And with that came extraordinary amounts of data.

We could measure impressions, clicks, engagement, conversions and customer behaviour. Reports became more detailed. Dashboards became more sophisticated.

Yet something always troubled me.

We had more data than ever, but meaningful insight was still surprisingly scarce.

We knew more about what customers were doing. But did we really understand what they needed? And when we did learn something important, was that understanding changing decisions across the business?

Too often, information remained within the function that collected it.

Marketing understood the campaign. Sales understood the conversation. Operations understood the experience of delivering the work.

But the business didn't always connect those perspectives.

Think about your own business. How much information do you collect about customers that never changes a decision?

And if you brought your marketing, sales and operational leaders together, would they describe the same customer in the same way?

The GFC brought a different challenge. Customers became more cautious, spending decisions were scrutinised and businesses had to reconsider what people considered essential and worth paying for.

COVID challenged another set of assumptions. Where people worked, how services were delivered, how relationships were maintained and what customers expected could change almost overnight.

Through each transition, I learned that responding to change wasn't simply about adopting new tools or reducing costs.

It was about understanding how customers and markets were changing, and reconsidering how the business could remain valuable.

AI brings those questions together, potentially on a much larger scale.

AI is changing more than the cost of production

Consider a creative agency.

Work that once required a team of specialists can increasingly be supported or produced through AI. Research, concepts, presentations, illustrations, animations and analysis are becoming more accessible.

That's an enormous opportunity for productivity.

But what happens when the client can access similar production capabilities?

What happens when the work itself is no longer as scarce as it once was?

The agency might become significantly more efficient while discovering that clients are less willing to pay the same fees for traditional deliverables.

The question then becomes: what is the client really paying for?

The creative asset? The thinking behind it? The understanding of the customer's business? The confidence that the work will achieve something worthwhile?

Or the ability to connect creativity with commercial outcomes?

These distinctions matter because the cost of producing something and the value a customer places on it are not the same.

And the implications extend far beyond marketing agencies.

Professional services firms, architects, accountants, consultants, designers and other established businesses are facing versions of the same question.

AI is changing what can be produced, who can produce it and what customers believe they should pay for it.

Consider your own business.

If your customers could access much of what you currently produce faster and at a fraction of the cost, what would they still come to you for?

And are you developing that part of your business, or continuing to invest primarily in the work that's becoming easier to produce?

I don't believe the answer is simply to defend traditional expertise or compete on price.

The opportunity is to reconsider what that expertise makes possible.

When producing an answer becomes cheaper, what happens to trust?

A recent conversation with someone close to me, who works in a large architectural practice in Greece, brought this into focus.

The practice produces professional reports based on specialist knowledge and judgement.

Increasingly, clients are taking those reports, putting them through AI and asking the technology to identify problems.

They then return with AI-generated critiques, requesting explanations and corrections.

Some of those questions may be entirely legitimate.

Others can be based on incomplete information, misunderstood technical requirements or conclusions that don't hold up when examined by someone with the relevant expertise.

But the architects still have to investigate them.

They must review the concerns, establish which are valid and spend time explaining the reasoning to clients who may not have the technical background to evaluate the answers themselves.

The practice is now responding commercially to that additional review and explanation work.

Think about what has happened.

AI has made it easier and cheaper for a customer to generate a critique.

But it hasn't necessarily made it easier to determine whether the critique is correct.

In some cases, it has created additional work.

When producing an answer becomes cheaper, establishing whether that answer deserves to be trusted may become more valuable.

That is a different economic equation.

It suggests opportunities around verification, interpretation, professional accountability and helping customers make informed decisions.

But it also presents a warning.

If AI allows us to produce more information without improving understanding, we may simply create more confusion, more rework and more pressure on the people responsible for delivering the outcome.

The value we were losing between people

Looking back at my own business experience, I wonder how many opportunities we missed because important information didn't move properly between people.

Over the years, I saw capable teams deliver technically good work and still disappoint clients.

Not necessarily because the work was poor.

Sometimes the original conversation hadn't been fully understood. A brief missed something important. An expectation was never clarified. A promise was interpreted differently by the client and the team.

By the time the misunderstanding became visible, the work had already been done.

There was rework, frustration and pressure on the relationship.

And sometimes the cost was greater than the work that needed correcting.

A commercial opportunity had been missed.

The client had expressed a concern or ambition that could have led to a better solution, but it hadn't travelled beyond the original conversation.

The strategist didn't hear it. The creative team didn't understand it. The people delivering the work didn't know it mattered.

The business had capable people, but the intelligence wasn't connecting.

If I were building Icon Visual again today, this is where I would begin thinking about AI.

Not how many designers, account managers or strategists I could replace.

I'd ask how much more valuable those people could become if they shared a deeper understanding of the customer and the work.

I'd want AI supporting the entire relationship, from conversations and analysis to briefing, delivery, testing and the next conversation.

Helping preserve context. Highlighting unanswered questions. Identifying possible inconsistencies. Connecting information that might otherwise remain isolated.

Not pretending to know what a client secretly thinks, but helping us recognise where we need to listen more carefully or ask another question.

And I would place even greater emphasis on people who could build relationships, exercise judgement, understand customers and create value.

Because the tools will continue to change.

The ability to understand people and take responsibility for what we promise remains fundamental to the relationship.

When the meetings changed

I saw the importance of shared understanding while working with an established business whose founder was increasingly carrying too much.

She held much of the knowledge behind the client relationships, the history, the expectations and the decisions.

Her team was capable, but when people didn't have the full picture, they naturally returned to her.

We began introducing systems to capture important client intelligence earlier and make relevant context more available across the business.

Initially, the opportunities appeared practical: better briefs, fewer misunderstandings and more consistent information.

But something more significant began to happen.

The meetings changed.

People became more engaged. They asked questions. They challenged assumptions. They could contribute with greater confidence because they understood more of the context behind the work.

They weren't simply receiving instructions. They were participating in the thinking.

The founder gained headspace. The team became more empowered. The culture began to shift.

What struck me was that the capability had been there all along.

What changed was people's access to the understanding they needed to use it.

This wasn't simply a technology change. It also required changes in communication, responsibility and leadership.

But it demonstrated something I believe is central to the opportunity AI now presents.

We often measure technology by the work it removes.

What if we also measured it by the capability it helps people develop?

Think about the people in your next leadership meeting.

Are they contributing their judgement because they understand the business and its customers?

Or are they waiting for the person who holds the missing context to explain what everyone needs to know?

The value isn't simply a faster brief or fewer hours spent correcting work.

It's a business capable of distributing understanding and responsibility more effectively.

A founder who doesn't need to carry every detail.

A team more capable of recognising opportunities and making informed contributions.

And a different foundation for growth.

The opportunity is bigger than efficiency

I'm enthusiastic about AI.

I believe businesses should be exploring it seriously, experimenting with it and learning how to use it.

But I don't believe the first question should be which tools to buy or how many tasks to automate.

The first question should be what is changing in the economics of the business.

What do customers value today? Which parts of that value are becoming easier or cheaper to obtain? What new expectations are emerging?

Where is valuable knowledge being lost between people and functions?

What could the business offer if its relationships, intelligence and capabilities worked together differently?

And where could AI help make those possibilities real?

AI can help us analyse, produce, communicate and connect information. It can support people in identifying patterns and considering alternatives.

But its outputs still need context, verification and judgement.

The goal isn't to automate the existing organisation without questioning how it works.

It's to understand how the organisation might create greater value.

Sometimes that will mean reducing costs.

Sometimes it will mean developing a new service or changing how expertise is delivered.

Sometimes it will mean strengthening customer relationships.

And sometimes the greatest opportunity will be giving capable people the information, understanding and authority they need to contribute more fully.

Three questions I'd ask before investing in AI

If I were sitting with the CEO of an established business considering its AI strategy, I wouldn't begin with a list of tools.

I'd encourage the leadership team to explore three questions.

1. What are our customers paying us for today that may become less valuable tomorrow?

Not simply what we produce, but why customers choose us, what they trust and what outcomes matter to them.

2. What do we already know about our customers that isn't reaching the people who could act on it?

Where is information lost between marketing, sales, operations and leadership? Which decisions depend on one person? What opportunities are we missing because the understanding isn't shared?

3. If we could connect that knowledge and capability differently, what new value could we create?

What could our people achieve with a fuller understanding of the customer? What could we offer that we don't offer today? And how might AI help us make that possible?

I suspect the answers would tell a leadership team more about where to invest in AI than a list of the latest tools.

Not because efficiency doesn't matter.

But because efficiency is only one part of value.

A business can become dramatically more efficient at delivering something customers no longer value.

It can also discover opportunities it never recognised because the knowledge required to see them was scattered across the organisation.

That is the distinction I believe leaders need to understand.

The question isn't simply what AI can do

We've lived through significant changes before.

Digital marketing changed how businesses communicated with customers. The GFC challenged what customers considered worth paying for. COVID changed how businesses operated and delivered value.

AI may change all of those things, and the economics of producing the work itself.

But I believe the greatest opportunity isn't simply to do more with less.

It's to rethink what value means, how it's created and how the people within a business can contribute to it.

The businesses that grow better won't necessarily be those that adopt the most AI. They'll be those that understand how value is changing and use AI, human judgement and organisational capability to respond.

So I would leave every business leader with two questions.

What part of your business creates value today that customers may no longer pay the same for tomorrow?

And perhaps more importantly:

What value could your business create tomorrow that it hasn't yet learned how to see?

These aren't questions with universal answers. Every business has its own customers, relationships, capabilities and commercial realities.

But I believe they're conversations worth having now.

If you're leading an established business and wondering what AI and changing markets mean for the value you create, I'd welcome the opportunity to compare perspectives.

Because the first step isn't choosing an AI tool. It's understanding what your business could become.

Joe Papadatos is the founder of Two Icons, a strategic growth advisory business helping established businesses discover commercial opportunities and develop the capability to realise them.

Grow Better.