Introduction

Most conversations about AI focus on productivity.

We hear about automating tasks, reducing costs, increasing efficiency, and helping people do more work in less time. For many businesses, those benefits are real and worth pursuing.

But the more time I spend working with AI, the more I find myself wondering if we're focusing on the wrong question.

What if AI's biggest impact isn't helping organizations do the same work faster?

What if its bigger impact is expanding what organizations are capable of doing in the first place?

At first glance, that distinction may sound subtle. But the more I think about it, the more important it seems.

Efficiency is about doing the same work with fewer resources. Capability is about creating options that didn't previously exist.

And if that's true, AI may end up changing far more than productivity.

The Traditional View of AI

When most businesses evaluate AI, they tend to focus on a familiar set of questions:

Can it help us work faster?
Can it reduce costs?
Can it automate repetitive tasks?
Can it help employees become more productive?

These are reasonable questions. In many cases, they're exactly the right questions to ask.

For a small business operating on tight margins, saving a few hours each week can make a meaningful difference. Automating administrative work, accelerating research, improving customer support, or reducing manual data entry can create real value. It's no surprise that productivity has become the dominant lens through which many people view AI.

In some ways, that focus makes sense. Throughout history, new technologies have often been measured by their ability to help us do more with less. Whether it was machinery, software, or automation, the conversation frequently centered on efficiency gains and cost reduction.

AI naturally fits into that narrative.

But as valuable as productivity improvements may be, they are not the only possible outcome. Focusing exclusively on efficiency may cause us to overlook a different question entirely:

What if AI doesn't just help people do their jobs faster, but helps businesses become more adaptable in how work gets done and who is capable of doing it?

That question may ultimately prove more interesting than productivity alone.

When the Traditional Path Disappeared

While building Wired Wits, I reached a point where I needed development capabilities but no longer had the funding to continue outsourcing the work.

As a solo founder, that left me with two choices:

Stop building and put the project on hold.
Find another way forward.

I chose the second option.

What followed wasn't simply a matter of opening an AI chat window and asking it to build software. I had to create structure around the process. Over time, I developed an approach that combined planning, documentation, standards, workflows, and AI assistance into a repeatable system for building and improving the platform.

AI didn't turn me into a software engineer, and I have no plans to become one.

What it did provide was something I often describe as an intellectual exoskeleton. It allowed me to operate beyond my experience and training and continue building when traditional options were no longer available.

That experience changed how I think about AI.

The biggest impact wasn't efficiency.

It was the expansion of what became possible.

Efficiency vs. Capability

At first glance, efficiency and capability can sound like the same thing.

In practice, they're very different.

Efficiency is about doing the same work with fewer resources. If a task normally takes four hours and AI helps you complete it in two, that's an efficiency gain. The work itself hasn't changed. You've simply reduced the time, effort, or cost required to complete it.

Capability is different.

Capability is about being able to do something that previously wasn't possible, practical, or accessible.

Consider two scenarios:

A small business owner uses AI to draft marketing content more quickly. That's an efficiency gain.
That same business owner empowers existing team members with AI, documented processes, and organizational knowledge to launch and manage a marketing program that previously required an agency or additional hires. That's a capability gain.

The distinction may seem subtle, but it has important implications.

Efficiency helps organizations optimize what already exists. Capability expands what becomes possible.

That doesn't mean expertise suddenly becomes unnecessary or that AI turns everyone into an expert. It doesn't.

What it may do is reduce some of the barriers that previously limited who could contribute, what organizations could attempt, and how quickly organizations could respond to new opportunities.

The more I reflected on my own experience, the more I realized that what changed wasn't simply how fast I could work.

What changed was the range of things I could realistically attempt.

That realization led me to a different question:

If AI can expand capability, what might that mean for the way businesses think about capacity, growth, and adaptability?

The Hidden Assumption

The Hidden Assumption

For most businesses, growth has traditionally been tied to headcount.

Need to serve more customers? Hire more people.

Need to launch a new service? Hire more people.

Need additional expertise? Hire more people.

In many cases, that's the right decision. People remain one of the most valuable assets any business can invest in.

But over time, assumptions can become so familiar that we stop questioning them.

Consider a few common questions:

If a business wants to increase capacity by 20%, does it always need 20% more people?
If a business wants to explore a new opportunity, does it always require hiring specialists before taking the first step?
If a business encounters a temporary surge in demand, is adding headcount the only viable response?

Historically, the answer to many of these questions has often been yes.

People carry knowledge, skills, experience, judgment, and organizational context. Expanding capability typically meant expanding the organization itself.

But what happens if AI changes that equation, even slightly?

Not by replacing expertise.

Not by eliminating the need for talented people.

But by helping existing employees access information, follow documented processes, overcome certain knowledge gaps, and contribute in ways that were previously difficult or impractical.

If that becomes possible, even in limited ways, it could create new options for how businesses think about capacity, growth, and resilience.

That doesn't mean the traditional relationship between growth and headcount disappears.

It simply means the relationship may become more flexible than it has been in the past.

A Different Possibility

If AI can expand capability and make organizations more adaptable, what might that look like in practice?

The answer will likely be different for every business. A local contractor faces different challenges than a marketing agency, accounting firm, or retailer. However, the underlying question remains the same:

What new options become available when existing people can contribute in more ways than they could before?

For some businesses, it may mean pursuing opportunities that previously felt out of reach.

Examples might include:

A small business testing a new service offering without immediately hiring a specialist.
A local retailer experimenting with ecommerce, digital marketing, or customer analytics using capabilities that previously required outside support.
A professional services firm expanding the ways it serves clients by combining employee expertise with AI-assisted research, analysis, and content creation.

For other organizations, the benefit may have less to do with growth and more to do with resilience.

Markets change. Customer expectations evolve. New opportunities emerge unexpectedly. Businesses that can adapt without immediately restructuring, hiring, or outsourcing may have advantages that extend well beyond productivity gains.

None of this guarantees better outcomes.

It simply creates more options.

And in business, optionality is often valuable in its own right.

The ability to test an idea, pursue an opportunity, respond to a challenge, or expand capacity without committing to major organizational changes may prove just as important as the efficiency gains that dominate today's AI conversations.

The Importance of Systems

The Importance of Systems

Before we get too carried away, it's important to acknowledge something:

AI alone does not create adaptability.

Giving employees access to AI tools does not automatically make them experts. It does not eliminate the need for experience, judgment, or specialized knowledge. And it certainly does not guarantee better business outcomes.

If anything, my own experience taught me the opposite.

The more I relied on AI, the more important structure became.

Processes mattered.
Documentation mattered.
Standards mattered.
Clear objectives mattered.

The value wasn't simply in having access to AI. The value came from combining AI with systems that guided how work was performed and how knowledge was applied.

The same principle applies to businesses.

Organizations that want to become more adaptable cannot rely on AI alone. They also need ways to:

Capture organizational knowledge
Establish repeatable processes
Define standards and expectations
Provide employees with the context needed to make good decisions

In many ways, AI may increase the importance of these foundations rather than reduce them.

If AI expands what is possible, systems help ensure those new possibilities can be pursued consistently and responsibly.

The businesses that benefit most may not be those with the most advanced AI tools.

They may be the businesses with the clearest systems, the strongest processes, and the best understanding of how work gets done.

Questions Worth Exploring

I don't know exactly how AI will reshape organizations over the next decade.

I suspect nobody does.

What I do know is that many of the assumptions we've relied on for years may be worth revisiting.

If AI expands capability, even modestly, what does that mean for how businesses think about growth?

Some questions I think are worth exploring include:

Could some organizations increase capability and adaptability without immediately increasing headcount?
Could businesses become more adaptable when employees are supported by AI, documented processes, and organizational knowledge?
Could businesses become more adaptable when opportunities emerge or conditions change?
Could leaders focus less on adding resources and more on increasing the effectiveness of the resources they already have?
Could adaptability become as important a competitive advantage as efficiency?

I don't have definitive answers to these questions.

But I think they're worth asking.

Because the future impact of AI may not be determined solely by what the technology can do.

It will also be determined by how organizations choose to use it.

Conclusion

For now, most conversations about AI continue to focus on productivity. That's understandable. Productivity gains are easier to measure, easier to explain, and often easier to justify.

But productivity may only be part of the story.

The question that continues to occupy my mind is whether AI might ultimately have a greater impact on adaptability than efficiency.

Not because it replaces expertise or eliminates the need for talented people. And not because it magically solves business challenges. Rather, because it may create new options for how organizations think about growth, capacity, and change.

Those options might include:

Exploring opportunities that previously felt out of reach.
Expanding what an organization is capable of doing without immediately expanding headcount.
Responding more effectively when circumstances change.
Becoming more adaptable in an increasingly dynamic business environment.

None of this is guaranteed. AI alone won't create these outcomes, and many organizations may never pursue them. The technology is only one part of the equation. People, processes, systems, and leadership decisions will continue to matter.

What I find interesting is not what AI can do by itself, but what organizations might choose to do differently because AI exists.

Whether these possibilities become reality remains to be seen. But if AI truly lowers some of the barriers that have historically limited organizational capability, then adaptability may prove just as important to the conversation as productivity.

And that may be one of the most important AI questions worth exploring.