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AI has become almost impossible for businesses to ignore. Every week brings another model, another tool, another promise of automation, and another story about a company transforming itself with artificial intelligence. The problem is that all of this activity can make it harder to answer the question that actually matters: What value is AI creating for the business?
We explored that question with Laura Chattington, CEO of Rebalance Network Limited, whose work focuses on helping established entrepreneurs and organizations move beyond AI as a simple productivity tool. Laura started her career as a software engineer and now works at the intersection of technology, leadership, and business strategy. That combination gives her an interesting perspective on the current AI transition because she understands both what the technology can do and why businesses often struggle to turn those capabilities into measurable results.
About Laura ChattingtonLaura Chattington is the CEO of Rebalance Network Limited. She works with established entrepreneurs and corporate leadership teams to move beyond AI as a productivity tool and use it to create measurable business value, commercial returns, capacity, and long-term business assets.
Laura began her career as a software engineer and has spent more than 20 years working across technology, leadership, communications, and business strategy. Her work includes AI strategy and transformation programs, executive AI workshops, AI CEO programs, the AI Evolution Multiplier, and the Four Levels of AI Value framework.
Learn more about Laura:
Rebalance Network: www.RebalanceNetwork.com LinkedIn: linkedin.com/in/laura-elizabeth-c YouTube: https://youtube.com/@laura.chattington
Businesses Are Tired of Hearing About AIOne of Laura's observations was that the conversation around AI has changed dramatically over the last 12 to 18 months. Not long ago, business leaders were eager to hear from anyone who could help them understand AI. Today, many of those same leaders are overwhelmed by the constant stream of tools, platforms, consultants, and promises competing for their attention.
The issue isn't necessarily that businesses don't believe AI has value. Many have already experienced it. A proposal that once took two days might now take an hour, research can happen much faster, and repetitive work can increasingly be automated. Those improvements are real, but saving time is only the beginning.
If AI gives someone two days back, what happens to those two days?
That is where the business conversation needs to begin. If the recovered time simply gets filled with more meetings, emails, or low-value work, the organization may have improved productivity without actually improving the business. The real opportunity comes when that new capacity gets intentionally redirected toward better customer experiences, new services, additional revenue, or other measurable outcomes.
Stop Chasing the AI BallLaura used a great analogy during our conversation: businesses are chasing the AI ball.
Think about young kids playing soccer. The ball moves across the field, and almost everyone runs after it at once. There isn't much positioning or strategy because everyone is focused on wherever the ball happens to be right now.
That describes a lot of AI adoption today. A new AI tool appears, so everyone experiments with it. Then agents become the big topic, so everyone starts building agents. A new model launches the next week, and attention immediately shifts again.
The result is a lot of movement without necessarily making much progress.
For business leaders, the better approach is to stop asking, "What AI tool should we be using?" and start asking where the organization is losing time, money, or opportunity. Once the problem is understood, AI becomes one possible part of the solution rather than the strategy itself.
That distinction is important because technology doesn't fix a poorly understood business problem. In many cases, it simply allows the organization to make the same mistakes faster.
Where Is the Return?This problem becomes even more important for organizations that have already invested heavily in AI. Companies have purchased licenses, launched projects, created internal initiatives, and encouraged teams to experiment. Eventually, leadership asks the question every technology investment has to answer: Where is the return?
That shouldn't be viewed as resistance to AI. It is basic business discipline.
If an organization invests thousands or millions of dollars into AI, someone should be able to explain what improved.
- Did revenue increase?
- Did customer response time decrease?
- Did the organization gain capacity?
- Did a process that previously required five days become a five-hour process?
- More importantly, what did the company do with the capacity it created?
Laura's work addresses this through what she calls the Four Levels of AI Value. She argues that many organizations get stuck at the earliest stages, where AI primarily saves time or amplifies existing work. She describes this as "Pilot Purgatory": companies are using AI and running experiments, but they aren't necessarily converting those experiments into meaningful commercial value.
The goal is to move beyond simply doing the same work faster. Businesses should be looking for ways AI can create capacity, generate commercial returns, improve customer outcomes, and eventually create business assets or advantages that compound over time.
AI Is Creating a Competitive DivideLaura illustrated the competitive pressure with an example of two service businesses. Imagine that both companies provide comparable quality. One refuses to integrate AI and needs five days to complete a project, while the other uses AI to complete the same core work in half a day.
The second company now has four and a half additional days to create value for the customer.
It could lower the cost, deliver faster, provide additional analysis, improve the customer experience, or use that capacity to develop new services. The important point isn't that AI replaced the people doing the work. It changed what those people could accomplish with the same amount of time.
That creates a difficult competitive environment for organizations that decide to ignore AI completely. A company may have excellent people and provide excellent service, but competitors using AI effectively can increasingly combine quality with speed and additional value.
The keyword there is effectively.
Simply forcing employees to use AI isn't a strategy either. Businesses still need people who understand the work, know where risks exist, and can recognize when an AI-generated answer is wrong. The advantage comes from combining human knowledge with AI capabilities, not blindly replacing one with the other.
AI Can Accelerate Bad Decisions TooThis is especially important in software development. AI has dramatically lowered the barrier to building software, allowing people without traditional development backgrounds to create applications and automate workflows that previously required a technical team.
That is incredibly powerful, but it also introduces risk.
AI is very good at confidently moving forward. It can generate code, suggest architecture, create integrations, and tell someone that everything looks fine. A person without a technical background may not recognize the assumptions, security issues, scalability problems, or quality concerns hidden underneath that apparently working solution.
Laura described the danger as essentially bad scope on rocket fuel.
If the original problem wasn't properly defined, AI can help build the wrong solution faster than ever. Instead of discovering the mistake after months of traditional development, organizations may reach that point much sooner. Faster development doesn't eliminate the need for requirements, architecture, testing, quality gates, and business understanding.
In many ways, it makes those disciplines even more important.
The Opportunity for Developers Is Bigger Than CodingThis shift also creates an important opportunity for developers and other technical professionals. As AI makes implementation faster, the value of understanding the business problem increases.
Businesses don't necessarily need another person telling them which AI tool launched this week. They need people who can walk into an organization, understand where the pain exists, determine what is worth solving, identify the risks, and connect technology investments to measurable outcomes.
That requires developers to think beyond code.
Technical knowledge remains important because someone needs to understand what is happening beneath the surface. However, the developer who can combine that knowledge with communication, business analysis, quality, and strategic thinking becomes increasingly valuable as AI handles more of the implementation work.
For developers building consulting businesses or side projects, that may be one of the biggest opportunities created by AI. The question clients increasingly need answered isn't simply, "Can you build this?"
It is, "Should we build this, and what value will it create?"
Start With the Business, Not the AIThe biggest takeaway from our conversation with Laura is surprisingly simple: stop starting with AI.
Start with the business.
Look at where people are spending their time. Find the bottlenecks. Identify the customer problems. Understand where money is being lost or where opportunities are being missed. Then determine whether AI can remove that friction or create something that wasn't practical before.
AI is moving too quickly for businesses to chase every new development. Trying to keep up with every model and tool will exhaust teams without necessarily creating an advantage. The companies that succeed won't be the ones that experiment with the most AI. They will be the ones that learn how to turn AI into measurable business value.
And for developers, consultants, and technical leaders, helping organizations make that transition may become far more valuable than simply knowing how to write the code.
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