Develpreneur: Become a Better Developer and Entrepreneur

Developer work-life balance is often treated as a productivity problem. We look for better schedules, better tools, fewer meetings, or another technique for getting more done. However, our second conversation with Gaylen A. Wilson raises a different question: what happens when the problem is not how much work you have, but your inability to stop working the problem?

Developers spend their careers learning how to stay with difficult problems. We debug issues that make no sense, work through production failures, deal with changing requirements, and keep digging until we find an answer. That persistence can make us successful. It can also make it difficult to recognize when we have crossed from productive problem-solving into stress, frustration, and burnout.

The challenge becomes even greater when we carry that state away from the computer. Work may have ended, but mentally, we are still debugging. Learning to recognize that transition is an important part of developer work-life balance.

About Gaylen A. Wilson

Gaylen A. Wilson is an author, relationship coach, technology veteran, entrepreneur, farmer, former stockbroker, and heart-transplant recipient whose career has required him to reinvent himself repeatedly. Together with his wife, Heather, Gaylen developed the Monument Method, an approach they use to help couples interrupt destructive conflict patterns and strengthen their connection.

Learn more about Gaylen A. Wilson through his website http://www.monumentmethodinstitute.com/.

Developer Work-Life Balance Starts with Recognizing Your State

Michael brought the conversation back to developers by pointing out something familiar to many people in technology. We can become so accustomed to constantly working the problem that we stop recognizing what it is doing to us.

There is always another release, bug, customer issue, deadline, or technology to learn. We keep pushing because that is what has worked throughout our careers. Eventually, though, we can reach a point where we are no longer solving the problem effectively. We are simply reacting to it.

Gaylen connected that experience to what he describes as fight-or-flight thinking. He had experienced an extreme version during his health problems. As a self-described Type A personality, one of the hardest things for him was accepting that he still wanted to keep going while his body was telling him that he could not. He credited his relationship with his wife, Heather, as an important source of support during that period.

You do not need to reach that extreme before recognizing the same pattern in your career. Sometimes the warning sign is much simpler. You close the laptop, but you are still thinking about the defect. You sit down for dinner and mentally replay a conversation from work. You are supposed to be relaxing, but you feel guilty because you are not doing something productive.

The workday ended. Your brain never got the message.

The Car That Would Not Start

Gaylen shared a story that provides a surprisingly good example of what this looks like.

He had spent two months working on one of his cars and finally got it running. After driving it around the block, he and Heather prepared to leave and visit friends. He pushed the button to start the car, and nothing happened.

For someone who likes solving problems, that was frustrating enough. Gaylen also felt that he was disappointing Heather because they were supposed to leave. He grabbed a multimeter, found the battery was low, and started looking for jumper cables.

He could not find them.

They were actually nearby, underneath a blanket, but Gaylen had become so frustrated that he could not slow down enough to move the blanket and see them. By his description, he had entered full fight-or-flight mode. He found another old set of jumper cables, discovered they were badly corroded, and became even more frustrated.

Developers have our own versions of that blanket.

You have probably stared at a defect for an hour only to discover the problem was obvious once you stepped away. Maybe you rewrote code that did not need rewriting. Maybe you blamed a dependency, the network, the database, or someone else's code before discovering a simple configuration problem.

The longer we fight the problem, the narrower our thinking can become. At some point, persistence stops being an advantage.

Developer Work-Life Balance Requires an Interrupt

The most interesting part of Gaylen's story came next. Heather was sitting in the car and did not realize how frustrated he had become. Gaylen finally walked over to her and said, "I need a monument."

That phrase comes from the relationship framework Gaylen and Heather developed, which they call the Monument Method. They use "monument" as an agreed-upon signal to interrupt an escalating conflict or emotional reaction and reconnect before continuing.

Gaylen said that when Heather turned toward him, his anger quickly disappeared. He then gave her permission to use the same technique whenever she saw him beginning to spiral in the future.

Whether or not you use Gaylen's specific method, there is a useful concept here for developers: we need an interrupt.

In programming, an infinite loop does not usually fix itself because we let it run longer. Sometimes something external has to break the cycle. Our work habits can operate the same way.

Build an Interrupt into Your System

Developers love systems, so it can help to think about disconnecting from work as something we deliberately design rather than something we hope happens automatically.

Your interrupt does not have to be Gaylen's "monument." It can be something simple that signals the transition from work mode to the rest of your life.

• Take a walk after shutting down your computer.
• Write down the next step before leaving a difficult problem.
• Create a short end-of-day routine that closes out unfinished work.
• Exercise before transitioning into your evening.
• Set a fixed point when you stop checking Slack, Teams, or email.
• Talk with someone rather than continuing to replay the problem internally.
• Step away from a frustrating problem before making another major change.

The specific ritual matters less than recognizing why you need it. If you regularly leave work in a frustrated state, simply closing the laptop does not necessarily reset that state. You may physically leave the work while mentally carrying it into everything you do afterward.

That affects more than your evening. It can affect how you communicate with the people around you and how prepared you are to return to work the next day.

You Cannot Debug Everything by Working Harder

Michael asked Gaylen an important follow-up question: what if you do not have someone there to help you reset?

Gaylen described an experience while Heather was away visiting family. He had become frustrated while working with ChatGPT and realized that Heather would normally recognize when he was becoming agitated. Without her there, he experimented with mentally recreating the calming experience they had practiced together.

He said that simply closing his eyes and imagining the familiar interaction helped him calm down. Gaylen attributed that response to repeatedly practicing their method until it had become a familiar signal for him to reset.

The larger lesson is not that developers need to reproduce Gaylen's exact technique. It is that we can become better at recognizing when our current state is no longer helping us solve the problem.

When you have been staring at the same code for three hours, are you still debugging effectively? When you are angry at a tool because it is not behaving the way you expect, are you still evaluating the problem objectively? When you are rewriting something for the third time late at night, are you improving it, or are you just unwilling to stop?

Developers spend enormous amounts of time learning how to diagnose systems. We should learn to diagnose ourselves, too.

Find Your North Star

Rob connected Gaylen's Monument Method to something we regularly discuss in software projects and businesses: having a "why."

Projects need a reason for existing. Without that reason, teams can spend enormous amounts of energy building things that do not actually move them toward their goal. A clear purpose becomes a North Star. When the project begins drifting, the team can return to the original question: what are we actually trying to accomplish?

Developers can apply that idea to our careers. Why are you working so hard? Why are you pursuing the promotion? Why are you building the business? Why are you learning another technology? Why are you putting in the extra hours?

There may be excellent answers to all of those questions. The danger comes when the work becomes its own answer.

Developer Work-Life Balance Requires Boundaries

There is a strange contradiction in successful technology careers. We work hard because we want to build a better life, but the habits that help us succeed can gradually consume the life we were trying to build.

Gaylen argued that professional success, money, and recognition lose much of their meaning if they come at the expense of the important relationships around us. Rob connected that back to the larger theme of Career Beyond the Code: career success needs a reason behind it. Simply pursuing more work, more money, or more achievement does not provide a natural stopping point.

That is why developer work-life balance cannot be solved entirely with productivity hacks. Sometimes you need to recognize that enough is enough.

The defect can wait until tomorrow. The email does not need an answer tonight. You do not have to understand every new AI tool this weekend. The side project does not have to become another full-time job. The people around you should not always receive whatever energy remains after work takes the best of you.

Those are not failures of ambition. They are boundaries that can make a long career possible.

Quality Includes the Life Around the Product

At EnvisionQA, we talk about finding problems before customers do. That mindset usually applies to software quality, requirements, integrations, processes, and the technology supporting a business. However, quality is also about sustainability.

A development process that only succeeds because someone works eighty hours is not a healthy process. A release that depends on heroics every time has a system problem. A developer who cannot disconnect from work indefinitely will eventually pay for that somewhere.

Building better developers should mean more than producing developers who can write better code. It should mean developing people who can solve difficult problems without allowing every difficult problem to consume them.

That is part of building a career beyond the code.

Developer Work-Life Balance Means Knowing When to Stop

One of Gaylen's final reflections brought both parts of our conversation together. Looking back over a life filled with career changes, financial setbacks, addiction, serious illness, and a heart transplant, he asked himself why those experiences had not left him bitter.

His answer was gratitude.

Gaylen said he realized that he had generally been more grateful for what he still had than bitter about what he had lost. Today, he practices that gratitude more intentionally.

For developers, perhaps there is another lesson in that perspective. We are trained to see what is broken. It is literally part of the job. We find the missing requirement, failed test, security vulnerability, performance bottleneck, integration problem, or defect that everyone else missed.

That skill makes us valuable, but we cannot spend our entire lives looking only for the next thing that needs fixing.

Sometimes building a career beyond the code means recognizing what is already working, protecting the things that matter, and knowing when to walk away from the keyboard.

The problem will still be there tomorrow. You will probably solve it better after the reset.

About Gaylen A. Wilson

Gaylen A. Wilson is an author, relationship coach, technology veteran, entrepreneur, former farmer and stockbroker, and heart-transplant recipient. His professional life has included multiple reinventions, from agriculture and finance to PeopleSoft consulting, computer repair, nationwide technical field work, writing, and relationship coaching.

Together with his wife, Heather, Gaylen developed the Monument Method, a relationship framework centered on interrupting destructive conflict patterns and protecting the connection between partners. Their work grew from their own relationship, Gaylen's health journey, his involvement in large online communities discussing intimacy and relationship challenges, and their subsequent relationship-coaching education.

Gaylen described his broader philosophy during our conversation as intentionally protecting what matters most rather than allowing the pressures of work and the outside world to damage it. You can learn more about Gaylen, his books, and his work through his PodMatch profile and the Monument Method Institute.

Stay Connected: Join the Developreneur Community

👉 Subscribe to Building Better Developers for more conversations on momentum, leadership, and growth. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at [email protected] with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development.

Additional Resources

• Constructive Communication in Software Development That Drives Results
• Facilitative Leadership: Why Modern Teams Need Guides Instead of Heroes
• Software Communication Gaps: The Hidden Foundation Problem Slowing Your Team
• Building Better Developers Podcast Videos – With Bonus Content

Career reinvention for developers is usually discussed in terms of learning a new language, moving into leadership, adapting to AI, or finding a new job. Our conversation with Gaylen A. Wilson takes that idea much further. His story shows what happens when careers, businesses, health, and even our assumptions about the future stop following the plan.

Season 29 of Building Better Developers is about building a career beyond the code. Usually, that leads us into conversations about leadership, communication, business value, quality, or the skills developers need as they move beyond completing technical tasks. This episode is different. We did not spend much of the first half talking about software development or QA. Instead, Gaylen told us his story, and the longer he talked, the clearer the connection to developers became.

Gaylen's career repeatedly forced him to confront something most developers eventually discover for themselves: you can master the tools, solve complicated problems, and work incredibly hard, but none of that guarantees life will follow the architecture you designed. His story is ultimately about rebuilding when the plan fails, which makes it an unexpected but valuable example of career reinvention for developers.

About Gaylen A. Wilson

Gaylen A. Wilson is an author, relationship coach, technology veteran, entrepreneur, farmer, former stockbroker, and heart-transplant recipient whose career has required him to reinvent himself repeatedly. Together with his wife, Heather, Gaylen developed the Monument Method, an approach they use to help couples interrupt destructive conflict patterns and strengthen their connection.

Learn more about Gaylen A. Wilson through his website http://www.monumentmethodinstitute.com/.

Your Career Is Not a Straight Line

Gaylen started his professional life far away from software. He became a farmer in his twenties and built a life around agriculture. Then drought and crop failures destroyed the business, eventually forcing him into farm bankruptcy. He had to reinvent himself.

His next chapter took him into finance as a stockbroker. He succeeded there until a company whose investment he had recommended collapsed after, according to Gaylen, misrepresenting its financial condition. He described that experience as another devastating setback. Without healthy ways to process what had happened, his personal life also began to unravel.

Eventually, Gaylen rebuilt again. In 2001, he found work as a traveling PeopleSoft computer consultant. He was earning more than he ever had before and thought he had finally reached a stable point in his career. Then September 11 changed the economy and consulting industry around him. By December, he had lost that job. After months of searching and hundreds of mailed résumés, he eventually took a job as a Walmart cashier because he needed to work.

For developers, there is an important lesson in that progression. We often build our identity around what we do. We become the Java developer, QA engineer, architect, DevOps specialist, technical lead, or whatever role currently defines our career. Then the technology changes, the company restructures, a project ends, AI changes part of the workflow, or the market simply decides that yesterday's valuable skill is commonplace today.

The ability to rebuild can matter more than the title you are trying to protect.

Career Reinvention for Developers Starts with Transferable Skills

One interesting part of Gaylen's story is how often skills from one chapter became useful in the next. Farming did not look anything like stockbroking, and stockbroking did not look much like technology consulting. Yet farming had already taught him far more about running a business than people might assume.

When the consulting market disappeared, Gaylen eventually returned to his hometown and put a small advertisement in the newspaper offering computer repair. He had never worked professionally as a computer repair technician. He had simply been interested in computers for years. Within six months, that small side business was outperforming his Walmart job.

Then the market changed again.

As Windows became more reliable and malware-related repair work declined, the computer repair business that had supported Gaylen and his wife, Heather, began drying up. Instead of assuming the old business would somehow return, he adapted. He found platforms connecting technicians with companies needing field work and began traveling throughout rural America installing and servicing technology. Eventually, that work took Gaylen and Heather through all 48 contiguous states.

That is where this story starts to sound much more familiar to a developer.

Technologies disappear. Frameworks fall out of favor. Companies reorganize. Entire categories of work become automated. A skill that was valuable five years ago can become commonplace today. Career reinvention for developers becomes easier when we stop defining ourselves by a particular technology and start recognizing the skills that survive those changes.

Those transferable skills include:

• Problem-solving and troubleshooting
• Learning unfamiliar systems quickly
• Breaking complicated problems into manageable pieces
• Communicating with customers and stakeholders
• Testing assumptions instead of blindly following them
• Adapting when the original solution no longer works
• Understanding the business problem behind the technology

Those are skills that remain valuable even when the code changes.

Developers Are Professional Problem Solvers

There is another reason Gaylen's story belongs in a developer-focused season. Developers tend to be fixers. Give us a broken system, and we immediately start looking for the defect. We gather information, isolate variables, test assumptions, and keep working until we understand the problem.

That mindset is incredibly valuable. It can also become dangerous when we start treating every problem as something we can overcome simply by working harder.

Michael touched on this at the beginning of the episode while discussing the difficulty of slowing down after a period of long releases and extreme work hours. Even after the immediate pressure disappeared, there was still that feeling that he should be doing something. Rob connected that experience to the larger conversation about how quickly technology is moving and how easily careers can consume the rest of our lives.

Many developers know that feeling. There is always another ticket, certification, framework, side project, production issue, release, or AI tool to learn. We tell ourselves things will calm down after the current deadline. Then another deadline appears.

Gaylen eventually encountered a problem he could not outwork.

When Working Harder Stops Being the Solution

In 2014, after years of traveling for technical work, Gaylen became seriously ill. He initially believed he had pneumonia and continued working. During another extended trip, his condition worsened until a clinic in Corpus Christi sent him for a chest X-ray. He was told he had congestive heart failure and needed to return to Colorado.

His first reaction is revealing.

The jobs were paying well. They already had weeks of work scheduled. Gaylen initially thought they should finish the trip before dealing with his heart. It took two more jobs before the seriousness of the situation finally broke through his drive to keep working.

That moment is an extreme example of a pattern many developers experience in smaller ways. We know we need sleep, but the release needs to go out. We know we need a weekend away from the computer, but production has a problem. We know we have not spent enough time with the people around us, but there is one more thing we need to finish.

The problem-solving mindset becomes a trap when the answer to every problem is simply more effort.

There are times when the correct solution is to stop.

What Are You Actually Building?

Gaylen eventually received a heart transplant in 2018. He described being near the point where he and Heather were preparing for the possibility that he would die when they received the call that a donor heart was available. By the next morning, Gaylen had received a new heart. He viewed the transplant as an opportunity to have more years with the person who had stayed beside him throughout his illness.

That experience changed what success meant to him.

It also gives developers a useful question to ask about our own careers: What are we actually building?

We spend our days building systems for other people. We think about architecture, scalability, reliability, technical debt, requirements, and defects. Yet we do not always apply the same intentional thinking to the systems surrounding our careers.

A successful career should support a life rather than consume it.

That does not mean ambition is wrong. It does not mean developers should stop working hard or stop pursuing difficult goals. It means the career itself should serve something larger. Otherwise, we can become incredibly efficient at building a future we eventually discover we did not want.

Quality Applies Beyond Software

At EnvisionQA, we spend a lot of time thinking about quality and finding problems before customers encounter them. One lesson from this conversation is that the same mindset can extend beyond software.

In software, we do not wait for catastrophic failure if we can avoid it. We monitor systems. We test assumptions. We look for warning signs. We examine recurring defects because they often point toward deeper problems.

Our careers deserve similar attention.

If every release requires heroics, something may be wrong with the process. If every week requires sixty or eighty hours, the workload may not be sustainable. If you cannot stop thinking about work when you leave the computer, that is information worth examining. If professional success consistently comes at the expense of health or important relationships, simply becoming more productive may not solve the underlying problem.

Sometimes the system itself needs redesigning.

Career Reinvention for Developers Is About More Than Technology

Gaylen's journey moved from farming to finance, technology consulting, computer repair, nationwide field service, serious illness, a heart transplant, and eventually relationship coaching. That is hardly a traditional developer career path, but that is precisely why this conversation fits our season.

Careers rarely follow the architecture diagram we created when we started them.

Technologies change. Businesses disappear. Markets collapse. Health changes. Priorities change. Sometimes we make mistakes, and sometimes circumstances outside our control rewrite the requirements completely.

The developers who build lasting careers are not necessarily the ones who perfectly predict what comes next. They are the ones who learn, adapt, rebuild, and carry lessons from one chapter into the next. That is ultimately what career reinvention for developers is about.

Most importantly, the career is not the final product. The life you are building around it is.

In Part Two of our conversation with Gaylen A. Wilson, we bring his experiences more directly back to developers, burnout, fight-or-flight thinking, relationships, and the challenge of leaving work at work. We also explore what happens when the same problem-solving mindset that makes us effective developers follows us home.

Stay Connected: Join the Developreneur Community

👉 Subscribe to Building Better Developers for more conversations on momentum, leadership, and growth. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at [email protected] with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development.

Additional Resources

• Customer Feedback for Developers: How to Listen Without Losing Your Vision
• Developer Career Growth: Breaking Through Stagnation
• Building Better Developers Podcast Videos – With Bonus Content

AI communication skills are becoming increasingly important as developers and leaders rely on artificial intelligence to summarize meetings, draft emails, organize ideas, and prepare for conversations. These tools can save time, but Salvatore Manzi warns that using AI to improve your communication is different from letting AI do your communicating for you.

In Part 2 of our Develpreneur conversation with leadership communication coach Salvatore Manzi, Rob Broadhead and Michael Meloche explore how communication changes as developers move into leadership roles and AI becomes part of everyday work. The discussion covers better meetings, keeping remote teams engaged, using AI without weakening your skills, and communicating technical ideas to people outside your technical world.

About Salvatore Manzi

Salvatore Manzi is a leadership communication coach whose work sits at the intersection of leadership, communication, and collaboration. He works with teams and analytical professionals to improve how they communicate so they can collaborate more effectively and move their ideas forward.

Salvatore is also the author of Clear and Compelling: Communication Strategies for Big Thinkers. During the Develpreneur interview, he explained that the book draws on twenty years of communication strategies and is aimed particularly at analytical, technology, and data-driven professionals with bold ideas.

His work fits closely with the theme of this season of Develpreneur: building a career beyond the code. Technical knowledge can help you develop the idea, but leadership communication helps you explain it, build support for it, and bring other people along with you.

Social: Facebook / YouTube / Instagram / LinkedIn

Website: Salvatore Manzi's Website

AI Communication Skills Need Structure Without Becoming Rigid

Developers tend to appreciate systems, but nobody wants every conversation governed by a complicated set of rules. That creates an interesting challenge for leaders: How much structure does a team actually need?

Salvatore describes communication as an art rather than something with a perfect formula. However, he also points out that people need parameters. We need to understand the limits before we know how much freedom we have within them. In a meeting, those limits might involve time, the topic being discussed, or how long each person should speak.

The key is keeping those parameters flexible. Telling someone they can speak exactly seven words creates a new problem because everyone becomes focused on satisfying the rule. Asking people to keep their responses to one or two sentences establishes a useful boundary without turning the meeting into an exercise in compliance.

This same principle applies throughout leadership. Structure should help people communicate rather than becoming another obstacle they have to navigate.

Better Communication Starts With the Purpose of the Meeting

Many developers have experienced days where one meeting runs into another until there is almost no time left to do the work discussed in those meetings.

The problem is not necessarily whether a meeting lasts fifteen minutes, thirty minutes, or an hour. Salvatore recommends starting with a more fundamental question: What is this meeting supposed to accomplish?

Without a clear objective, a meeting can continue indefinitely without producing a useful result. When people understand the goal and receive the information they need beforehand, however, some discussions can be resolved in five or fifteen minutes.

That changes the way we think about meeting efficiency. Instead of automatically scheduling an hour and trying to fill it, define the outcome first. Then determine how much time the team actually needs to achieve it.

AI Communication Skills Require Human Engagement

AI has added another dimension to the meeting problem. We can now record conversations, generate transcripts, create summaries, identify action items, and return to the information later.

Those capabilities are useful, but they can also create an excuse to disengage.

Michael raised a scenario that is becoming increasingly familiar: people attend a meeting with their cameras off, barely participate, and depend on an AI-generated transcript or summary to tell them what happened afterward.

Salvatore argues that leaders have a responsibility to create engagement. His recommendation is the 3-5 rule: every three to five minutes, introduce some type of pattern disruption that brings people's attention back into the conversation.

That disruption does not need to be dramatic. A leader can:

• Ask a question.
• Poll the group.
• Ask someone for their perspective.
• Introduce an interactive visual.
• Connect the discussion directly to someone's current work.
• Change the way information is being presented.

One of the most effective approaches is relevance. If you connect a discussion directly to a project someone is working on, that person's attention naturally increases. Instead of simply delivering information to a group, you are showing people why that information matters to them.

That may become even more important as AI makes passive participation easier.

Communication Skills Matter Even Without a Leadership Title

What happens when you recognize that a meeting is failing but you are not the person running it?

That is a common situation for developers. You may be sitting in a retrospective, refinement, stand-up, or project meeting where two or three people dominate the conversation while everyone else remains silent.

Salvatore suggests that participants can help create engagement without taking over the meeting. One approach is to respond directly to something another person shared. You might briefly explain what you appreciated about their contribution and how it connects to the work.

If the meeting consistently prevents important voices from being heard, that can also become a conversation with the facilitator before the next meeting. Explain what you need from the discussion and why hearing from more of the team matters to your ability to move forward.

This approach changes the interaction from criticism to a request. You are not simply saying that the meeting is bad. You are explaining what would make it more useful and why that change matters.

Better Team Communication Permits People to Challenge Ideas

Another problem appears when people have concerns but do not want to become known as the person who is always negative.

Salvatore described working with a startup that deliberately assigned roles such as the critic, negator, or contrarian during meetings. The roles rotated between people, which gave someone explicit permission to challenge an idea without being personally labeled as difficult.

Over time, something interesting happened. Team members reported feeling safer raising uncomfortable issues even when they were not officially assigned one of those roles.

For software teams, this concept has obvious applications. Projects need people willing to ask what happens when something fails. Testers need to challenge assumptions. Developers need to question architecture. Business analysts need to identify missing requirements. Security teams need to think about how a system could be abused.

The goal is not negativity. The goal is making constructive disagreement part of the process instead of treating disagreement as a personality problem.

Communication Skills Help Make Complex Ideas Easier to Process

Technical experts often struggle with another communication problem: we know too much.

When someone asks about a system we have spent months or years building, it is tempting to provide all the history, context, data, dependencies, and edge cases necessary to prove that we understand the problem.

The listener probably does not need all of it.

Salvatore recommends narrowing information into a small number of meaningful chunks. The underlying data still matters, but effective communication requires deciding which pieces another person needs to understand before moving forward.

That skill becomes increasingly important as developers move into leadership. Executives, customers, operations, marketing, legal, and other departments usually do not need the same technical depth that another developer needs.

The challenge is not knowing the information. It is knowing which information matters to the person in front of you.

Do Not Let AI Communication Replace Your Own Voice

This is where AI communication skills become particularly important.

AI can summarize a long email chain, draft a response, prepare meeting notes, and transform rough ideas into polished language. Salvatore sees value in those capabilities, but he also sees a risk when AI begins communicating back and forth while the people involved stop thinking for themselves.

Eventually, an email conversation becomes a real conversation.

At that point, you have to explain the ideas that supposedly came from you. You have to answer questions in real time, adapt to new information, defend decisions, and respond to a dynamic situation. If AI has been doing all of the thinking and communicating for you, you may discover that you are not prepared for that conversation.

Salvatore's recommendation is straightforward: use AI, train it toward your voice, and take advantage of what it can do, but always read before you send.

Edit the output. Make sure it sounds like you. More importantly, make sure you understand and can stand behind what is being communicated.

AI should reduce unnecessary work. It should not remove you from your own communication.

Build AI Communication Skills One Problem at a Time

Avoiding AI entirely is not the answer either. The challenge is maintaining your own cognitive and communication abilities while taking advantage of increasingly capable tools.

Salvatore offers a simple exercise: one a day.

Once each day, take one problem and work through it yourself before asking AI for help. Think through the problem, develop your answer, and write out your reasoning. Then give it to AI and compare what it produces with your approach.

That creates a very different relationship with the technology.

Instead of asking AI to think so you do not have to, you are using it as another perspective against which you can test your thinking. You continue exercising the skill while also learning where AI can improve your work.

For developers, this should feel familiar. We do not become better programmers by copying code we do not understand. We improve by solving problems, reviewing alternatives, testing assumptions, and learning from the differences.

AI communication skills should develop the same way.

Communication Skills Help You Speak Outside the Technical Room

Technical professionals face another challenge as their careers progress. Eventually, the audience changes.

You may spend years discussing systems with developers who understand your terminology, assumptions, and data. Then you walk into a room with operations, HR, legal, sales, marketing, executives, or customers.

The same communication approach no longer works.

Salvatore recommends practicing metaphors because they force technical professionals to translate an idea into something another audience can visualize. He compares this to giving someone the blueprint for a house versus showing them a rendering of the finished house. The blueprint may contain more precise technical information, but the rendering makes the result easier to understand.

Developers can practice this before they ever enter the boardroom. Pick something familiar—a hobby, sport, cup of coffee, or everyday activity—and explain how your current project is similar to it.

The metaphor does not need to be perfect. The exercise teaches you to look at the same technical idea through a different lens.

That ability to switch communication styles is part of moving beyond the code. The more responsibility you gain, the more likely you are to communicate with people who do not share your technical background.

AI Communication Skills Are Career Skills

AI will continue getting better at writing, summarizing, organizing, and preparing information. That makes learning how to use these tools important, but it also increases the value of the skills that remain human.

Can you read the room? Can you recognize when someone has disengaged? Can you explain a technical problem to a nontechnical stakeholder? Can you challenge an idea without attacking the person who proposed it? Can you answer an unexpected question without waiting for an AI-generated response?

Those are communication skills, but they are also career skills.

Salvatore's advice is not to reject AI. Use it for interview preparation. Use it to evaluate your communication. Use it to help generate new ways to explain an idea. Train it to better understand your voice.

Just do not stop developing your own.

Stay Connected: Join the Developreneur Community

👉 Subscribe to Building Better Developers for more conversations on momentum, leadership, and growth. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at [email protected] with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development.

Additional Resources

• Business Communication – Critical For Success
• Gain Wisdom Through Better Listening Skills
• Constructive Communication in Software Development That Drives Results
• Building Better Developers Podcast Videos – With Bonus Content

Leadership communication becomes increasingly important as developers move beyond writing code and begin leading people, projects, and technical decisions. Being the smartest person in the room does not automatically make you the most effective leader. In fact, technical expertise can sometimes work against a team when the people with the strongest opinions dominate the conversation while everyone else quietly checks out.

In this episode of Develpreneur, Rob Broadhead and Michael Meloche talk with leadership communication coach Salvatore Manzi about what happens when developers and technical experts move beyond writing code and begin leading people. The conversation focuses on learning how to make sure people understand one another, contribute their ideas, and work together instead of simply talking past each other.

About Salvatore Manzi

Salvatore Manzi is a leadership communication coach whose work sits at the intersection of leadership, communication, and collaboration. He works with teams and analytical professionals to improve how they communicate so they can collaborate more effectively and move their ideas forward.

Salvatore is also the author of Clear and Compelling: Communication Strategies for Big Thinkers. During the Develpreneur interview, he explained that the book draws on twenty years of communication strategies and is aimed particularly at analytical, technology, and data-driven professionals with bold ideas.

His work fits closely with the theme of this season of Develpreneur: building a career beyond the code. Technical knowledge can help you develop the idea, but leadership communication helps you explain it, build support for it, and bring other people along with you.

Social: Facebook / YouTube / Instagram / LinkedIn

Website: Salvatore Manzi's Website

Leadership Communication Starts With Understanding

Technical professionals are trained to process information quickly. Someone explains a problem; our brains immediately start analyzing it. This happens even before the other person has finished speaking; we may already be constructing a solution.

That speed can be useful when solving technical problems. It can also create problems when working with people.

Salvatore recommends starting disagreements with shared understanding. Before offering your own response, rephrase what you believe the other person said and confirm that you understood it correctly. The purpose is not to repeat their words back to them. It is to demonstrate that you understood the idea behind those words before adding your own perspective.

For developers, this can initially feel inefficient. If everyone in the room understands the technology, why repeat something that was just said? Salvatore argues that this small investment in leadership communication can prevent much larger problems later. Rephrasing establishes a common starting point, particularly when people are working together for the first time or discussing an important decision.

A simple way to develop the habit is to practice it once each day. Find one conversation where, instead of immediately responding, you rephrase what you heard and ask whether you understood correctly. Over time, that intentional pause becomes part of how you communicate.

Get Your Voice Into the Meeting Early

The opposite communication problem happens when someone has something useful to contribute but waits too long to speak.

This is common on technical teams. When one or two people are viewed as the experts, everyone else may wait for them to speak first. Once those people begin driving the conversation, it becomes increasingly difficult for quieter team members to enter it.

Salvatore recommends getting your voice into the meeting within the first five minutes. You do not need to make a profound statement or immediately solve the problem. Ask a question, acknowledge someone else's contribution, or invite another person into the discussion. The goal is simply to become an active participant instead of a passive observer.

That advice applies especially well to developers who prefer to process information before speaking. Waiting until you have the perfect answer can mean never entering the conversation at all.

Leadership can make that problem easier or harder.

Leadership Communication Means Creating Space for Everyone

A leader's responsibility is not simply to provide the best answer. Effective leadership communication also means creating an environment where the team can produce better answers together.

Salvatore suggests several practical techniques for getting more people involved. One is a quick round-the-room check-in where everyone provides something that is going well and something that could be going better. The important part is establishing limits, such as one sentence for each answer, so the exercise does not turn into a series of monologues.

Another technique is the 10-second rule. Ask the team a question, but tell everyone that nobody should answer for ten seconds.

That short silence gives analytical team members time to process the question. Instead of rewarding whoever can speak fastest, the meeting gives everyone a chance to formulate a useful response.

These techniques are simple, but that is part of their value. Improving team communication does not necessarily require a new platform, complicated process, or another meeting. Sometimes it requires changing the way an existing conversation is structured.

Stop Putting People on the Spot

There is also a significant difference between inviting someone into a conversation and suddenly calling on them.

Anyone who remembers being unexpectedly called on in school knows the feeling. Instead of thinking about the question, your brain suddenly starts thinking about the fact that everyone is looking at you.

Salvatore demonstrated a better approach during the conversation: give the person advance notice that you are coming to them, tell them the subject you want them to address, briefly move the group's attention elsewhere, and then return to them.

That small handoff gives the person time to prepare mentally. Instead of creating a performance test, the leader creates an opportunity for the person to contribute.

This is particularly useful for technical teams where some members may need a few moments to organize a complex answer. The goal is not to eliminate spontaneity. It is to stop confusing fast responses with valuable responses.

Breaking Down Technical Silos Through Better Communication

Technical organizations naturally create specialists. One person understands the database. Another understands infrastructure. Someone else knows the business rules, testing process, user interface, or deployment pipeline.

Specialization helps teams solve difficult problems, but it can also create silos.

Salvatore described working with a team whose individual groups functioned effectively but struggled when they came together. They were essentially speaking different languages. During a half-day session, the team worked through what they needed from one another and what they believed they were hearing from each other. They also formed smaller cross-functional groups.

One surprisingly effective part of the exercise was asking people to begin by identifying something they appreciated about another person's work. That forced team members to think about what their colleagues actually contributed rather than seeing only their own piece of the system.

That lesson matters in software development because it is easy to judge another role by how it affects your own work. Developers can become frustrated with QA. QA can become frustrated with developers. Technical teams can become frustrated with business stakeholders, while business stakeholders wonder why seemingly simple requests take so long.

Good leadership communication helps people understand those different perspectives. Understanding what another person contributes does not eliminate disagreement, but it gives that disagreement context.

What About the Person Who Never Stops Talking?

Creating space for quieter voices introduces another challenge: the person who takes too much of that space.

Most teams have experienced the meeting where someone gets the microphone and never seems to give it back. Salvatore recommends addressing this before it becomes a problem by establishing communication agreements for the team.

Those agreements might include:

• Make sure every voice has an opportunity to contribute.
• Keep individual contributions short.
• Lead with the bottom line.
• Provide additional detail when the group needs it.

Once those expectations are established, a phrase such as "bottom line" can become a shared reminder rather than a personal criticism. Everyone already understands what it means and why the team uses it.

That distinction is important. Good meeting facilitation is not about silencing people. It is about managing the limited amount of attention and time available so the entire team can participate.

Leadership Communication and the Move From Expert to Leader

One of the biggest career transitions for developers is moving from being responsible for your own technical output to helping other people produce results.

The skills that made you a strong developer do not disappear when you become a technical lead, architect, manager, or executive. However, they are no longer enough by themselves.

You have to listen differently. You have to recognize when someone has stopped participating. You have to create room for people who need time to think while keeping stronger personalities from consuming the entire conversation. Most importantly, you have to stop measuring leadership communication by whether you successfully said something and start asking whether the other person actually understood it.

Salvatore's advice throughout this conversation comes back to small, repeatable behaviors. Rephrase someone's idea once a day. Speak within the first five minutes of a meeting. Give people ten seconds to think. Prepare someone before handing them the conversation. Establish expectations before someone starts monopolizing the room.

None of those techniques requires a major organizational transformation. They require practice.

That is also what makes them valuable for developers working to build a career beyond the code. Leadership does not suddenly begin when someone gives you a management title. It develops through the way you communicate, listen, facilitate, and help the people around you contribute their best ideas.

Stay Connected: Join the Developreneur Community

👉 Subscribe to Building Better Developers for more conversations on momentum, leadership, and growth. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at [email protected] with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development.

Additional Resources

• Constructive Communication in Software Development That Drives Results
• Facilitative Leadership: Why Modern Teams Need Guides Instead of Heroes
• Software Communication Gaps: The Hidden Foundation Problem Slowing Your Team
• Building Better Developers Podcast Videos – With Bonus Content

AI can produce code faster, automate repetitive work, analyze enormous amounts of information, and move development tickets at a pace that would have seemed impossible only a few years ago. Yet faster execution does not automatically create better software delivery systems. In fact, speed can expose weaknesses that organizations previously managed to hide.

That distinction becomes central in the second part of Dana Hetté's conversation with Rob Broadhead and Michael Meloche on Building Better Developers. The first part explored the organizational cracks that form between roles and how developers can use systems thinking to recognize them. Part two moves from recognition to action: What happens when delivery is already struggling? Where should an organization start? And what role should AI play when the underlying system is not healthy?

Hetté's answer challenges a common technology reflex: tools do not define the solution. They enable a solution that has already been defined.

About Dana Hetté

Dana Hetté, also known as Blondie Geek, is a fractional operator who works with founders and leadership teams at startups and small product organizations. After earning a degree in computer science, Dana spent roughly seven to eight years as a software engineer before moving toward roles centered on product, operations, and the connection between business strategy and execution.

Dana describes her work as occupying the operator seat: the connective role between what leadership intends and what teams ultimately deliver. Her approach combines the systems-thinking foundation of software engineering with product discovery, organizational operations, and cross-functional communication. Rather than focusing only on what individual teams are doing, she looks for the cracks between roles where context, ownership, and business intent can disappear.

You can learn more about Dana at BlondieGeek.com, connect with Dana Hetté on LinkedIn, or subscribe to her newsletter, Closing the Loop.

Software Delivery Systems Need Diagnosis Before Automation

Consider an organization where code is still shipping, deadlines are still being met occasionally, and every department appears busy. Underneath that activity, however, pipelines are breaking, quality is deteriorating, bugs are increasing, and teams are getting closer to the red line.

Leadership sees the symptoms and understandably wants a solution. Maybe another project-management platform will help. Perhaps a new AI development tool can increase productivity. Maybe another automation will remove the bottleneck.

Hetté argues that this is precisely where organizations need to resist the urge to start with technology.

"Tools are not the solution, they enable a solution that you've already defined." — Dana Hetté

When an organization does not understand why its delivery system is breaking, automating that system can simply make the dysfunction operate faster.

Instead, Hetté returns to something far less glamorous: discovery. She describes getting into the organization, talking to people, observing stand-ups, examining project-management tools, reviewing tickets, and investigating what is actually happening. AI can assist with that process by analyzing meeting transcripts, tickets, sprint information, and other organizational data, but human judgment remains necessary to understand what those signals mean.

The objective is not immediately to fix everything. It is first to define what is actually broken.

Software Delivery Systems Improve When You Find the Real Problem

Hetté points to the classic Five Whys technique as an example of how straightforward the diagnostic process can be. Something is broken. Why? Then ask why again. Continue until the team moves past symptoms and reaches the underlying cause.

This sounds elementary, particularly to developers accustomed to sophisticated debugging tools, observability platforms, AI assistants, and complex architectures. Yet the underlying principle is remarkably similar to debugging software.

A developer encountering an exception does not permanently solve the problem by hiding the error message. The exception is evidence. You trace the execution path until you understand the condition producing it. Organizational problems deserve the same discipline.

Hetté describes the balance as spending much of the effort defining the problem. Once the actual problem is understood, the solution often becomes surprisingly straightforward.

Complicated symptoms do not necessarily require complicated solutions. The complexity may come from not knowing which problem you are actually solving.

This is also why firefighting becomes dangerous when it turns into a permanent operating model. There are legitimate emergencies where a team has to use what Hetté calls "duct tape and chewing gum" to cross the finish line. Production needs to be restored. A deadline cannot move. A customer needs an immediate resolution.

The distinction is whether the organization recognizes that temporary stabilization is not the same as solving the underlying problem.

Align Software Delivery Systems Around Trade-Offs

Even after identifying a problem, teams can struggle because leadership and engineering are optimizing for different outcomes. Engineering may want time to improve quality. Leadership may be focused on speed to market. Finance may be focused on cost. Product may be trying to protect scope.

None of those priorities automatically makes one group wrong. The organization needs to decide what matters most.

Hetté uses what she calls project sliders during discovery. The idea involves familiar project constraints such as speed, quality, and cost. Leadership needs to determine where those sliders sit rather than pretending every priority can simultaneously be maximized.

A company may say it needs to reach the market tomorrow while spending as little as possible. The next question becomes unavoidable: What is the organization willing to compromise—quality, scope, consistency, or time?

Many disagreements that appear technical are actually disagreements about priorities that were never explicitly decided.

Those decisions provide developers with something extremely valuable: guardrails.

Hetté shares an example involving an engineer who wanted an increasingly agentic workflow. Instead of manually associating work, the engineer wanted one agent to determine what another agent was doing. Hetté suggested a dramatically simpler solution: put the ticket number in the pull request. The engineer could even have an agent perform that step.

The exchange highlights an important distinction between technological possibility and operational value. An organization does not need to automate something merely because it can.

Once leadership has established its priorities, those principles can become the North Star for deciding which practices are worthwhile.

AI Needs Human Inspection Points

The role of human judgment becomes particularly important when AI enters development workflows.

In Part One, Hetté described developers using AI agents to move rapidly through tickets only to discover during user acceptance testing that the output did not match what was actually needed. Part Two reinforces why those failures matter.

AI dramatically increases the amount of work a developer can produce. However, increasing output also increases the consequences of an incorrect assumption. A misunderstanding that once produced one incorrect implementation might now propagate across specifications, tickets, code, tests, and documentation before anyone notices.

The answer is not necessarily a large new governance process. Sometimes it is simply a sanity check.

An AI agent turns raw requirements into a specification. Before the next agent begins producing code, someone reads the specification and asks: Is this actually what we need?

That lightweight inspection can prevent an incorrect assumption from accelerating through the entire delivery pipeline.

Find one AI-assisted handoff in your development process and introduce a deliberate verification point. Do not merely ask whether the AI completed the task. Ask whether its output still matches the original intent.

Human-in-the-loop development should not mean manually redoing everything AI produces. That would eliminate much of AI's value. Instead, human judgment should be strategically positioned where errors become increasingly expensive if they continue downstream.

Software Delivery Systems Need Ownership

There is another problem that neither AI nor better tooling automatically resolves: ownership.

As organizations grow, a single operator cannot personally inspect every handoff. If that person attempts to do so, the individual who was supposed to eliminate bottlenecks becomes one.

Hetté discusses solving this through mentorship and delegation. In one engagement, she entered knowing from the beginning that part of her responsibility would be finding and developing her eventual replacement. Rather than building an organization permanently dependent upon her, she worked with a more junior person and transferred the systems-thinking and operational mindset needed to continue the work.

That approach provides an important lesson for technical leaders: good systems should reduce dependency on specific individuals.

This is familiar territory for developers. We know the danger of a critical application understood by only one engineer. We recognize the bus-factor problem in software. Yet organizations can accidentally create exactly the same dependency in operations.

The answer is to transfer judgment, context, and ownership.

Hetté argues that systems thinking, product-oriented thinking, and operational judgment can be taught. Instead of requiring one senior person to own every organizational seam, leaders can develop others who learn how to recognize gaps and take responsibility for them.

AI Changes What Makes Developers Valuable

The conversation ultimately moves from organizational systems back to the developer's career.

Michael Meloche raises a significant concern: If AI increasingly performs junior-level coding work, how do junior developers acquire the experience necessary to become senior developers?

Hetté compares the current transition to earlier abstractions in programming. Developers who worked closer to the machine once worried about newer generations who did not understand the same low-level concerns. She uses the transition from languages such as C++ to Java as an example. Developers could work at a higher abstraction level because the language handled concerns that previously required more direct attention.

AI may represent another, significantly larger abstraction.

Future developers may write less code manually while becoming increasingly skilled at managing AI systems that produce it. Hetté sees the possibility of an AI product builder role that blends characteristics traditionally associated with engineering and product management.

The valuable capability then becomes judgment.

Can you determine whether the generated solution makes sense? Can you understand what the business is trying to accomplish? Can you recognize when an agent has produced something technically plausible but strategically wrong?

These questions point directly back to systems thinking. Knowing syntax matters less when a machine can generate syntax. Understanding consequences becomes more important when a machine can generate enormous amounts of implementation.

Start With One Seam

Hetté closes the extended conversation with a particularly useful challenge for developers who want to move beyond their existing role.

First, adopt an ownership mindset.

Many employees naturally stay within their lane. They complete their assigned responsibilities and leave adjacent problems to whoever supposedly owns them. That approach can be perfectly functional, but it does not develop the perspective required for an operator or broader leadership role.

Hetté describes the alternative as approaching the work with a sense that "the buck stops with me and I own this thing." That does not mean becoming controlling or assuming responsibility for the entire company. It means becoming interested in whether the overall outcome succeeds rather than only whether your assigned portion is complete.

Then make the exercise manageable.

"Pick one handoff point, pick one seam." — Dana Hetté

Perhaps it is the transition from requirements to specifications. Maybe it is specifications to tickets, tickets to pull requests, or pull requests to testing. If AI generates code, compare what was produced against the original specification.

Do not attempt to redesign the entire organization. Investigate one boundary.

As Rob Broadhead notes during the discussion, unclear ownership is frequently where the cracks appear. Ask who owns a particular handoff. If everyone starts looking around the room waiting for someone else to answer, you may have found the gap.

Conclusion: Better Tools Require Better Systems

The future of software development will almost certainly involve more AI, more agents, and greater automation. That makes strong software delivery systems more important, not less.

When execution becomes cheap and fast, mistakes can become cheap and fast too.

The organizations that benefit most from AI will therefore not necessarily be those using the largest number of agents or adopting every new development tool. They will be the organizations that understand what they are trying to accomplish, define ownership clearly, establish meaningful guardrails, and know where human judgment creates the most value.

For developers, this creates an opportunity that goes well beyond learning another AI tool. Learn to diagnose systems. Learn to recognize trade-offs. Learn to see the seams between people, processes, software, and strategy.

When you want to start, follow Hetté's advice: pick one handoff and find out whether it actually works.

Stay Connected: Join the Developreneur Community

👉 Subscribe to Building Better Developers for more conversations on momentum, leadership, and growth. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at [email protected] with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development.

Additional Resources

• Handling Software Delivery Panic: Strategies for Developers
• Software Delivery Clarity: Why Visibility Beats More Process
• Hero Culture Risks: Why AI Is Exposing the Cracks in Software Delivery
• Building Better Developers Podcast Videos – With Bonus Content

Software development careers have traditionally been measured by technical ability: the languages you know, the systems you can architect, the bugs you can solve, and the quality of the code you produce. But developer systems thinking expands that definition. As developers advance in their careers, their value increasingly comes from understanding not only how software works, but how people, processes, business goals, and technical decisions work together.

That distinction sits at the center of Dana Hetté's conversation with Rob Broadhead and Michael Meloche on Building Better Developers. Hetté, also known as Blondie Geek, began her career in software engineering before moving into what she describes as the "operator seat." Her work now focuses on a problem that many startups and product organizations encounter: teams may be busy building, leadership may have a clear vision, and every individual may appear to be doing their job, yet what ultimately ships does not match what the business actually needs.

The problem is often not inside any one role. It exists in the space between those roles.

About Dana Hetté

Dana Hetté, also known as Blondie Geek, is a fractional operator who works with founders and leadership teams at startups and small product organizations. After earning a degree in computer science, Dana spent roughly seven to eight years as a software engineer before moving toward roles centered on product, operations, and the connection between business strategy and execution.

Dana describes her work as occupying the operator seat: the connective role between what leadership intends and what teams ultimately deliver. Her approach combines the systems-thinking foundation of software engineering with product discovery, organizational operations, and cross-functional communication. Rather than focusing only on what individual teams are doing, she looks for the cracks between roles where context, ownership, and business intent can disappear.

You can learn more about Dana at BlondieGeek.com, connect with Dana Hetté on LinkedIn, or subscribe to her newsletter, Closing the Loop.

Developer Systems Thinking Starts With the Gaps

Hetté describes herself as the cohesive layer between strategy and execution. In smaller organizations, founders often carry an enormous amount of context in their heads. They understand the company strategy, board expectations, customer demands, product direction, and countless decisions that have accumulated over time. Engineering, meanwhile, is responsible for turning some version of that vision into working software.

The trouble begins during the transfer.

A founder may tell a technical lead what needs to be built. The technical lead interprets that request through an engineering lens and passes work to developers. The developers execute against the information they have. Everyone can perform their individual job correctly while the organization still produces the wrong outcome.

Hetté calls attention to the "cracks" between roles. Those cracks are where assumptions go unchallenged, context disappears, ownership becomes unclear, and seemingly small communication problems become delivery problems.

A team can execute every assigned task successfully and still fail to accomplish the business objective. Local success does not guarantee system-wide success.

One example from Hetté's work involved a feature-intake process at a larger organization. The company already had automations built into its project-management platform. On paper, the system appeared complete. Requests entered the system, automations processed them, and work moved through the expected steps.

Then Hetté started asking questions.

What business rule determined a particular assignment? Why did an item move from one place to another? Who owned the decision at a particular handoff?

The answers were missing.

The automation existed, but the underlying operational logic had gaps. As Hetté explains, people tend to operate within the boundaries of their defined roles. Consequently, "the gaps form between the roles."

That observation is important for developers because those gaps are fundamentally systems problems.

Why Developer Systems Thinking Goes Beyond Architecture

Developers already learn to think in systems. We reason about dependencies, inputs and outputs, interfaces, failure states, data flows, and interactions among components. When one component behaves incorrectly, experienced developers do not automatically assume that component itself is the root cause. They trace what entered it, what left it, and how it interacts with everything around it.

Hetté argues that this same ability can be applied to an organization.

Instead of looking only at software architecture, look at the architecture of the business producing the software. Where does a requirement originate? Who translates it? What information is lost before it reaches engineering? What happens when engineering finishes? Who determines whether the result actually fulfills the original business objective?

This represents an important shift for developers interested in careers beyond coding.

Hetté spent roughly seven or eight years as a software engineer after earning her computer science degree. Over time, she realized she was becoming more interested in what the team was building and why than in every technical detail of how it was built.

That did not make her engineering background irrelevant. It gave her a different place to apply it.

She specifically identifies systems thinking as one of the strengths engineers can carry into product, program management, and operator roles. The mental model remains useful; the boundaries of the system simply become larger.

Developer Systems Thinking Connects Code to the Customer

One danger of remaining exclusively focused on implementation is that developers can unknowingly optimize for the wrong definition of success.

Engineering naturally views a product through an engineering lens. Users do not.

Hetté references Alan Cooper's The Inmates Are Running the Asylum when discussing this distinction. One of the ideas she draws from the book is that engineers and the people using their software frequently operate with very different mental models. What seems intuitive to someone who understands the internal mechanics of a system may be confusing or ineffective for the person the product was actually designed to serve.

That principle extends beyond user interfaces.

A technically elegant implementation can still solve the wrong problem. A completed ticket can still fail to deliver the expected business outcome. A feature can satisfy its technical acceptance criteria and still disappoint the customer.

Moving beyond code does not mean abandoning technical thinking. It means expanding the system you are thinking about until the user, business objective, and organization become part of it.

Hetté encourages developers to ask larger questions while they work. What is the company trying to accomplish? What is the product supposed to achieve? Does the current work support that strategy? What does the end user actually care about?

Holding that context changes technical decisions because developers are no longer treating a ticket as an isolated unit of work. They are seeing where that ticket fits into the larger machine.

The Operator Vacuum Between Founders and Engineering

This becomes particularly important in startups.

A founder or CEO in a six-person organization cannot spend the entire day translating product ideas for engineering. The founder may also need to work with investors, communicate with the board, develop partnerships, pursue business development, and determine company strategy.

Yet that same founder may be the person carrying the clearest picture of the product.

The result is what Hetté calls an "operator vacuum."

The founder needs to leave the weeds, but the organization still needs someone capable of translating strategy into execution. Hiring a project manager or product manager may address portions of the problem, but Hetté notes that those people also have defined roles. The missing function may be broader: someone who can extract the founder's context, understand stakeholder demands and strategy, and work across project managers, technical leads, and the execution team.

Hetté compares the position to being a first officer to the captain.

The operator is not there to replace engineering, product management, or leadership. The operator connects them.

For developers considering how to broaden their careers, this presents an interesting opportunity. You do not necessarily need the word "operator" in your title to begin thinking this way.

Look for Symptoms Instead of Assuming the Problem

An important part of systems thinking is resisting the temptation to diagnose a problem from its most visible symptom.

A founder may say, "My team isn't shipping on time." Another may say that the team is shipping, but what arrives is not what was envisioned. A leader may feel trapped in day-to-day decisions because leaving the team alone seems likely to send development in the wrong direction.

Those are symptoms.

Hetté approaches them similarly to product discovery. She begins with probing questions for leadership and then talks with the people performing the work. Comparing those perspectives exposes where expectations, processes, and reality diverge.

One useful signal appears when all the formal indicators say a project is healthy while downstream results say otherwise. A project board can be green. Stand-ups can report that everything is on track. Developers can close their tickets. Then the product reaches user acceptance testing and gets rejected.

Something happened between "everything is green" and "this isn't what we wanted."

That seam deserves investigation.

Metrics that measure activity inside individual roles can hide failures at the handoffs between them. A green dashboard is not proof that the entire delivery system is healthy.

This matters even more as AI accelerates individual parts of software development. Hetté describes a project where developers used agents to move rapidly through tickets. From a productivity perspective, it looked impressive. Code was being produced quickly, and boxes were being checked.

But when the results reached UAT, the output was not aligned with what was actually needed. Some of it was effectively unusable.

The AI had accelerated execution without repairing the underlying handoff.

Developer Systems Thinking Requires Collaborative Ownership

Seeing a broken system is one thing. Raising the issue inside an organization is another.

Hetté acknowledges that developers do not always feel comfortable telling leadership that a process is broken. Even when managers explicitly invite candid feedback, employees may remain quiet.

Her recommendation is to frame the conversation around improving the system rather than blaming an individual.

The objective is not to prove that leadership, product, or another developer made a mistake. The objective is to make what Hetté calls the organizational "machine" work as effectively as possible.

That distinction changes the conversation. Instead of saying, "This person is doing this wrong," a developer can identify a disconnect between what the organization intends to accomplish and what its current process produces.

Developers are particularly well suited to these conversations because systems thinking already encourages us to look at relationships rather than isolated components.

Action: Identify one recurring handoff in your current work. Compare what the person on one side believes they are providing with what the person on the other side believes they are receiving. The difference may reveal more than another process meeting ever will.

The goal is not to take control of everyone else's job. It is to recognize that meaningful ownership sometimes requires looking beyond the boundaries of your assigned ticket.

Moving From Code Builder to Problem Solver

The transition Hetté describes does not require developers to stop being technical. Instead, it asks them to redefine what they consider the system.

Early in a career, the system might be a function, service, application, or architecture. As responsibility grows, the system can include requirements, customers, teams, handoffs, leadership decisions, and business strategy.

That broader perspective is where developers can begin creating value that is difficult to measure in lines of code.

The developer who notices that two teams are individually performing well but consistently losing information during a handoff is solving a technical organization problem. The engineer who asks whether a feature actually supports the company strategy is contributing beyond implementation. The technical lead who recognizes that a founder's abstract vision needs another translation step before reaching developers is preventing expensive rework.

Those behaviors represent a career beyond the code without requiring someone to abandon the skills that got them there.

In fact, the engineering mindset may be precisely what makes the transition possible.

Conclusion: Expand the System You Are Building

Developer systems thinking begins when developers stop viewing their responsibility as ending where the code ends.

The lesson from Dana Hetté's experience is not that every developer should become an operator. It is that the skills developers already possess can be applied to larger and more consequential systems.

Organizations have inputs, outputs, dependencies, bottlenecks, interfaces, and failure states just as software does. The hardest problems frequently emerge not inside the individual components, but at their boundaries.

Learning to see those boundaries is a powerful step toward becoming more than the person who implements the solution. It positions a developer to understand which problem should be solved, why it matters, and how the pieces must connect for the solution to actually work.

Stay Connected: Join the Developreneur Community

👉 Subscribe to Building Better Developers for more conversations on momentum, leadership, and growth. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at [email protected] with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development.

Additional Resources

• Building Forward Momentum as a Developer Entrepreneur
• Growth Ceiling Systems: Why You're Not Actually Stuck
• Private AI Systems: Why Smart Developers Build for Themselves First
• Building Better Developers Podcast Videos – With Bonus Content

Software development is changing quickly. AI can generate code, build prototypes, test ideas, and turn concepts into working software faster than most of us would have imagined just a few years ago. That raises an important question for developers: If writing the code is becoming easier, what makes you valuable?

Welcome to Season 29 of the Develpreneur Podcast. This season, we're returning to an idea that has been at the heart of the show from the beginning. Becoming a better developer means developing more than your ability to write code. Our theme for Season 29 is Build a Career Beyond the Code, and throughout the season, we'll explore what that means for developers navigating an industry being reshaped by AI.

More Than a Coder

We've always made a distinction between being a coder and being a developer. A coder can take a requirement and turn it into code, while a developer needs to understand the problem behind that requirement. Developers have to determine whether the proposed solution makes sense, identify potential risks, and ultimately help deliver something useful.

That distinction matters more now than ever. AI is becoming remarkably good at generating code. Give today's tools enough context, and they can produce in hours what might previously have taken a developer days or weeks. That doesn't eliminate the need for developers, but it does change where developers provide value.

The valuable skills increasingly involve asking better questions, understanding the business problem, evaluating possible solutions, communicating with stakeholders, testing assumptions, and recognizing risks. We also have to determine whether what we're building actually solves the original problem. In other words, the future developer isn't simply someone who knows how to code. The future developer knows how to solve problems.

AI Changes the Speed, Not the Responsibility

One of the most exciting things about AI is how quickly we can experiment. An idea that might once have sat in a notebook for years can now become a prototype over a weekend. We can pressure-test concepts, generate alternatives, experiment with architectures, and throw away approaches that don't work without investing months of development effort.

Instead of having one opportunity to build something correctly, we may be able to test dozens of approaches before committing to one. That's an incredible advantage, but it also creates a new problem: just because AI can build something doesn't mean we should build it.

We can generate a prototype overnight and then immediately add another feature. That leads to another feature, another integration, and another idea that suddenly seems easy to implement. Before long, the simple application we originally wanted to build starts looking like we're trying to recreate Salesforce.

Knowing when to stop is becoming an important development skill. So is knowing which features actually matter and whether the application still solves the problem we started with. AI can dramatically increase our development speed, but speed without direction simply allows us to make mistakes faster.

The MVP Is Changing

For years, building a minimum viable product required meaningful investment. Even a relatively simple prototype could require developers, designers, infrastructure, and weeks or months of work. AI has changed that equation.

Today, creating something that looks impressive is relatively easy. You can quickly build an application with polished screens, dashboards, workflows, integrations, and features that would have required a significant development team not long ago. That means the existence of a prototype doesn't tell us nearly as much as it once did.

We have to ask deeper questions about what we've created. Does it actually work? Is the architecture sustainable? Can it handle real users and real data? Is it secure and maintainable? Does anyone actually need it, and does it create real value?

Those are not primarily coding questions. They're development questions, and answering them requires experience, critical thinking, testing, and an understanding of the people and businesses using the software.

Developers Still Need to Think

AI tools are powerful, but they still require direction, context, validation, and oversight. Working with AI agents can sometimes feel a lot like leading a development team. You assign work, review the results, discover something wasn't done the way you expected, refine the requirements, and try again.

The difference is that AI lets you repeat that cycle incredibly quickly. That makes critical thinking even more important. Developers need to learn how to communicate what they want, provide the right context, establish boundaries, review results, test assumptions, and recognize when an answer that looks convincing is actually wrong.

This also changes the questions developers should be asking. Instead of focusing entirely on whether AI can generate a particular application or feature, we need to ask whether we should build it in the first place. Then we need to determine how we'll know whether we built the right thing.

That's where experience becomes leverage.

Your Career Isn't Disappearing. It's Evolving.

There's understandable concern about what AI means for software development careers, particularly for developers entering the industry. Some traditional career paths are already changing. Work that once gave junior developers opportunities to learn may increasingly be handled by AI, teams may become smaller, and individual developers may become capable of producing dramatically more than before.

However, that doesn't mean there is nowhere left to go. The career ladder hasn't disappeared. The angle has changed.

Developers who combine technical knowledge with problem-solving, communication, business understanding, critical thinking, leadership, and testing have an opportunity to become far more capable. AI can amplify those skills by helping developers move from an idea to something they can evaluate much faster.

The goal isn't to compete with AI over who can type code faster. The opportunity is to become the person who understands what needs to be built, why it needs to be built, how to validate it, and how technology can solve the underlying problem.

Building the Skills Beyond the Code

Season 29 brings Develpreneur back to its roots while building on everything we've explored during the last several seasons. We're going to continue talking about AI because it is now part of the development landscape, but AI itself isn't the destination. We're interested in what developers can accomplish with it and which skills become more valuable as these tools improve.

That means exploring technical skills alongside communication, critical thinking, problem-solving, requirements, testing, leadership, business value, decision-making, and project ownership. We'll look at how developers can pressure-test ideas before investing too much time in them, manage AI-generated solutions, and recognize the difference between a flashy prototype and production-ready software.

Quality becomes particularly important when software can be generated so quickly. Producing code faster doesn't automatically mean we're producing better software. Every additional feature introduces complexity, potential failure points, testing requirements, and long-term maintenance. If the foundation isn't solid, AI can help us create a complicated mess much faster than we could have created one ourselves.

That's why testing, validation, and asking the right questions become even more important. Generating more code isn't necessarily progress. Generating the right solution is.

A New Career Path for Developers

The changes we're seeing aren't limited to the tools developers use. They affect how people build careers. Developers need to become comfortable moving between technology, business problems, communication, testing, and decision-making rather than defining themselves by a programming language or framework.

AI may even accelerate that career progression. Developers can experiment with ideas that previously required an entire team. They can work with AI agents in ways that resemble managing junior developers, explore unfamiliar technologies, and rapidly test different approaches to a problem. However, getting the most from those capabilities still requires knowing enough to recognize when something is wrong.

Experience matters because AI doesn't remove responsibility from the developer. If anything, it increases the number of decisions we can make in a shorter period. The better we become at making those decisions, the more valuable the technology becomes.

Welcome to Season 29

We're now well beyond 1,000 episodes of the Develpreneur Podcast, but the mission remains remarkably similar to where we started: helping developers become better developers. The tools have changed, the industry has changed, and AI has accelerated what individuals and teams can accomplish. The path developers take through their careers is changing along with it.

That's exactly why this is the right time to return to the fundamentals. We need to learn how to think through problems, communicate effectively, evaluate what we're building, test our assumptions, and understand the value behind the technology. We also need to learn how to use AI without allowing the speed of AI to replace good judgment.

Season 29 is about taking everything we've learned about software development, careers, business, quality, and AI and asking where developers go from here. The answer isn't to abandon technical skills or stop writing code. It's to recognize that code is only one part of what makes someone a valuable developer.

The developers who thrive in this next stage of our industry will be the ones who can look beyond the code, understand the problem, use the tools available to them, and consistently turn ideas into solutions that provide real value.

Welcome to Season 29 of the Develpreneur Podcast: Build a Career Beyond the Code.

Stay Connected: Join the Developreneur Community

👉 Subscribe to Building Better Developers for more conversations on momentum, leadership, and growth. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at [email protected] with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development.

Additional Resources

• Destined For A Career In Software – A William A. Adams Interview
• Enterprise AI Reality: What Software Teams Are Learning Beyond the Hype
• Developer Career Growth: Breaking Through Stagnation
• Building Better Developers Podcast Videos – With Bonus Content

AI is changing software development faster than most organizations can adjust their processes around it. Developers can generate code, tests, documentation, and prototypes in a fraction of the time those tasks once required. Business owners who have never written software can now describe an idea to an AI tool and have something working surprisingly quickly.

That creates tremendous opportunity, but it also changes where the real value in software development lives.

In the second half of our conversation with Laura Chattington, CEO of Rebalance Network Limited, we explored what happens when producing code is no longer the hardest part of building software. Laura argues that developers still have an important role, but the value they provide is shifting toward understanding the problem, managing risk, assuring quality, and connecting technology to business outcomes.

About Laura Chattington

Laura Chattington is the CEO of Rebalance Network Limited and works with established entrepreneurs and corporate leadership teams to turn AI capabilities into measurable business value.

Laura began her career as a software engineer and has more than 20 years of experience spanning technology, business, communications, and leadership. 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 through Rebalance Network, connect with Laura Chattington on LinkedIn, or follow Laura Chattington on YouTube.

AI Has Lowered the Barrier to Building Software

One of the most significant changes AI has introduced is access. Entrepreneurs and other non-technical users can now experiment with software without immediately hiring a development team. They can create prototypes, automate workflows, connect systems, and build surprisingly capable applications by working directly with AI.

Laura sees this happening regularly with the businesses she works with. The challenge is determining where experimentation should stop and professional software engineering needs to begin.

An AI assistant will enthusiastically keep building when you ask it to. It may make an assumption, misunderstand a requirement, or introduce a problem that isn't immediately obvious. Someone without a technical background may see a working application and reasonably assume everything underneath it is working correctly as well.

A software engineer sees a different set of questions. How is the data protected? What happens when an integration fails? How does the application handle unexpected input? Can it scale? How is it tested? What assumptions did AI make while generating the solution?

That knowledge hasn't suddenly become irrelevant because AI can write code. In many ways, it has become more important.

Bad Scope on Rocket Fuel

Laura offered one of my favorite descriptions of this problem during our conversation: AI can become "bad scope on rocket fuel."

Software teams have always struggled with poorly defined requirements. If the customer and development team don't clearly understand the problem, developers can spend months building something before everyone realizes they were solving the wrong problem.

AI changes the speed of that mistake. A poorly scoped project can now generate a tremendous amount of code and functionality very quickly. That can create the illusion of progress because there is always something new to demonstrate. However, producing more software doesn't necessarily mean you are getting closer to the correct solution.

This is something I've seen repeatedly from the testing and quality side of software development. Speed is valuable only when you maintain confidence in what you're producing. Accelerating implementation while removing validation simply moves problems downstream, where they usually become more expensive to fix.

The goal shouldn't be to slow AI down. It should be to build the right quality gates around that speed.

Quality Assurance Becomes More Important, Not Less

This led to one of the more interesting parts of our conversation. Laura believes AI may change the relationship between software development and quality assurance.

Traditionally, developers have often received most of the attention. Development creates the product, while QA can sometimes be treated as the group that appears later and tells everyone what is wrong with it.

AI disrupts that equation. If AI can generate large amounts of code quickly, simply producing code becomes less of a differentiator. The more important question becomes whether the generated software is correct, reliable, secure, maintainable, and actually solving the intended problem.

As Laura explained, the challenge becomes how we use the intelligence and speed AI provides while retaining high-quality assurance throughout the process.

That doesn't mean developers disappear and testers take over. It means development and quality need to become much more tightly connected. Developers need to understand validation, testers need to become involved earlier, and teams need automated checks and human review positioned throughout the development process.

AI gives us speed. Quality gives us confidence that we're moving quickly in the right direction.

Developers Need to Rethink the Value They Provide

This also raises an uncomfortable question for developers: if AI can write much of the code, what exactly are we being paid to do?

The answer can't simply be typing faster.

Laura argued that software engineers need to think more creatively about the value they bring to the table. Knowing how software works remains important, but understanding what should be built and why becomes increasingly valuable as implementation gets easier.

A developer who understands the business can identify assumptions before they become expensive mistakes. They can recognize when a proposed AI solution creates unnecessary risk, determine where automation makes sense, and help leadership distinguish an impressive demo from something that can safely support the business.

That combination of technical understanding and business communication creates a significant opportunity.

Laura saw a similar pattern early in her own career as a Java developer. Her ability to communicate with the business pulled her toward management and leadership roles. She sees that opportunity emerging again for technical professionals who can bridge the gap between AI capabilities and business outcomes.

What Do You Do With the Time AI Gives Back?

The conversation then returned to a question from the first half of the interview: what happens when AI dramatically reduces the time required to perform work?

Suppose a task that once took a week now takes two hours. Saving that time sounds impressive, but the time savings alone aren't necessarily business value. The real question is what happens to the rest of the week.

Laura believes relatively few businesses have figured this out. Organizations may purchase AI licenses and encourage adoption, but simply giving employees tools doesn't create a strategy. Leadership needs to decide how the capacity created by AI will be used.

If a 100-person organization gives each employee several hours back every week, the combined capacity can be enormous. That time could improve customer service, accelerate product development, generate new revenue, reduce delays, or allow teams to solve problems they previously didn't have time to address.

Without that next step, the company has optimized a task without necessarily transforming the business.

Look for the Biggest Pressure Points

Laura shared an example from working with a leadership team in the urgent healthcare sector. Before spending time with the organization, she could have arrived with a long list of sophisticated ways the company might use AI.

Once she understood how the organization actually worked, however, she discovered something much simpler: people were spending huge amounts of time in meetings.

Some meetings existed because people had missed previous meetings. Others were necessary to communicate information that had already been discussed elsewhere. Executives needed to catch up before attending another meeting, creating another cycle of lost time.

The valuable AI opportunity wasn't necessarily an elaborate new application. It was using AI to capture meetings, summarize information, create executive reports, and prepare people before they entered the next conversation. That kind of workflow could return a substantial amount of time to the organization.

You don't discover opportunities like that by starting with a list of AI tools. You discover them by understanding how people work.

Start With Pain, Then Apply AI

That example reinforces a principle we continue returning to throughout this season of Develpreneur: start with the problem.

Look at where the organization is spending its time. Find the repeated manual work, communication gaps, customer frustrations, delays, and bottlenecks. Determine which problems are actually expensive enough to solve, and then ask where AI can provide leverage.

Laura described two questions she looks at when working with organizations. First, where is the business spending large amounts of time that AI could reduce? Second, how could AI help customers reach their desired outcomes faster?

The second question is particularly important because it moves AI beyond internal productivity. Saving employees five hours is useful. Helping customers achieve an outcome twice as fast may fundamentally change the value of your product or service.

That's where AI starts becoming a business strategy rather than another productivity tool.

Faster Iteration Changes How We Build

AI may also change some of the traditional assumptions around software planning.

Historically, implementation was expensive. Organizations could spend months designing a solution and creating detailed specifications because getting development wrong meant wasting months of work from an expensive development team.

When implementation becomes dramatically faster, the economics change. Laura's approach is to maintain the necessary scope and fundamentals while recognizing that teams can now learn through much faster iteration.

Instead of spending months trying to document every possible detail before anything gets built, teams can create something smaller, validate it, learn from it, and iterate. That doesn't mean abandoning requirements or planning. It means adjusting the amount of upfront work to the cost and speed of implementation.

From a quality perspective, that makes feedback loops critical. Faster iteration only works when teams can quickly determine whether the change actually works. Automated testing, monitoring, user feedback, and clearly defined outcomes become essential parts of an AI-accelerated development process.

The Opportunity Is Bigger Than AI

One of Laura's strongest messages was that many business leaders know AI represents a significant competitive transition, but they still don't know how to convert it into measurable value.

That's an opportunity for developers.

The technical professionals who succeed in this environment won't necessarily be the people who know every new model or AI framework. Those technologies are going to continue changing too quickly. The more durable skill is being able to walk into a business, understand how it operates, identify where technology can create leverage, build the appropriate solution, and establish enough quality assurance to trust the result.

For developers who can also communicate that value to business leaders, the opportunity becomes even larger.

AI can generate code, create tests, and automate processes. What businesses still need are people who understand what should be built, where the risks are, how to know whether it works, and how it creates value.

That may ultimately be one of the biggest changes AI brings to software development. It isn't eliminating the need for good developers. It is forcing us to become much clearer about what being a good developer actually means.

Stay Connected: Join the Developreneur Community

👉 Subscribe to Building Better Developers for more conversations on momentum, leadership, and growth. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at [email protected] with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development.

Additional Resources

• Why AI Readiness Matters More Than AI Adoption
• AI Governance Framework: Why Guardrails Help AI Move Faster
• AI Governance Implementation: Turning AI Policies into Everyday Practice
• Building Better Developers Podcast Videos – With Bonus Content

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 Chattington

Laura 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 AI

One 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 Ball

Laura 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 Divide

Laura 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 Too

This 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 Coding

This 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 AI

The 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.

Stay Connected: Join the Developreneur Community

👉 Subscribe to Building Better Developers for more conversations on momentum, leadership, and growth. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at [email protected] with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development.

Additional Resources

• AI Value Creation: Build Systems Around What Actually Moves the Business
• Why AI Readiness Matters More Than AI Adoption
• AI Workflow Automation: Start With the Problem, Not the AI
• Building Better Developers Podcast Videos – With Bonus Content

AI value creation is not the same as doing more work with AI. That distinction is becoming increasingly important as AI compresses the time required to build, market, analyze, and deliver products. Faster execution can create remarkable leverage, but it can also accelerate activity that was never particularly valuable in the first place. In the second part of Rob Broadhead's conversation with Scott Shagory, that tension becomes a central issue. AI is changing delivery, sales, marketing, engineering, product ownership, and financial planning simultaneously. When everything can move faster, leaders face a more difficult question than "Where can we use AI?" They must determine where value is actually created.

Speed becomes leverage only when the work being accelerated contributes to an outcome that matters.

About Scott Shagory

Scott Shagory is the founder and CEO of the Purple Finch Group, where he helps technology CEOs navigate growth when organizational complexity begins to outpace the clarity that originally drove the business. His work focuses on identifying underlying growth constraints, clarifying where organizations create value, making implicit founder knowledge visible, and helping leaders adapt their strategy and operations as technologies such as AI reshape the business environment. Outside his business work, Shagory is a senior martial arts instructor, an experience that complements his focus on teaching and breaking complex ideas into understandable frameworks.

AI Value Creation Begins With the Value Chain

Shagory points to delivery, sales, and marketing as areas experiencing significant disruption. Roles are morphing and overlapping, and responsibilities that once seemed clear are becoming harder to define. What does it mean to be an engineer when AI can accelerate portions of development? Where does product ownership begin and engineering end? What should sales and marketing teams prioritize when content, research, outreach, and analysis can all be accelerated? These questions become more difficult because organizations are not simply introducing a new tool. They are changing the speed and boundaries of work across multiple functions at once.

The natural response to that pressure is often to do more, but that is precisely where trouble begins. Shagory describes reacting as "the lowest form of action." When organizations feel pressure to keep up, they can launch projects without deciding what trade-offs those projects represent. Every AI initiative consumes something, whether that resource is money, attention, management capacity, employee energy, or opportunity. The systems question is therefore not simply, "Can AI perform this activity?" Instead, leaders need to ask where value is being created and whether accelerating a particular activity strengthens that value.

AI Value Creation Requires Clear Trade-Offs

The early AI rush encouraged experimentation at enormous speed. Teams pushed products, consumed tokens, worked weekends, and pursued the possibilities created by rapidly changing technology. Shagory describes attending conferences where leaders talked about pushing their teams to produce more and "token max," yet he rarely heard them clearly articulate what the company intended to do and, equally important, what it had deliberately decided not to do.

That second decision is where strategy becomes important. AI expands the number of things a company can attempt, but it does not expand attention, capital, leadership capacity, or customer demand at the same rate. Prioritization therefore becomes more important, not less. A company might use AI for generation, categorization, development, customer analysis, or internal automation. Each application may be technically possible, but technical possibility does not mean each one deserves investment.

For every proposed AI initiative, identify the customer or business outcome it should improve and what the organization is willing to deprioritize in exchange.

This approach changes AI adoption from experimentation for its own sake into a portfolio of deliberate business bets. It also forces leaders to acknowledge opportunity cost. A team spending months developing one AI capability is choosing not to direct those same people, resources, and attention somewhere else. Faster development does not eliminate that trade-off.

AI Value Creation Exposes Busy Work

Broadhead raises an organizational problem that predates AI: people can work extremely hard without moving customer or company value forward. Employees may be busy every day, completing tasks and generating output, while producing little that materially changes an outcome. AI makes this problem more visible because it dramatically increases the amount of output a team can generate.

A team can now produce more code, campaigns, prototypes, research, documents, and ideas in less time. Increasing the volume of work, however, does not prove that the organization has become more productive. It may simply mean that the organization is producing waste faster. Shagory therefore returns to what he calls the linchpins of value creation. Leaders need to understand what the organization does better than anyone else, which activities support that advantage, and where customers actually experience meaningful value. Once those elements become visible, teams have a clearer basis for deciding what deserves acceleration.

AI can make an unfocused organization look extraordinarily productive because output can rise long before anyone proves that outcomes have improved.

Without that clarity, teams can work at cross-purposes while every individual appears busy. Engineering can accelerate one direction while product moves toward another, and sales and marketing can generate more activity without improving the customer experience. AI does not automatically align those functions. In fact, increasing their speed can make misalignment more expensive.

From AI Hype to Organizational Discernment

The conversation also identifies an important change in how businesses are approaching AI. The initial phase was characterized by excitement and pressure to move. Organizations feared being left behind, and experimentation itself sometimes became evidence that a company was adapting. Shagory describes the beginning of the year as a period of "token maxing," when businesses were caught up in the excitement, power, and perceived magic of what the technology could accomplish.

He now sees signs of a transition toward greater discernment. Companies are beginning to confront harder questions about what their experiments actually produced, what customers gained, which initiatives deserve continued funding, and where automation may not be the right choice. That transition matters because moving quickly up the wrong ladder still leaves the company in the wrong place. As Shagory observes, "Everyone's chasing or moving up a ladder, but it's not necessarily the right ladder."

AI may allow an organization to unwind certain mistakes faster than before, but speed does not restore the months, attention, money, and employee energy consumed by a poorly chosen initiative. Shagory also points to the human cost of aggressive experimentation, describing teams working seven days a week as they attempted to keep pace with rapid technological change. That effort can eventually create exhaustion and burnout. For Shagory, employees remain an organization's most important resource, which means a strategy that accelerates technology while exhausting the people responsible for directing it is not a sustainable system.

AI Value Creation Must Show Up in the Financial Model

Eventually, the AI system reaches finance. As AI becomes embedded in products and operations, organizations need to understand its costs rather than treating the technology as an experimental budget with unlimited upside. Shagory discusses how companies are beginning to determine how inference costs should be measured. Leaders have to consider whether those costs should be understood at an aggregate level, individual product level, or product-channel level because each approach changes what they can see about the economics of the business.

These questions matter because unpredictable AI costs can undermine budgeting and forecasting. A product that appears attractive when AI usage is inexpensive can become considerably less attractive when inference grows with adoption. Shagory notes that even a substantial variance in inference costs can create serious problems for a company because finance leaders may no longer know how to forecast what a product or project will actually cost.

The underlying financial foundation matters just as much. Shagory describes AI as a spotlight that exposes areas where a business is weak, including fundamentals such as the general ledger and chart of accounts. Poor underlying data does not automatically become useful financial intelligence because an AI layer has been added. The business still needs coherent data if leadership expects technology to provide accurate insight.

The more sophisticated the AI system becomes, the more important seemingly boring foundations become. Coherent financial data, meaningful metrics, and clear cost attribution help leaders distinguish genuine growth from expensive activity.

Build the System Before You Accelerate It

AI creates a genuine opportunity because many established assumptions are being reset. Large companies and small founders alike are reconsidering how products are built, how work is organized, and what customers will pay for. Shagory sees that disruption as a potential leveling field because organizations of different sizes are facing many of the same fundamental questions. The advantage does not automatically belong to the company that uses the most AI. It can belong to the organization that develops greater clarity about where technology creates meaningful leverage.

Companies that understand what Shagory calls their "origin of genius," know where value is created, and can articulate their strategic trade-offs have something meaningful to accelerate. Companies without that clarity may simply move through confusion faster. The practical challenge is to connect the system from end to end: customer value informs strategy, strategy determines priorities, priorities shape AI investments, and those investments produce measurable costs and outcomes that inform the next decision. That creates a business system rather than a disconnected collection of AI experiments.

AI Value Creation Also Requires Leadership Leverage

Shagory's bonus recommendation provides a practical way for founders and executives to apply the same thinking personally: audit their time. He recommends recording where time is actually spent without immediately judging the results. A leader may intend to devote two hours to deep work and discover that the activity repeatedly consumes four or five hours. Another high-priority responsibility may continually receive less attention than expected because the executive is being pulled toward work that is interesting, familiar, or urgent.

The audit matters because Shagory describes a founder as the company's "very first professional investor." Money is not the founder's only investment; time and attention are capital as well. Looking carefully at how those resources are allocated can reveal the same kind of misalignment that an organization may discover when evaluating its AI investments. The question remains consistent: Where is the resource going, and is that where it creates the greatest leverage?

Conclusion: Faster Is Not the Same as Forward

The most important question in AI adoption is becoming less technical. Organizations already know that AI can produce extraordinary amounts of work. The harder challenge is deciding which work deserves to exist. AI value creation begins when leaders identify the few activities that genuinely move the business, make explicit trade-offs around them, measure their economic impact, and use technology to increase leverage where it matters. AI can accelerate the conveyor belt, but leadership still has to decide what belongs on it.

Stay Connected: Join the Developreneur Community

👉 Subscribe to Building Better Developers for more conversations on momentum, leadership, and growth. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at [email protected] with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development.

Additional Resources

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• Elastic IP Creation and Assignment in AWS
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