After 17 years of domain expertise and AI becoming increasingly accessible, I discovered something I hadn’t expected: I could start turning the business problems I understood into solutions I could actually help build.
For most of my career, I have worked on the functional side of business.
But technology has always fascinated me.
I liked building things — an automation that saved time, a better way of doing something repetitive, or simply turning an idea into something that actually worked.
There was one obvious limitation.
I didn’t know how to code.
I could understand a business problem. I could imagine how a process could work better. But when the solution required programming, there was only so far I could go myself.
Then generative AI arrived.
It Started with Small Things
When tools like ChatGPT and Gemini became available, I didn’t start by thinking about AI agents or enterprise transformation.
I started experimenting.
I used them to help me create small macros and Excel automations. Things I would previously have associated with someone who knew programming suddenly became things I could attempt myself.
They weren’t revolutionary solutions.
But something important had changed.
The distance between having an idea and trying to build it had become much smaller.
Instead of stopping at:
“I don’t know how to code this.”
I could start asking:
“How could I make this work?”
AI was becoming more than a tool for getting answers.
It was giving me access to building.
And then, almost accidentally, a much bigger idea appeared.
A Small Feature Triggered a Bigger Question
One day, while exploring an office communication tool, I came across a workflow function.
I wasn’t working on an AI project. I was simply exploring what the feature could do.
But it triggered a thought.
In enterprise environments, people often work across multiple applications — ERP systems, CRM platforms, cloud applications and other business systems.
Getting a simple piece of information can mean knowing which application to open, where to navigate, what screen to use and what information to enter.
I wondered:
Why should users always have to navigate through these systems to find what they need?
What if they could simply ask?
That question became the starting point.
I understood the business problem.
I could imagine the experience I wanted to create.
What I didn’t have were all the technical answers.
Turning an Idea into Something People Could See
This is where AI helped me again — but in a different way.
I used ChatGPT to explore the idea, challenge my thinking, organize it and turn it into structured presentation material.
The idea gradually became something I could explain and communicate.
I presented it to management. The discussion progressed, and eventually I had an opportunity to present it to senior leadership.
The idea gained support.
Looking back, this was an important moment in my journey.
I hadn’t started by learning a technology and then searching for somewhere to use it.
I had started with a business problem and then explored how technology might solve it.
That opportunity eventually led me somewhere I hadn’t expected when I first started experimenting with small automations.
AI agents.
Suddenly, I Was Working with AI Agents
I got an opportunity to participate from the business side in an AI-agent pilot, working alongside technical colleagues.
There was a lot to understand.
How does an agent actually work?
What can it do — and what can’t it do?
What information does it need?
How should instructions be provided?
How does it interact with tools and enterprise applications?
Most importantly:
How do you take something that makes perfect sense as a business process and translate it into something an AI agent can actually execute?
My functional experience didn’t automatically give me those answers.
So I learned.
Interestingly, AI itself became one of the tools that helped me learn AI.
I used tools such as ChatGPT and Gemini to break down unfamiliar technical concepts, understand structures, explore possibilities and understand why something worked — or why it didn’t.
I experimented.
I made mistakes.
I changed things and tried again.
Gradually, concepts that initially looked highly technical started making sense.
Eventually, I was able to build working AI agents.
That changed my perspective.
But what happened next changed it even more.
“Do You Have a Technology Background?”
As the work gained visibility, the story began spreading within the organization.
I suddenly found myself interacting with people across the globe whom I had never worked with before.
People from sales, supply chain, HR and other areas beyond my traditional domain wanted to understand what I had done and how I had approached it.
One question came up repeatedly:
“How did you do this?”
And often:
“Do you have a technology background?”
My answer was simple.
No. My background was functional.
But by then, I had learned something that I believe is becoming increasingly important.
I had started understanding the space between a business problem and an AI solution.
The Missing Middle: Business Problem → AI Solution
Domain professionals understand problems.
Spend enough years working inside a business process and you develop knowledge that is difficult to capture in a process diagram.
You know where people struggle.
You know which steps consume unnecessary effort.
You know the exceptions and workarounds.
You know which information users repeatedly search for.
And you often know exactly what you wish worked differently.
That knowledge is incredibly valuable.
But identifying the problem is only one side of AI transformation.
The next question is:
How do we translate that problem into something AI can actually solve?
That requires another layer of thinking.
What part of the process should AI handle?
What is an AI agent actually capable of doing?
What context and information does it need?
What instructions should it receive?
What tools or actions should it have access to?
Where should human judgment remain?
And how do all those pieces come together into a solution that actually works?
This became the area I increasingly found myself discussing with people.
Not only:
What business problem should we solve?
But:
How could that business problem become an AI solution?
I had begun understanding that bridge because I had experienced both sides of it.
How Domain Expertise and AI Can Work Together
Something else happened during this journey.
For many years, my professional experience had naturally been concentrated around my own domain.
AI began expanding that horizon.
I was now having conversations with people from sales, supply chain, HR and other functions about their processes and transformation opportunities.
The problems were different, but many of the questions required to turn those problems into AI solutions were surprisingly similar.
That’s when I started seeing my years of domain experience differently.
For someone who has spent 10, 15, 20 or more years building expertise, the arrival of AI can raise an uncomfortable question:
Do I now need to start again?
I don’t think we do.
I certainly didn’t.
My domain experience wasn’t something I needed to leave behind to move toward AI.
It was the foundation I was building from.
And that realization eventually became much bigger than my own journey.
The Idea Behind ACCIGEN
As more people asked about what I had done, I started sharing my journey, my approach and the practices that had helped me.
And I kept thinking about how many experienced professionals must be sitting in a similar position.
They understand their businesses.
They understand their processes.
They know the problems worth solving.
They can see AI changing the world around them.
They may even have ideas about what should be built.
But there is often a gap between:
“I understand the problem.”
and
“I understand how this could become an AI solution.”
That is the thinking that eventually led me to create ACCIGEN.
Not to turn every business professional into a software developer.
And not to suggest that domain professionals should replace technology professionals. My own journey benefited enormously from working alongside technical colleagues.
Instead, ACCIGEN is about helping experienced professionals become more capable participants — and increasingly capable builders — in the transformation happening around them.
I see that journey as:
Domain Expertise → Technology Understanding → Solution Building → Transformation Leadership
And importantly, it begins with something you already have.
Your experience.
You Don’t Need to Throw Away What You’ve Spent Years Building
There is enormous attention today on learning AI.
That’s understandable.
But experienced professionals shouldn’t undervalue what they already know while chasing what they don’t.
If you’ve spent years in finance, accounting, sales, HR, supply chain, operations, procurement, project management or another business domain, you possess something AI itself doesn’t automatically give you:
Context earned through experience.
You understand why processes exist.
You understand what happens when they fail.
You understand people, controls, exceptions, dependencies and consequences.
Technology knowledge can be learned.
AI can even help accelerate that learning, as it did for me.
The opportunity is to combine the two.
Your domain experience isn’t what you need to leave behind to enter AI. It may be the most valuable thing you bring with you.
Where We Go from Here
ACCIGEN is just beginning.
And I don’t want this to become a place where I simply talk about AI.
I want us to build along.
I’ll share what I’ve learned — including concepts that initially confused me, approaches that worked, things that didn’t, and frameworks that helped me think differently.
But I also want to make this practical.
We’ll explore real use cases across different business domains — finance, sales, HR, supply chain, operations, project management and beyond — with one important question in mind:
Could you take this idea and apply it to a process in your own world?
Sometimes that might be an AI-agent use case.
Sometimes it could be a simple automation that removes a repetitive task.
And sometimes it might begin with something as small as a carefully structured prompt that turns a tool you already use into something significantly more useful.
The intention isn’t simply to show you a finished solution.
We’ll work through how to think about it:
What is the business problem?
Is AI or automation actually the right solution?
What should the solution do?
What information, instructions, knowledge and tools does it need?
How should we structure it?
How can we build and test it step by step?
In other words, we’ll repeatedly work through the bridge:
Business Problem → AI Solution
We’ll start with accessible things.
Small prompts → Practical productivity improvements → Tool-based automations → Workflows → AI agents → More sophisticated business solutions
And I want to go beyond discussing use cases.
When we identify something useful, we’ll look at the structure needed to build it so that you can understand not just what was created, but how to approach creating something similar yourself.
A use case may originate in one domain but have applications somewhere completely different.
An automation pattern used in finance might inspire something in HR.
A sales use case might reveal an opportunity in supply chain.
Because once you begin understanding how to structure a business problem for AI, you start seeing opportunities differently.
More Than a Website
As ACCIGEN grows, I want this journey to continue across different formats.
The ACCIGEN website will be the home for deeper guides, use cases, frameworks and build-along resources that you can come back to when you need them.
On LinkedIn, I’ll share shorter ideas, practical observations, use cases and lessons that can start conversations among professionals going through similar transformations.
And when something is better demonstrated than described, YouTube can become the place where we actually build it together — step by step.
I don’t want ACCIGEN to become a collection of articles that you read once and forget.
I want it to become somewhere you return when you’re thinking:
“There must be a better way to do this.”
And perhaps you leave with something useful.
An idea.
A prompt.
A framework.
A use case.
An automation you can try.
Or eventually, something you can actually build.
The objective isn’t to become a technologist overnight.
It’s to become capable of looking at a problem you deeply understand and thinking:
“I know how this process works. I understand what needs to change. And now I can start figuring out how to build it.”
That’s the journey that led me here.
And it’s the journey I want ACCIGEN to help others make.
Start with what you already know. Build what you need next.
Domain First. Transformation Next.