I did not attend the workshop because I needed someone to teach me how to use AI. I attended because I wanted to understand how people with less technical experience were being introduced to it.
What I saw clarified the opportunity in front of me: many people do not need another tool. They need someone who can help them turn AI into something useful in their actual lives.
That realization connected the session to the work I have already done, the business I am building, and the kind of work I want to do next.
The insight was not the template
The session introduced beginners to agent templates they could adapt without starting from a blank screen. That is a sensible way to lower the barrier to entry. I would probably choose a different path. After years of building companies, software, and integrations, I want to understand what is happening underneath the result.
That difference helped me see the audience more clearly. Many capable people are interested in AI but do not know how to connect it to their actual work. They do not need to become programmers. They need help identifying a useful problem, choosing a reasonable starting point, and knowing where human judgment still belongs.
I left thinking less about the template and more about the person who needs help deciding what the agent should do.
Experience changes how I see AI
I have spent much of my career translating between business needs and technical reality. I see AI as more than prompts and new tools. I see workflows, handoffs, scattered information, and hours lost to tasks that could be organized or automated.
I also see the limits. An agent can draft, summarize, research, or prepare a plan. It cannot decide whether the workflow is worth building, understand every consequence, or take responsibility for the outcome. Someone still has to ask good questions, check the result, and make sure the system fits the way people work.
That is where my experience can help. I want to help people find one real problem, build a practical first version, and learn what the system can and cannot do before adding more complexity.
From ideas to useful work
My next step is to turn the automated workflows I have already been developing into clear demonstrations. A potential client should be able to see what a workflow does, what information it uses, where a person remains involved, and how it improves the work.
I also want to test a practical community session where people can bring ordinary problems and explore how AI or automation might fit into their lives. Those problems might involve small-business operations, marketing, personal financial organization, or life planning. The method stays the same: listen first, understand the work, and build only what helps.
A good solution may be modest. It could be better lead follow-up, a system that turns scattered notes into a plan, or an assistant that prepares information for a decision without making the decision itself.
A familiar kind of work
This feels like forward deployed engineering applied beyond traditional software teams. The work begins close to the person using the system. You understand the environment, build something that fits, and stay involved long enough to see whether it helps.
That is familiar territory for me. The difference is that modern tools are becoming accessible to people who would never have built a traditional software system for themselves. My role can be to translate their frustration into a workable process, then help them use it with confidence.
The value is not determined by how impressive the technology sounds. It is determined by whether the person's day becomes easier or more effective.
The opportunity requires action
I left the session hopeful and excited about the future, but enthusiasm is only the beginning. The real work is choosing one idea, building it, testing it with real people, and changing it when the first version is not good enough.
I do not need to pretend that I am starting from zero. I also cannot assume that experience alone is enough. I still have to listen, build, test, and earn trust one result at a time.
That is the opportunity I see now. AI may give me more leverage, but the meaningful part is still human: understanding what matters to someone and helping make it better.
For examples of how I have been exploring this already, see Building an AI-Assisted Carousel Pipeline and An Agentic AI Investigation of Disputed Online Reviews. Those projects taught me that AI becomes more useful when it is placed inside a workflow with clear boundaries, human review, and a result that someone can actually use.
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