This month marks one year since I launched Noel Consulting Solutions.
When I started the business in August 2025, I expected to offer freelance writing, editing, and fractional content leadership. I still do that work, and I still love it. But as I talked with clients and business leaders, the conversations kept drifting toward something else.
“What do you think about AI?”
“Have you used AI?”
“How could AI help us?”
At first, those questions mostly came from marketing and editorial teams. Then I started hearing them from newspaper publishers, advertising sales teams, school administrators, and leaders in industries outside of the digital world.
Each organization had different needs and problems, but nearly everyone was wrestling with the same question: What can AI do for us?
Following that question changed the direction of my business. Over the past year, I’ve led AI workshops, helped teams build AI literacy, developed custom systems, written plenty of proposals, and gotten a few things wrong. Working with real teams trying to answer these questions, I’ve learned a lot about how AI adoption actually works.
Here are five lessons that stand out from the past 12 months.
1. Start with the work, not the technology
The best AI opportunities rarely begin with a particular tool. They begin with someone describing a frustrating part of their day.
“We don’t have enough time to practice sales calls.”
“Our information is spread across too many files.”
“I can’t keep up with my inbox.”
One of my most rewarding projects this year was SAL-E, an AI roleplay and coaching system for an advertising sales team. The idea emerged organically during our AI workshops and conversations about their work. The team needed more opportunities to practice sales calls, but roleplaying with coworkers took time and could feel awkward.
We didn’t start by asking, “What can we build with AI?” We started with the bottleneck. That gave us a much better question: “Could AI give this team a realistic way to practice whenever they need it?”
This is where AI adoption begins. Not with the latest technology, but with the work people are already doing.
2. The first request is often a symptom
Nearly every business leader I meet tells me they receive too many emails. But “too many emails” is rarely the entire problem.
The real problem might be an urgent message getting buried, a leader spending hours sorting operational noise, or a team lacking a shared view of what needs attention.
Learning to dig a little deeper is crucial. Saving someone a few minutes of scrolling may not justify a major investment. Preventing important information from falling through the cracks, or helping a senior leader refocus their time on high-level work instead of operational details, might be.
Before you build anything, you have to keep asking questions until you understand the core friction causing the complaint. The most valuable AI opportunity is often one or two layers below the first thing someone asks for.
3. The best solutions combine AI knowledge with domain expertise
I understood what AI could bring to SAL-E. The client understood what a realistic advertising sales call sounded like, which objections their team encountered, and what useful coaching feedback should include.
Neither side had the full answer alone.
We combined their knowledge of local advertising and sales training with my knowledge of AI tools and system design. After using the first version, they returned and asked me to build two additional practice modes. The best version of the tool came from us working together, not me dictating how they should use new technology.
This is why I don’t believe every organization needs to become an AI company. The people closest to the work already possess the most important ingredient for AI adoption: a deep understanding of their customers, processes, and problems. My role is to help translate that domain expertise into a tool or system the technology can support.
4. Adoption moves at the speed of employee trust
The other question I heard repeatedly this year was more personal:
“Is AI going to take my job?”
I heard it from writers and editors, but also from owners and CFOs. In my experience, AI often feels most powerful and threatening to people who have used it the least. When your understanding comes primarily from headlines and speculation, it is easy to imagine an all-knowing machine capable of replacing entire professions overnight.
Hands-on experience makes AI more concrete. People begin to see what it does well, where it struggles, and how much human context and judgment it still requires.
You cannot impose a tool on a team and call that adoption. People need opportunities to test it, question it, influence how it is used, and understand how it supports their work. AI literacy isn’t just technical training. It’s change management. It’s how organizations replace anxiety with agency. Part of my job that I love is pulling back the curtain so people can see how AI might empower them, not replace them.
5. The most advanced solution isn’t always the right one
One of my most important lessons of the year came from a rejected proposal.
I met with a prospect who described a particular problem they wanted to solve in one of their systems. I told them AI could absolutely do that. I then designed something close to a perfect-state AI ecosystem with data management, automated updates, urgent alerts, and a dashboard for senior leaders. It was technically feasible. It was also too expensive, too complex, and far beyond what the client had asked me to solve.
I let what AI could do distract me from what the client actually needed.
The size and shape of the solution must match the size and shape of the problem. It must also fit the organization’s budget, capacity, and readiness for change. Teams are already busy. A system meant to make work easier cannot require them to reinvent their entire operation just to use it.
I learned that sometimes the most valuable solution is the simplest one. The best approach is to solve one meaningful problem, learn from how people use the system, and expand when the results justify it.
Building bridges
A year ago, I didn’t expect Noel Consulting Solutions to become an AI adoption consultancy. I discovered what my business really was by listening to what people kept asking me.
That process also helped me understand the type of practice I want to build.
I want to be the bridge between non-technical teams with real workplace problems and the new possibilities created by AI. I want to help people feel capable and empowered using these tools instead of intimidated by them. And I want Noel Consulting Solutions to become the first call someone makes when they find themselves wondering, “How can we use AI in our workplace?”
That is what I’m building toward in year two.
And if the first year taught me anything, it’s that the answer starts by listening.