In 2023, I spent an entire day building a suite of custom GPTs for business analysis. They took stakeholder interview transcripts and turned them into structured deliverables: current state, future state, pain points, KPIs, user stories. The whole pipeline. They were some of the first custom GPTs I ever built, and they worked beautifully.
That was almost three years ago. Today, I wouldn't build custom GPTs. I would build a workflow or an app that does the same job, but faster and better.
What I built then was good. Still useful two years ago. Still relevant a year ago. Technology was moving quickly, but the things you built had time to earn their keep. That pace has changed. The shelf life is shorter now, and it keeps getting shorter.
I have watched this happen before. When I was teaching web design, WYSIWYGs (what you see is what you get) were just hitting the market. What you used to hand-code line by line, you could suddenly drag and drop. It opened the door for people who didn't know coding to build their own websites.
Something similar is happening now, except faster. Writing custom logic, building AI pipelines, connecting tools together - things that used to require real coding or complex platforms to wire up - you can now just describe out loud. Tell it what you want. It builds. Adjust as you go. It's referred to as vibe coding. Six or eight months ago it was clunky at best. Today, your imagination is the only thing holding you back.
That Is Not a Problem. That Is the Opportunity.
Most companies treat AI implementation like a software install. Scope it, budget it, roll it out, check the box. Done.
That model worked for CRM systems that looked the same year to year. I know, because I trained teams on a $50 million CRM rollout that barely changed between annual updates. You could train a team in January and it still applied in December.
AI does not work that way.
In the last twelve months, the tools I use daily have changed significantly. Features that did not exist six months ago are now central to how I work. The model I relied on in January got replaced by something better in March. A workflow I built in February had a faster path by April. This is not unusual. This is the pace.
And here is the part most people miss: that pace is not exhausting. It is compounding. Every time the tools improve, the things I can build with them get more powerful. The day I spent building those first custom GPTs taught me patterns I still use. The difference is that now I can apply those patterns in a fraction of the time, to harder problems, with better results.
Why "Set It and Forget It" Leaves Money on the Table
AI is not an IT project you hand to one department. It touches sales, operations, training, customer service, content, scheduling, reporting. Every team that handles information, which is every team, can benefit.
But each team needs someone who understands their specific work AND understands what AI can do right now. Not what it could do theoretically. Not what the vendor promised last quarter. What it can do today, with the tools that exist today.
Consider what happens when nobody is watching. A team sets up an AI workflow in January. It works. They use it every day. Six months later, the same task could be done in half the steps because the underlying model improved. But nobody told them. Nobody checked. They are still running the January version, getting January results, while the tools have moved on.
That is not a technology failure. That is a gap in the implementation model. The "project" ended. The capability did not keep up.
What Ongoing AI Capability Actually Looks Like
The companies that will pull ahead are not the ones with the best initial setup. They are the ones who treat AI as a living capability that someone is actively maintaining.
Regular workflow audits. Someone reviews what the team is using, checks it against current tool capabilities, and flags what can be improved. Monthly during a rollout. Quarterly once things stabilize. The audit does not need to be long. It needs to be consistent.
A practitioner, not a consultant who read the whitepaper. The person training your team needs to be using AI tools themselves, every day. Not reading about them. Using them. They need to know what just shipped, what broke, what got better, and what is coming next. I have built over a hundred custom GPTs and AI workflows across sales, training, operations, and content. I use AI daily because my own work depends on it. That is what makes a practitioner: staying current because the work demands it, not because someone scheduled a quarterly review.
Training that builds ownership, not dependency. The goal is for your team to own it. To spot opportunities themselves. To say "wait, there might be a faster way to do this now" without needing permission or outside help. That only happens when the training builds confidence, not just compliance.
Teach the Pattern, Not Just the Tool
The tools change every few months. That is a fact. But the pattern of finding repetitive work and compressing it with AI does not change at all.
That is the thing worth teaching. Not "here is how to use this specific feature." That will be outdated in a quarter. Instead: here is how to look at your daily work, find the parts that repeat, and ask whether AI can compress them. Here is how to evaluate whether a new tool does the job better than the old one. Here is how to rebuild a workflow when the platform underneath it improves.
A team that understands the pattern does not need someone to come back every time a model updates. They get more self-sufficient over time, not less. That is the compounding effect. That is why this is an opportunity, not a burden.
The companies that treat AI as a one-time project will get one round of efficiency gains and then plateau. The ones that treat it as an ongoing capability will compound those gains quarter over quarter. Same technology. Different results. The difference is not the tools. It is whether someone is paying attention.
If This Sounds Like Your Team
The pattern is the same in every organization. The technology is ready. The people need someone who genuinely uses these tools every day, understands how systems fit together, and can show your team how AI fits into the work they already do. That is what I do. If that is what your organization needs right now, I would love to help.