Two years ago, almost every conversation I had about “AI strategy” came down to one question: should we let our people use ChatGPT? That question is settled now. The one that’s replaced it is harder, and honestly, it’s the one I find more exciting to sit with a client and work through: which parts of your business should run on AI agents that don’t just answer questions, but actually complete real work end to end — and which parts absolutely shouldn’t.
That distinction — between generative AI and agentic AI — is the one I care most about getting right for the people I work with in 2026.
Generative AI versus agentic AI, the way I explain it to clients
Generative AI produces something — a draft email, a summary, a first-pass answer — and a person decides what happens next. Agentic AI goes further: it takes an action, checks the result, and takes the next action, chaining steps together toward an outcome with very little human hand-holding. A generative tool drafts a reply to a customer. An agentic system reads the inquiry, checks the order record, drafts the reply, sends it, and updates the CRM, start to finish.
That extra independence is exactly why I think agentic AI is both more valuable and more serious to get right. A mediocre draft wastes thirty seconds of someone’s day. A wrong autonomous action can update the wrong record, message the wrong customer, or commit your business to something it shouldn’t have. I don’t say that to scare you away from it — I say it because I want you to go in with your eyes open, the same way I would.
Where I’ve actually seen this pay off
Across the small and mid-sized businesses and healthcare practices I work alongside, one pattern shows up again and again, and it always surprises people: the highest-payback agentic AI work is almost never the customer-facing chatbot everyone assumes it will be. It’s the invisible back office — the parts of the day nobody puts in a strategic plan, but everybody feels.
Lead intake and scheduling
Qualifying an inbound lead, checking availability, booking the appointment — this quietly eats hours of your most senior people’s time every single week in most service businesses. It’s one of the cleanest wins I know of, because it’s high-volume, rules-based, and genuinely easy to measure.
Documentation and follow-up
In the healthcare practices I advise, intake paperwork, visit documentation, and follow-up communication routinely consume 30 to 40 percent of clinical staff time. When an agentic system takes on the administrative shell around a visit — not the clinical judgment inside it, never that — it hands real time back to patient care without touching anything that requires a license to perform. That distinction matters enormously to me.
Back-office reconciliation
Matching invoices to purchase orders, flagging exceptions, chasing down missing documentation — none of it glamorous, all of it quietly consuming real headcount every month.
Notice what’s missing from that list: nothing here replaces judgment, or the relationships you’ve built with your clients, or the work that genuinely requires a human being. That’s not an accident, and it’s not a coincidence either. The AI engagements I’ve seen go wrong are almost always the ones that tried to automate judgment instead of workflow.
The question I ask before we do anything else
“Should we build custom AI or buy something off the shelf?” is the question I hear most often, and I’ll tell you honestly — it’s the wrong first question. The one I actually ask is: where, specifically, are your senior people’s hours leaking? In turnaround work, the first question is always where the cash is leaking. I bring the same discipline to AI, because the honest answer is almost never “we need a chatbot” — it’s always found by looking closely at where the same repetitive, well-defined task is quietly eating disproportionate senior time.
Once we’ve named that, the build-versus-buy decision gets much easier, because now it’s scoped to one real workflow instead of “AI” as some abstract category. And if a vendor can’t tell you how you’ll measure the return within 60 days, I’d gently suggest they’re not proposing a business solution — they’re proposing a science project, on your budget.
Where this work actually lives
My own AI strategy work — the same lens I bring to every engagement at Alton — is delivered through the practice I built specifically for this: Interactive Intel. It’s a small, Miami-based team, and we design, build, and run production AI agents for SMEs and healthcare practices — lead intake, scheduling, documentation, back-office reconciliation — with every engagement led by me personally and scoped around an outcome you can actually measure, not a slide deck I hand off to a junior team.
The entry point is intentionally small, on purpose: a fixed-price AI Opportunity Scan — one workflow, two weeks, the payback math in writing before you commit to anything bigger. And if the math doesn’t work for your business, I will tell you that plainly, because that’s worth more to you than a sale is worth to me. You can read more about how I frame this decision for operators on our AI strategy page, or go straight to Interactive Intel and let’s scope your first workflow together.
