Adair & Co

This is what modern AI consulting work actually looks like.

Today, I found myself using multiple AI tools across several real business workflows:

Video editing.

Client reporting.

Research.

Content development.

Internal process documentation.

And building a custom AI knowledge base to help a client’s team take, process, direct, and escalate calls more confidently from day one.

That’s where AI becomes practical for small businesses. It’s not about replacing people. It’s about giving teams better tools, reducing training time, improving consistency, and creating better customer experiences.

AI is moving from “interesting technology” to everyday business infrastructure.

And for businesses willing to rethink how work gets done, that’s a very exciting thing. 🚀

Can we talk about the fake little pleasantry we keep stapling to the front of emails and messages?

“Hope you are doing well.”

I would like to formally nominate it for retirement. Possibly by firing squad. 🤠

Too much? Maybe. But hear me out.

Over the last few months, that phrase has started to rub me the wrong way. Seriously. Not because I’m against manners. I was raised right, y’all. But because most of the time, “hope you are doing well” does not actually mean “I hope you are doing well.”

It means, “I am performing concern before getting to the point.”

And listen, I’m not mad about getting to the point. 🫵 I love getting to the point. What bothers me is the costume. The little lace doily of fake warmth draped over a cold ask.

Because when someone actually wants to know how you are, they ask. They leave room for a real answer. They do not toss a pre-packaged pleasantry over the fence and keep walking. 🥀

Maybe this is a small thing, but small things are where authenticity either shows up or gets exposed.

So here’s my proposal: ask if you want to know. Skip it if you don’t. Both are more honest than pretending.

So, death to “hope you are doing well.”

Is it just me, or are we done pretending this phrase still has a pulse? 😂

Small and mid-sized regulated businesses are in a strange AI gap right now.

They handle sensitive data like PII, ePHI, or PCI-subject information, so they could absolutely benefit from AI. But many of the safest options are still built, priced, or licensed for much larger organizations.

ChatGPT and Claude both have more isolated business and enterprise options, and I expect the SMB side of this to change quickly. But today, minimum license counts, budget, and configuration requirements can still be real barriers.

For HIPAA specifically, I’m following tools like Hathr and others pretty closely for my consulting clients. 🔐

For now, my advice for businesses with these constraints is simple:

Stay tuned, but don’t sit still.

Start using AI where the data is not sensitive. The “unsexy” stuff is where a lot of the time savings live anyway.

Things like:

📚 Institutional knowledge and internal procedures
📝 Drafting and refining policies
📌 Meeting notes and SOPs
📬 Email and calendar management
🤝 Lead or prospective client follow up
⏰ Follow up reminders and automation
💬 Client communication templates
🎓 Training materials
🗂️ Project organization

That may not sound as exciting as feeding an AI model all your regulated data, but it can still save hours every week. More importantly, it helps build the habits, workflows, and internal confidence businesses will need as privacy-safe AI options become more accessible.

AI strategy does not have to start with your most sensitive data.

In many cases, it probably shouldn’t. 💡

Kill switch, anyone… 👀

This is exactly why AI safeguards matter.

Not long ago, a very smart friend gave me the honor of reviewing an AI toolset he was considering, and one of the things I appreciated most was how much thought had gone into situations like this.

Not the obvious use cases.

The unanticipated ones.

Because sometimes the biggest risk is not “AI gone rogue.” It’s a support workflow, permission gap, or helpful automation being used in a way no one expected.

AI can do incredible things for business.

But speed without safeguards can create problems just as quickly.

Build the tool. Use the AI. Automate the workflow.

But please, build in the kill switch. 🔐

Meta patches bug used to hijack popular Instagram accounts | LinkedIn The resolution comes after hackers tricked Meta's AI-powered chatbot into granting access to several high-profile accounts.

Follow the white rabbit...🐰

With the dismal decline of good new movie releases, I find myself rewatching old favorites instead.

Most recently, one of my all-time favs: The Matrix.

And this line hit a little differently this time:

“We marveled at our own magnificence as we gave birth to AI…”

Now, I’m not taking it literally, but it is funny how prescient some movies can be.

Some days I do wonder if we’re headed for a mashup of The Matrix, Idiocracy, and Terminator. 🤖

Did I leave any good ones out?

My biggest AI class takeaway this week: don’t just learn the prompts, keep up with the features. 🤠💻

Way back in the ancient days of Microsoft Windows, back when dinosaurs roamed the server room 🦖 and “have you tried rebooting?” was both a troubleshooting strategy and a spiritual practice, I learned by digging.

I poked through files, registry settings, temp caches, application instruction files, and all the weird little corners where Windows hid its secrets like a raccoon with a stolen USB drive. 🦝

And because I did that, I didn’t just learn how to use Windows.

I learned how it worked. 🧠

This week reminded me that AI tools deserve that same kind of curiosity. 🔎

It’s easy to get comfortable using ChatGPT, Claude, and other tools the way we already know how. But meanwhile, they’re quietly rolling out features like skills, projects, artifacts, custom instructions, memory, connectors, and who-knows-what-else while we’re over here treating them like slightly fancier search bars. 🤖

That’s how you end up with an AI version of a PICNIC error:

Problem In Chair, Not In Computer. 🪑

The lesson?

AI fluency isn’t just about writing better prompts. ✍️

It’s about staying curious enough to look under the hood, learn the new capabilities, and notice when the tool has grown past your current habits. 🛠️

Because in this era, the people who get the most leverage from AI won’t just be the ones asking better questions.

They’ll be the ones who keep exploring the machinery underneath, before they accidentally spend six months doing something manually that a new feature could have handled in six seconds. ⚡

And yes… guilty as charged. 🤠

I was actually supposed to become a doctor…according to my mother anyway. 😂

Between attending a medical magnet high school (go Northside Steers 📣) and spending most of my childhood fascinated by anatomy, science exhibits, and all things “gross” at the Fort Worth Museum of Science and History, she was convinced medicine was my destiny.

But life had other plans.

Instead, I ended up in technology — and honestly, it turned out to be exactly where I belonged.

I’ve always loved learning, and tech never stops evolving. There’s always something new to build, solve, or figure out. And now AI is changing everything in the best possible way.

Over the last few weeks, I’ve been deep in conversations with some incredibly brilliant people who are already using AI to run massive portions of their businesses, and honestly, it lit a fire under me. Seeing what’s possible made me realize it’s time to level up even more.

I’ve spent years building prompts, reusable AI frameworks, and custom GPTs for consulting clients, but now I’m diving deeper into orchestration, workflows, and AI agents.

So if I’m a little quieter online lately, just know I’m over here drinking from the AI firehose and loving every minute of it. 🚀

So… it seems large companies are making sweeping layoffs while simultaneously rushing to backfill with a wave of poorly defined “AI” roles.

Meanwhile, in SMB land, small teams are quietly becoming powerhouses.

Right now, AI is doing two things at once that seem contradictory, but really aren’t.

On one side, it has become the most powerful cost-cutting lever large companies have ever seen. Entire layers of repeatable, process-driven work are being compressed or eliminated. Some of that is strategic, but a meaningful portion is reactionary. Short-term margin moves dressed up as transformation.

At the same time, those companies are racing to fill vaguely defined “AI roles.”

Head of AI. AI Strategist. Prompt Engineer. Automation Lead.

Titles are moving faster than understanding.

So you end up with this dynamic: organizations laying off people who deeply understand their systems and customers, while hiring for roles they have not clearly scoped or integrated.

That is not transformation. That is turbulence.

Meanwhile, something very different is happening on the SMB side.

AI is quietly giving smaller businesses something they have never had before: leverage.

* A five-person team can now execute like a fifty-person one
* Small operators can produce at enterprise-level quality
* Speed and capability are no longer locked behind headcount

That is a seismic shift.

But here is where large companies are getting it wrong:

They are treating AI like a replacement tool instead of what it actually is, a force multiplier that still requires human direction.

Because AI does not eliminate work. It changes the nature of it.

Someone still has to ask the right questions, design workflows, test outputs, and continuously improve the system.

That is not less work. That is higher-leverage work.

And it is work their existing teams, if retrained, are often best positioned to do.

Instead, many organizations are letting that institutional knowledge walk out the door, then trying to rebuild it under a new “AI” label.

That is an expensive way to learn the same lesson twice.

SMBs do not have the luxury of getting this wrong. They are not asking, “What can we cut?”

They are asking, “What can we now do that we could not before?”

They are embedding AI into workflows, experimenting quickly, and building real advantage, not just org charts with new titles.

Over time, that mindset gap will matter more than the technology itself.

Because the winners will not be the companies that reduced headcount the fastest.

They will be the ones that paired human judgment with machine capability most effectively.

* The ones that retrained instead of replaced
* The ones that defined roles based on real work, not hype
* The ones that treated AI as a system to be managed, not a switch to be flipped

This is not just a workforce shift.

It is a clarity test.

And right now, a lot of companies are moving faster than they understand.