Corner Alliance

Founder

Working with government and non-profit leaders to innovate, communicate, and build better programs, including the Department of Homeland Security, National Institutes of Health, National Institute of Standards and Technology, and Department of Commerce.

OwnerRx.com

Building the always on AI-enabled strategic copilot for SMB owners.

Owner

Entrepreneurly

Board Member

OwnerInstitute

Owner Institute helps owners grow their businesses while achieving independence.

Entrepreneurs'​ Organization

James Lee Witt Associates

Director

SRA Touchstone Consulting Group

Principal

The Touchstone Consulting Group, SRA International, Inc.

extreme blue intern

US Senator Max Baucus

Speechwriter

CEO @ Corner Alliance | Author @ The Small Business Mentor

I spent 17 years and $200K+ hunting through every business course, mastermind, and guru to find the nuggets that actually work.

EOS? Implemented it. Scaling Up? Read it. EO/YPO? Joined both.

The result? 10,000+ hours distilled into OwnerRx - the AI coach that knows every framework, every playbook, every shortcut.

What it took me years and big dollars to learn, OwnerRx delivers in seconds for a fraction of the cost.

I can do amazing things with AI.

My team can't.

And that's the biggest AI problem nobody talks about.

Last week, I built a wild agent orchestration system in 90 minutes.

It took meeting transcripts, classified them by type, generated follow-up emails, made action items, updated project plans, even evaluated team performance. All stored in a searchable database.

Game-changing, right?

Here’s the catch: It works great on my computer. But my team can't use it. My clients can't benefit from it. It doesn’t scale.

This is what I call single-player AI.

You've mastered ChatGPT. Built custom bots. Saved hours every week.

But your team? They’re still asking you to "do that AI thing" for them.

Why the gap?

Because right now, advanced AI means:
- Living on GitHub
- Command line confidence
- Python basics
- Prompt engineering muscles
- Troubleshooting everything

That's fine for solo tinkerers.

It leaves most organizations behind.

Personal AI skills are skyrocketing. But organizational AI adoption? Still mostly talk.

If we want real impact, AI has to be built for teams, not just individuals.

That’s the next frontier.

Alan Pentz

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Owner @ OwnerRx.com | OwnerRx uses battle-tested playbooks from decades of building and coaching SMBs to create AI agents that run your business for you. | Read my newsletter: newsletter.ownerrx.com

$130k in salaries replaced by $100/month in AI agents.

Marketing person ($80k) writing newsletters and LinkedIn posts? Gone.

Assistant ($50k) taking meeting notes and client follow-ups? Gone.

I saved $129,900/year running agent workflows on my laptop with Claude Code.

Then I tried teaching John how to replicate it.

12 hours of hell.

2 hours fighting IT security blocks. 10 hours trying to get his team to use it.

His VA took one look at the terminal and said: "I quit if this is my job now."

Nobody wanted to touch GitHub. The command line terrified them.

John gave up. Went back to paying his $60k assistant.

Here's what I learned: local AI agents are incredible for tech-savvy founders running solo.

But they completely fall apart at company scale.

Just brilliant at processing files on MY laptop that only I can access.

John's still paying $5k/month for work AI could do for $100.

That's the gap OwnerRx is solving: agent systems that work for SMB teams, not just founders who know how to use a terminal.

How many hours did you waste last week on work an AI agent should have done—but your team can't access?

A consulting firm came to me at 15% margins.

Target: 20%.

Five years ago, I had a playbook for this:

Old playbook:

- Better hiring → get 1-2% back (maybe)
- Global talent → squeeze out 3-5% (with cultural pain)
- Cut overhead → another 1-2% (one-time gain)

We'd grind our way to 20% over 18-24 months.

Today? That playbook is obsolete.

Here's what I'm seeing with AI agents:

Get 10% margin expansion by holding back office costs constant and growing topline.

Not cutting. Not offshoring. Not grinding.

Growing revenue without scaling headcount.

The new math:

Professional services firm:

- Revenue: $5M
- Back office: 8 people
- Margin: 15%

Deploy AI agents for:

-Financial Analysis
-Client Reporting
-Marketing
-Proposals and Quoting
-Quality Control
-Meeting analysis and follow ups
-Employee onboarding and HR 
-Client research (80% faster)
-Hiring

Result:

- Same 8 back office people
- Revenue grows to $7M (+40%)
- Margin: 25%

I spent 10 hours a day for 4 days in a row re-building all my AI workflows last week.

3 months ago, I was building OwnerRx, the newsletter, and my Linkedin. I had 4 vendors helping with each aspect. Spend was out of control at $30k a month.

Now I've replaced them all with AI agents. That saves $360k a year.

First came the client tracker agent - every interaction, deadline, and renewal date monitored automatically. 

Then the meeting analyzer that turns Zoom recordings into 5 different artifacts to track and follow up on the meeting content.

The content pipeline was the game-changer. What took 2 hours to write now takes 3 minutes to generate and 15 minutes to polish.

Total investment: 100+ hours of my nights and weekends. 
ROI: 20 hours a week saved and $360k

But there's a downside: the exhaustion is real.

Every improvement reveals 10 more possibilities. Fix the client tracker? Now you see how broken your invoicing is. Automate content? Your distribution system looks ancient.

Last week's 4-day marathon started as "quick workflow optimization." 

The shift from doing work to building tools that do work isn't optional anymore. Teams still manually tracking clients in spreadsheets are losing 15-20 hours weekly that automated teams invest in growth.

My clients now run smoother with zero employees than they would with 5. 

But the pace is picking up. Some people spend all day building tools to make their tools work more effectively.

I'm not sure how many of us can handle it but the productivity increases are real and some people are achieving breakout.

One of biggest PE firms just allocated $1.5B to buy businesses like yours at traditional multiples, deploy AI, and watch margins expand.

Their thesis? You won't adapt fast enough.

I keep having the same conversation. Four owners last month asked me: "Should I sell before AI disrupts my business?"

Meanwhile, I invested in a metal fabrication company. The owner was spending 20-30 hours per week processing purchase orders. Not sustainable when you're trying to grow.

We built an AI system in two weeks. PO processing now takes 5 minutes. He got 25 hours of his week back.

We're closing on our second acquisition next month. Same playbook: build the AI capability once, plug each new company into the same operating system.

That's exactly what the PE firms are doing. At scale.

Here's the data that matters:

95% of AI pilots fail. But the 5% who break through? 91% see revenue boost. 86% improve margins.

This isn't a technology problem. It's a commitment problem. Pilots are experiments nobody owns. The winners pick one process, assign ownership, and measure results until it works.

The rinse and repeat.

The gap between "wanting to use AI" and "actually transforming your business" is where most owners get stuck.

That's exactly the gap PE firms are exploiting.

I'm not going to pretend I'm not doing the same thing PE firms are. The difference: I'm showing you exactly how it works so you can do it yourself.

Build the capability yourself, or become someone else's margin expansion play.

What's the one process eating 20+ hours of your week that you keep saying you'll fix "next quarter"?

$300K.

That's what each failed sales hire costs when you add: salary + lost deals + operational chaos + owner time back in the weeds.

The owner I talked with last week had just gone through his third failed sales hire. 

The pattern?

"We hired for product knowledge... failed to look at character."

But here's the deeper mistake:

They were trying to hire ONE person who could do everything the founder does.

Product expertise + all the relationships + sales nuance + operations + strategy.

That's not a job description. That's a unicorn.

And unicorns don't exist.

Here's what happens when you track your sales process with AI and ask: "What am I actually doing?"

AI breaks it down:

- Marketing: 9 hours/week (lead gen and qualification)
- Admin: 5 hours/week (Writing and sending followups, updating the CRM and dashboards)
- Selling: 7 hours/week (actual conversations with buyers)
- Meetings and Reporting: 5 hours/week (in internal meetings and preparing for those meetings)
-Customer Service: 10 hours/week (Talking recent sales through onboarding issues)

You're not hiring for one role. You're trying to get one person to do a bunch of different stuff.

The AI insight:

Don't hire 1 "unicorn" at $150K who'll fail in 18 months.

Engineer it into 3 achievable roles:

- Sales Rep ($75K) - hire for relationship skills and ability to close
- Operations Coordinator ($55K) - hire for ability to track actions, update the CRM, and do follow ups.
- Customer Service Rep ($60K) - hire for deep product knowledge and ability to answer customer questions 

Then AI generates hiring scorecards for each:

- 5 character traits (integrity, ownership, learning ability, culture fit, resilience)
- 5 technical competencies (specific to THAT role, not "do everything")
- Interview questions revealing both dimensions
- Red flags from your past failed hires

The real problem:

You're not bad at hiring.

You're trying to hire another version of yourself.

The solution:

Stop looking for unicorns.

Start engineering roles AI can help you fill.

Drop your calendar export in ChatGPT and ask:

"What am I actually doing, and should this be 1 role or 3?"

OpenAI just launched ChatGPT Shopping 3 days before Black Friday. It's built for products.

But if you run a professional services firm, the behavior change matters more than the feature.

People are getting trained to ask AI for recommendations instead of searching Google. "What do I need?" "Who can help me?" "Who's best for my situation?"

Right now, that's mostly products. But the habit is forming.

So I ran an experiment.

I asked ChatGPT: "Who are the best fractional CFOs in Austin for manufacturing companies doing $5-10M?" (Congrats to vcfo Austin, you won)

What showed up varied each time I asked with a slightly different query (AI results aren't consistent like Google). But patterns emerged in what seemed to influence recommendations:

- Firms with clear website positioning about who they serve
- Those with substantial Google reviews
- People mentioned in industry publications
- Content that directly answered the type of question I asked

The firms with vague "we serve everyone" messaging? Didn't appear.

Here's my hypothesis for professional services:

Your next client might never Google you. They might ask AI. And AI appears to synthesize everything it finds about you into a recommendation. Or not.

This isn't proven science yet. But it's worth investigating.

Three things worth trying this week:

1. Ask ChatGPT who the best [your profession] in [your city] are for [your ideal client type]. Run it 3-4 times. See patterns in who shows up and what signals seem to matter.

2. Look at your Google reviews. Not just the star rating. The actual words clients use. AI reads those.

3. Check if your website clearly states who you serve and what problems you solve. Specificity seems to beat vagueness.

The shopping feature is for products. The behavior shift affects all of us.

What patterns did you notice when you ran the search for your profession?

We almost lost a $240k renewal because three sentences got buried across eight meetings.

No one was wrong. We just had zero memory for what was said.

Here’s what the transcripts showed after the fact:
– 12 mentions of a competitor we never logged
– 4 risks called out by the client that never became tasks
– 2 promises made on calls that never hit the project board

That’s when I stopped treating meetings like conversations and started treating them like data.

Now, every call is transcribed, auto-tagged (sales/client/internal), and rolled up into questions founders actually ask:
– What objections are killing deals this month?
– Which clients are quietly at risk?
– Where are projects stalling across teams?

After we turned this on, we recovered $300k in renewals and cut 10 hours of “sync” time per week.

I’m building this into OwnerRx because I don’t want another “we should have seen this coming” post‑mortem.

What did your team miss last week that cost you real money?

"Should we start Google Ads?"

This is always the refuge of the desperate.

Last month, a client called panicking about not having enough leads. After a few questions, it turned out she had a 20,000-person email list.

She was emailing them once a year. Once. With no clear call-to-action.

That same week, another client needed more leads for her design business. Her retail location had hundreds of walk-ins per month, but zero signage about her design service—which has twice the margins of retail and solved the exact problem customers came to the store for.

In every one of these conversations, I'm essentially saying: "You're stepping over gold to dig for lead."

After 200+ consulting sessions, I've noticed three types of blindness in SMB founders:

Asset blindness: You don't see what you already have. Email lists gathering dust. Past clients not contacted in 18 months.

Proximity blindness: You're too close to see adjacent opportunities. The retail client thought her retail and service business were separate entities.

Assumption blindness: You've decided something won't work without testing. "We don't want to bother people." "Email doesn't work in our industry."

Your next $50K-$200K is probably hiding in plain sight.

Here's what to do: Block 30 minutes this week. Pull up ChatGPT and have it interview you. It can ask you to list every customer touchpoint you have. Every product or service you offer. Your typical customer profile.

Then ask it to help you brainstorm: Where am I already reaching people who could buy more, buy again, or refer others?

Or keep spending $5K/month on ads while ignoring the gold pile in your office.

What opportunity are you currently stepping over in your business?

Last month I had the same conversation with four different owners. All professional services - consulting, marketing, accounting. All asking the same question: "Should I sell before AI disrupts my business?"

Not one of them is failing. They're all profitable. But they see what's coming.

I asked each of them the same questions: 

-Are you willing to learn this yourself - not delegate it? 
-Can you force organizational change even when your team resists? 
-Will you commit 2-3 years to rebuilding how work gets done?

One went quiet. Then honest: "I'm 5 years from retirement. I don't want to spend them becoming a technologist."

That's not weakness. That's clarity.

Here's what's interesting: these are all knowledge work businesses. The consulting firm owner is more worried than the metal fabrication company I invested in. 

Professional services feels the threat more acutely because their product IS knowledge work - exactly what AI does well.

Here's what I'm seeing: Owners who built great businesses over 20 years are struggling because they can't make themselves learn new systems. 

PE-backed competitors are acquiring AI capability through rollups. Multiples that look reasonable today won't in 18 months.

So I've started having a different conversation. Three paths:

BUILD: Learn deeply, force change, create proprietary AI workflows that competitors can't copy. 2-3 year commitment minimum.

PARTNER: Bring in PE or an operator who already has AI capability. Trade some equity for transformation capacity.

EXIT: Sell while you can get 5-8x EBITDA instead of waiting until AI-enabled competitors compress your margins to 2-3x.

No wrong answer. The only wrong choice is "wait and see."

This isn't pessimism. It's strategic honesty.

Do you have an AI strategy yet ?

For the last year, I've been watching a founder I coach hold onto a toxic performer. 

He wasn't great to work with and grumbled about how he could run the business better. 

So fire him? Problem was he was bringing in $1m in revenue.

But he cost the company too. They lost 2 good employees because of Mr. Toxic.

The founder didn't want to pull the trigger and risk the revenue.

So he waited. And waited. His behavior would improve for a few months, then get worse again.

Until he started actively undermining the owner with other employees.

That finally made it unbearable.

He got fired and guess what happened?

They didn't lose any revenue. In fact, they just kept growing.

The real cost of KEEPING him? Two good people gone. $50k in recruiting and rehiring costs. And 3-4 years of stunted growth because nobody wanted to work on his team.

the founder thought he was making a business decision by keeping the million-dollar producer.

He was actually making an emotional decision driven by fear.

What I learned: Track your performance conversations.

If you're using AI to transcribe meetings, run those transcripts through an LLM to track relationship health and accountability patterns.

It will highlight the problems earlier—before you lose good people, before someone poisons the culture, before you waste years tolerating what you know needs to change.

My founder waited 3 years and racked up $50k plus in costs.

AI would have shown me the pattern in month 3.

What high performer are you tolerating because you're afraid of the revenue loss?

Talked with an owner last week who runs a $10M compliance consulting firm for large construction projects.

$3M EBITDA. Niche expertise. Regulatory moat.

But AI is coming for the core deliverable. The environmental studies his team spends weeks researching and writing? AI can now draft those in hours. Not perfectly—but good enough to commoditize the work.

His question: When do I sell?

He's the sixth owner in two months who's asked me some version of this.

The trap: Actual disruption might be 3-5 years out. But buyers see the writing on the wall earlier. By the time your industry is visibly changing, your multiple has already dropped.

You're not racing disruption. You're racing the perception of disruption.

His business trades at 5-6x EBITDA today. If buyers start pricing in AI risk, that becomes 3-4x. That's a $6M swing on the same business.

My advice to most owners: Go BE the AI change. Use AI to produce studies faster and cheaper. Become the consolidator, not the consolidated.

But that's not everyone's game. Some owners are 5 years from retirement. Some don't want to bet a 20-year business on a tech transition.

Both paths are valid. What's not valid is deciding without understanding what AI can actually do today.

I've watched owners dismiss AI while competitors train it on exactly their workflows.

I've also watched owners panic-sell businesses with 10+ years of runway.

The only way to know which camp you're in? Get your hands dirty. Not reading headlines. Actually testing AI against your deliverables.

Then you can decide instead of guess.

Are you building toward disruption or planning your exit?

How to win as a business onwer:

Just focus on the most important thing.

I know owners who spend 10 hours getting the best pricing on a service or product that makes up 5% of their cost.

You know what they’re not doing during that 10 hours? 

Selling a service or product that’s worth 100x that cost savings to them.

I see owners who read a book or hear a podcast about some new marketing technique then try to pivot their whole company to it.

What aren’t they doing? Doubling down on a tactic that is already bringing in leads.

Stop what you are doing and think about this one question: what single thing will guarantee that I won’t reach my goal? 

Then go solve that thing. Rinse and repeat and ignore everything else.

24,000 hours.

That's how much client interaction one consulting company records per year.

Most would say: "That's expensive storage."

They're thinking: "This is our competitive moat."

Here's what they discovered:

AI analyzed those recordings and found something competitors can never replicate:

Their methods deliver outcomes 1.5 standard deviations better than industry average.

Not "we think we're better."

Data-proven superiority from analyzing thousands of real sessions.

Now they can:

- Prove results to justify premium pricing
- Train new staff 3x faster using what actually works
- Continuously improve by spotting patterns in top performer sessions
- Show clients exactly why their approach works

*his is happening across industries:

A coaching business recording sales calls:

- AI identified: Top closer uses these 3 questions, bottom performers don't
- Automated quality coaching for entire sales team
- Conversion rate: 30% → 40% in 90 days

The dark data problem:

Right now, every meeting in your company contains:

- What actually works (buried in someone's memory and an unprocessed transcript)
- What clients really care about (mentioned once, forgotten)
- Patterns that predict success/failure (invisible without AI)
- Your team's best moves (can't scale what you can't see)

It evaporates the moment the meeting ends.

What AI can do with recordings:

1. Pattern recognition: What do top performers do that others don't?
2. Outcome correlation: Which approaches actually drive results?
3. Training acceleration: New hires learn from 100s of real scenarios
4. Competitive proof: Data-backed claims vs. competitor marketing fluff
5. Continuous improvement: Spot quality drift before it costs you clients

The 10x value play:

Companies recording systematically are building:

- Proprietary methodology (competitors can't copy)
- Pricing power (proven better outcomes)
- Faster onboarding (new hires effective in weeks vs. months)
- Quality consistency (AI catches drift)
- Category leadership (your data becomes industry benchmark)

First-movers are building 2-3 year leads.

The objections I hear:

"Storage is expensive" → Storage is $50/month. Lost knowledge costs $50K+
"No one will review it" → AI reviews it. Humans get the insights.
"Compliance nightmare" → SOC 2 + consent frameworks exist. Not harder than current QA.
"Clients won't consent" → Position as "quality improvement" + show benefit. 95%+ consent.

Most SMB owners think:
"We're too small for this."

Reality:
You're the perfect size. Big enough to have volume. Small enough to move fast.

The companies doing this wrong:

Recording but not analyzing. Terabytes of dark data, zero insights.

The companies doing this right:

Every recording becomes training data. AI finds patterns. Insights improve delivery. Better results justify premium pricing. Category domination.

In 2016, AlphaGo made a move that looked insane.

Commentators called it a mistake.
The world champion froze.
Go experts scratched their heads.

Fifteen moves later, everyone could see it—AlphaGo had just won the match because of that "mistake."

AI found a strategy no human had discovered in thousands of years.

That was AlphaGo's "Move 37."
And business is about to have its own Move 37 moment.

Here's what keeps me up at night:

AI agent systems are already plugging into:
- Meeting transcripts
- Customer calls
- Sales data
- Market trends
- Financials
- Team chats

Soon, these systems will see patterns we can't.
They'll start making Move 37-type recommendations:

"Fire your best salesperson."
"Double prices on your worst product."
"Go all-in on your smallest market."

They'll sound nuts.
They'll cut against all your experience.
You'll want to ignore them.

But they'll be right.

People say, "AI helps humans, but it can't replace judgment."
I think that's only true for another couple of years.

After that?
AI will spot winning plays no human considered. The logic will be invisible to us but the wins will be real.

The hard part won't be building the tech. It will be building your nerve.

Are you willing to trust the data when the advice feels wrong?
Will you act on the insight before you understand it?

Because your competitors will.
And while you're catching up to their "crazy" strategy, they'll be winning.

This isn't sci-fi.
It's coming. I'd guess within 5 years.

The businesses that start building AI orchestration now—capturing data, building trust in their systems, experimenting—will be ready for their Move 37 moment.

Those that wait will spend years trying to catch up.

Are you preparing now to act on the insights you can't explain?

Do you really want to share good content during Thanksgiving week?

I've got some gems saved up but kind of feel like everyone's still talking to their family at this point.

Maybe by Friday they are back on linkedin trying to do anything but talk with their family?

Every week, your team sits through dozens of meetings.

Sales calls. Strategy sessions. Project updates. Client check-ins.

That’s millions of dollars in insights, just floating by.

And most of it vanishes into thin air.

Here’s the usual routine:

Take some notes (maybe)
Fire off a follow-up email (if you remember)
Add a few action items (when someone nudges you)
The rest? Forgotten.

All those patterns, breakthroughs, and warnings? Gone.

Last week, I showed a CEO the meeting intelligence system I’m building.

His eyes got wide: “Wait, you’re telling me I can just ask, ‘What happened in my company this week?’ and actually get an answer?”

Yep. Here’s how it works—in plain English:

Every meeting gets transcribed automatically.
AI sorts them by type—sales, client, internal, leadership.
Specialized agents break it all down:
- Who talked about blockers in projects?
- Which clients mentioned a competitor?
- Where are the risks in the pipeline?
- What decisions were actually made?

All those insights get stored in a way that’s actually searchable. Hooked up to your CRM, project management, financials, everything.

Which means you can ask questions like:
- “What are the top objections in sales calls this month?”
- “Where are projects getting stuck across the company?”
- “Which clients are at risk of churn?”

But here’s where it gets wild:

With enough data, AI will start making calls you’d never think of.
Recommendations that sound backwards.
Strategies that don’t match your gut.
And sometimes, they’ll be right—because the patterns are bigger than any one person can hold in their head.

This isn’t a five-year maybe.

It’s here now.

OwnerRx is building it.
Microsoft and Google are racing toward it.
The only question is—will you be ready?

The companies that start capturing and processing meeting data now will be miles ahead when this shift goes mainstream.

The ones who wait? They’ll be left in the dark, while everyone else gets answers on demand.

Your meetings are happening either way.

Are you learning from them?

Or are they just... dead data?

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