Optimizing AI in Your Business: An Operator's Guide to Turning Adoption Into Enterprise Value

Almost every business owner is using AI now. Very few can point to where it landed in the P&L. Here is how to close that gap.

By Mitch Felderhoff, Weeks Ahead Ventures, LLC

The adoption argument is over.

The U.S. Chamber of Commerce found that 58% of small businesses now self-identify as generative AI users, up from 40% in 2024 and 23% in 2023. The Chamber Foundation's Main Street AI Monitor, conducted with Ipsos in May 2026, found that half of all small business workers already use AI on the job, whether or not their employer sanctioned it. Only about one in ten said they were ever offered formal training on it.

So the question is not whether your people are using AI. They are, right now, probably on an account you are not paying for. The question is whether any of it is reaching your income statement.

MIT's Project NANDA studied more than 300 enterprise AI deployments in 2025 and reported that roughly 95% of generative AI pilots produced no measurable P&L impact. You can argue with the methodology. I would not argue with the pattern.

I was the fourth generation owner of Muenster Milling Company and served as CEO for six years. We pioneered the business into a leader in the freeze dried pet food space, and in 2021 I led it through a successful transaction to a private equity group. So I have sat on both sides of this: the operator trying to make technology earn its keep, and the seller watching a buyer decide what my process was actually worth. Today, through Weeks Ahead Ventures, I install AI inside operating companies for a living.

Here is what I see over and over. Owners buy motion. They do not buy margin.

What "optimizing AI in your business" actually means

Optimizing AI means embedding it into a specific, repeatable workflow with a number attached to it, and keeping the result after the novelty wears off. Everything else is a demo.

Three tests. Fail any one of them and you are not optimizing, you are playing.

1. Can you name the number it moves? Hours per week, cost per ticket, days sales outstanding, quote to close, return on cash. If you cannot name it, you cannot defend it.

2. Does it run without you? If the value lives in your prompts, you did not build an asset. You built a hobby with a subscription fee.

3. Would a buyer pay for it? More on that below, because for this audience it is the whole game.

   
   

Start with the decision, not the tool

The most common mistake I see is an owner asking "which AI should we buy?" That is the third question, not the first. Start with a decision you make repeatedly and expensively.

At Benchmark Equipment, a heavy equipment business I am involved with, that decision is simple to state and hard to answer: should we buy this machine?

That question used to lean on experience and instinct. So we built a rental return calculator. It takes the purchase price, current market rental rates, return on cash, prevailing interest rates, and where our fleet utilization actually sits, and it tells us whether a specific machine earns its keep. Not a category. That machine.

Notice what that is. It is not a chatbot. It is not a productivity hack. It is capital allocation, which is the highest leverage decision an owner makes, and it now runs on a model instead of a gut feel. AI did not replace the judgment. It made the judgment legible, repeatable, and available to somebody other than the owner.

That last part is where the value compounds.

Build for the process, not the person

Here is where this gets specific to owners thinking about growth, transition, or exit.

Buyers do not pay for you. They pay for what happens after you leave. Owner dependency is the most reliable way to compress your multiple, and AI can quietly make owner dependency worse if you are not careful. If you are the only one who knows how to get a good answer out of the tools, you have concentrated more of the business inside your own head, not less. That is the opposite of what a buyer wants to underwrite.

I learned this from the other side of the table. In diligence, nobody asks how clever you are. They ask what survives you.

The version that creates value looks different. Pricing logic is encoded, not remembered. The buy decision runs on a model anyone can open. The dispatch call is a workflow the team executes. That is transferable earnings, and transferable earnings are what get a premium.

There is a data dimension too. Well structured, well governed data is an asset that shows up in diligence. Messy data is a discount. If AI forces you to finally organize your operational data, that alone may be worth more than the automation you build on top of it.

Ship small, then actually ship

Speed is available now in a way it simply was not three years ago.

At AMA Dumpsters, we had a working dispatch prototype in about a week and fully implemented dispatch software running the business within 90 days. I am not a developer. I direct AI tools rather than write code by hand, and that is now a completely viable path for an operator to get real software into their company.

But the outcome is the part that matters. That system has helped us absorb price compression in our market. When pricing gets squeezed and you cannot raise the top line, your only options are to take the margin hit or take the cost out of the process. We took it out of the process. That is the difference between an AI project and an AI result.

The prototype was the easy part. Getting to full implementation in 90 days took a named constraint, a named owner, and a number we agreed to measure.

A 90 day plan any owner can run

1. Days 1 to 15. List your five most expensive recurring decisions, each in one sentence with a number attached. Pick the one with the shortest path to a measurable result. Not the most exciting. The shortest.

2. Days 16 to 30. Publish an acceptable use policy and a simple data classification rule before anything else. Your team is already using AI. Give them guardrails so their good intentions do not become your data problem. This costs you a morning.

3. Days 31 to 60. Build or buy the smallest thing that addresses it. Put one person's name on it. Baseline the number before you launch, because if you do not, you will never prove the return and you will never get budget for the next one.

4. Days 61 to 90. Measure. Document the workflow so a new hire could run it. Then decide: expand, fix, or kill. Killing it is a legitimate and underused outcome.

5. Ongoing. Train your people. This is the highest ROI line item in the plan and the one owners skip. When only one in ten workers has been trained on a tool half of them already use, the gap is not technology. It is capability.

   
   

Frequently asked questions

What is the best AI tool for a small business? There is no best tool, only the right fit for a defined use case. Define the workflow and the number you are trying to move first, then evaluate two or three options against it. Running multiple tools is fine as long as your governance supports it.

How do I measure AI ROI in my business? Baseline the metric before you deploy, not after. Pick one number tied to cost, speed, or revenue, capture it for 30 days, then compare. If you cannot get a clean baseline, you have picked the wrong first use case.

Does AI adoption increase the value of my business? It can, but only when it is embedded in documented, transferable processes rather than in the owner's head. Buyers pay for repeatable earnings that survive your exit. AI that reduces owner dependency and cleans up your operational data supports your valuation. AI that increases owner dependency quietly works against it.

Should I wait until the technology settles down? No. But do not confuse urgency with recklessness. Move fast on one narrow use case, slow on governance, and never on your data.

The bottom line

AI is not a technology decision anymore. It is an operating discipline. Adoption is table stakes. Impact is the differentiator, and impact comes from starting with an expensive decision, building for the process instead of the person, and measuring the thing you claimed you would move.

If you are growing, that shows up as margin. If you are transitioning, it shows up as multiple.

Working through where AI fits in your operation, or what it does to the value of what you have built? I am happy to be a sounding board. Find us at weeksahead.ai.

About the Business Transitions Summit

The Business Transitions Summit is an event for business owners who are serious about what's next - whether growing, evolving, or exiting. BTS helps attendees identify where they are in their entrepreneurial journey, gain clarity on what’s next, and walk away with the tools and strategies to move forward with purpose.

About Mitch Felderhoff

Mitch Felderhoff is co-founder of Weeks Ahead Ventures, LLC, a holding and investment company whose flagship product is RelayHub, an AI platform for operating businesses. Mitch was the fourth generation owner of Muenster Milling Company, where he spent 17 years and served as CEO for six, pioneering the business into a leader in the freeze dried pet food space across branded, co-packing, and direct to consumer lines before leading it through a successful transaction to a private equity group in 2021. Today, he serves as an operating partner across several companies, including AMA Dumpsters and Benchmark Equipment, where he focuses on AI integration that shows up in the numbers.