The Founder’s Edge Is Speed
How AI lets small companies turn messy operations into working systems before big companies finish the approval process.
One of the best parts of being an entrepreneur is not freedom. That word gets thrown around too loosely.
Entrepreneurship is not exactly freedom when you have customers, employees, vendors, landlords, banks, delivery companies, software platforms, inventory, payroll, and refrigerators that all seem to develop personalities at the worst possible time.
The best part is speed.
After spending years in corporate America, this is probably the thing I appreciate most about being a founder. A large company is like a cruise ship. That is not an insult. Cruise ships are impressive. They are complex, durable, highly coordinated machines with thousands of people and systems working together. They also do not turn quickly.
A startup is more like a speedboat. It is less stable, easier to break, and much less comfortable in rough water. But if you see something ahead, you can turn.
That distinction matters more than ever because of AI.
A few days ago, I was talking to a friend who works at a large company, one I know well from my own corporate years. I asked him how AI was showing up in his day-to-day work. His answer was basically that they had Copilot, but it was restricted and they were not really using it for much.
My first reaction was: I do not miss that.
My second reaction was more charitable: of course that is how it works.
At a Fortune 500 company, the question is not just, “Can this tool make us faster?” The question is, “Can this tool expose customer data, create legal risk, violate internal policies, confuse employees, break a process, embarrass the company, or produce an output that nobody knows how to audit?” Those are real questions. Large companies are slow for reasons that are often rational.
But rational slowness is still slowness.
In corporate America, a lot of time gets spent reducing downside — or, as we used to say, covering your ass. That is not wrong. At scale, avoiding mistakes is part of the job. But it changes the operating culture. The incentive system gradually shifts from “how do we get this done?” to “how do we make sure nobody gets blamed if this goes wrong?”
That is one of the parts I understood, agreed with, and hated all at once.
And it is also the part that makes this AI moment so interesting for founder-led companies. AI does not just make small companies more productive. It reduces the time between noticing a problem and building a system around it.
That is a big deal.
At Meat N’ Bone, the best example right now is merchandising and category management.
This is funny to me because I am a finance guy by background. I understand margin, pricing, inventory, velocity, customer behavior, contribution profit, and the pieces that sit underneath category management. But merchandising itself was not something I was naturally deep in when we started.
It took me close to a year to really get my arms around it.
And that makes sense. Meat N’ Bone is not a simple 10-product company. We have more than 600 SKUs across beef, Wagyu, poultry, seafood, pork, lamb, game, pantry, bundles, DTC, local delivery, nationwide shipping, boutiques, and restaurants. Category management in that environment is not just deciding what to sell. It is deciding what role each product plays in the business.
Some products are traffic drivers. Some are margin drivers. Some are add-ons. Some are discovery products. Some convert well but create fulfillment complexity. Some look good in theory but sit there doing nothing. Some need better product pages. Some need better tags. Some need to be in different collections. Some need pricing work. Some need to be promoted. Some need to be quietly retired.
The problem is not understanding that conceptually. The problem is doing it across hundreds of SKUs while also running the company.
Before AI, the process was heavy. We would pull data from Shopify, Google Analytics, Microsoft Clarity, Omnisend, margin reviews, product pages, collections, and internal reports. Then we would talk to the ops leads, review what was actually happening on the ground, update documents, make recommendations, communicate changes, and follow up.
It was the kind of work that everyone agrees is important but that easily becomes periodic instead of continuous. If we were disciplined, we reviewed it monthly. If the business got busy, it became quarterly. If everything was on fire, which is not exactly rare in a startup — no fire jokes, please — it could drift longer than that.
Over one weekend, I built a merchandising and category management tool that changed the rhythm of the function.
Not a PowerPoint. Not a consulting roadmap. Not a theoretical AI strategy. A working internal tool.
The system reviews SKU scores, product performance, conversion, PDP quality, SEO, tags, collections, pricing, and recommendations. It helps identify which products deserve more attention, which ones need cleanup, where the product page is weak, where tagging is hurting discoverability, where collections need work, and where pricing may not match the role of the SKU.
One of the first useful outputs was around product tags and prioritization. That sounds boring, which is exactly why I like the example. In e-commerce, boring details compound. If the wrong products are surfaced, if the wrong SKUs are prioritized, if tags are inconsistent, or if strong products are buried inside the wrong collection, you leak conversion without seeing one obvious smoking gun.
This is not glamorous AI. It is not a robot making steaks or a chatbot pretending to be a sommelier.
It is more useful than that.
It takes a messy, cross-functional process and turns it into something closer to a live operating system. The difference is not that AI suddenly became a merchant. The difference is that the logic we had in our heads, our docs, our meetings, and our spreadsheets can now be made repeatable.
That is what people miss when they talk about AI only as a productivity tool. Productivity is part of it, but the bigger opportunity is operational cadence.
A company that used to review merchandising monthly can start reviewing it daily. A company that used to depend on a founder remembering to chase a category issue can now have the issue surfaced automatically. A company that could not justify a full-time merchandiser can start building some of that function into its internal systems.
A big reason the build was quick is that we had already done the hard thinking. The process was documented. The PowerPoints, whitepapers, and Excel analyses already existed. AI did not replace the strategy. It turned the strategy into a system.
That does not mean the AI gets to run wild.
At Meat N’ Bone, the current rule is simple: AI suggests, humans approve.
I think that line matters. I do not want an agent changing pricing, rewriting product pages, or modifying Shopify because it produced a confident recommendation on a random Tuesday. Confidence is not the same thing as correctness. Anyone who has used AI seriously knows that.
But I do want the system to say: here are the SKUs that need attention, here is the likely issue, here is the supporting data, and here is the recommended action.
That is already valuable.
Over time, the line may move. If the system recommends the same type of tag fix 50 times, and humans approve it 49 times, maybe that becomes an automated action. If PDP recommendations follow a clear pattern and improve conversion, maybe the agent starts drafting them directly into an approval queue. If pricing suggestions become reliable within certain guardrails, maybe the system is allowed to stage changes for review.
That is where agentic AI gets interesting. The point is not that the AI “learns” in some magical way. The point is that repeated human judgment can become operating logic. Once you can observe the pattern, approve the pattern, and audit the pattern, you can start deciding which parts of the workflow should remain human and which parts can become system-driven.
We are experimenting with tools and frameworks like Claude Code, Replit, GitHub repos, Hermes, LangGraph, and other agentic systems. I am not particularly religious about the tool stack. It will probably change. The important part is the capability curve. A founder can now go from pain point to prototype to internal tool at a speed that would have been very hard to imagine a few years ago.
In a large company, this merchandising system would probably have become a project. There would be stakeholder alignment, IT review, data governance, budget approval, vendor selection, legal review, security review, implementation planning, training, and a pilot. Again, not because the people are dumb. It is because the company is at a different stage of life.
Large companies manage risk. Startups manage survival.
Those are different games.
For a large company, moving too fast can create unacceptable downside. For a startup, moving too slowly can be the unacceptable downside.
I built this tool over the weekend and rolled it out Monday.
That is why I think AI will create a strange advantage for small and mid-sized companies that are willing to use it aggressively but responsibly. The biggest companies will eventually have better tools, more data, larger teams, and deeper budgets. I would not bet against them over a long enough timeline.
But in the short and medium term, I would bet on the companies that can move fastest from insight to implementation.
That is where founder-led businesses have an edge.
We do not need AI to replace the operator. We need AI to remove the drag between the operator seeing something and the business doing something about it.
That is the part that feels new.
And honestly, that is the part that reminds me why I became a founder.
It is not because this is easier. It is not. There are days when corporate America, with its benefits and clean reporting lines and functioning HR departments, looks very appealing. But then I remember the tradeoff.
In a big company, seeing the answer is only the beginning. You still have to get permission to pursue it.
In a founder-led company, seeing the answer can be the beginning of the build.
That speed is fun. It is also dangerous. Speedboats crash. Startups overbuild. Founders chase shiny objects. AI makes it easier to move fast in the wrong direction, too. So the answer is not reckless automation. The answer is controlled aggression.
Use AI to surface the work. Use humans to approve the judgment. Use repeated approvals to build rules. Use rules to automate low-risk actions. Then keep tightening the loop.
That is the playbook, at least for us.
And if I had to guess, that is how a lot of the next generation of founder-led companies will operate. Not with one giant AI transformation initiative, but with dozens of small internal systems built around the specific places where the business is slow, messy, or dependent on tribal knowledge.
Merchandising is one example. Pricing is another. Shipping risk is another. Customer service, procurement, finance, retention, training, and sales management all have versions of the same problem.
Too much data. Too many small decisions. Too much context trapped in people’s heads. Too much work that is important but not always urgent enough to get done on time.
AI is useful because it can turn those weak points into systems.
That is not science fiction. That is not hype. That is not “AI will replace everyone.”
It is more practical than that.
It is a finance guy who spent a year learning merchandising, then used AI to build a tool over a weekend that lets the company manage the function every day instead of whenever we can get around to it.
That may not sound as sexy as the AI headlines.
But in the real world, that is where the money is.
Luis Mata
Luis Mata is the CEO and co-founder of Meat N’ Bone, CFO & Partner of Olos Impact, and partner of Standard & Scale. He writes about food, founders, capital, technology, and the operating systems behind growing companies.