At Hisco, we had a mantra. How do we grow for free? It was a relentless march to get better at what we already did, so that when volume climbed, we didn’t have to add a full-time employee (FTE) to carry it. Grow the business, hold the headcount. That was the discipline.
As an Employee Stock Ownership Plan (ESOP), the stakes were personal. Every employee owned shares. When we found a way to handle more without adding cost, the share price got better, and everyone in the company felt it. This was never a slogan about cutting people. It was about not automatically bolting on a salesperson or a customer service representative (CSR) every time the business grew.
Here is the hard part. Most of what we did to grow for free was brutally manual. We rebuilt processes by hand. We fought for every point of margin. It was slow, and it was expensive to figure out. Distributors now have AI tools that make growing for free easier than it has ever been. The reason it works has almost nothing to do with the labor line, and that is the part most business cases miss.
Why Labor Savings Dominate the ROI Conversation
Most distributor AI business cases lead with headcount. Hours saved. FTEs avoided. Cost per transaction. There is a good reason for that. Labor is easy to measure. You know what a CSR costs, you know how long a task takes, and you can multiply your way to a savings figure your CFO will accept.
The easy number is the small number. McKinsey made this point directly in an August 2026 analysis. At most companies, even mature ones, Selling, General, and Administrative (SG&A) runs 5% to 12% of revenue. A large cut to that line still cannot explain the returns leading AI adopters are reporting. The labor story is real. It is also nowhere near the whole story. Stop your business case at hours saved and you are measuring the least valuable thing AI does.
The Hidden Cost of Slow and Poor Decisions
Here is what almost nobody puts on a balance sheet. Decisions cost money. Not just the labor to make them, but the outcome when they are made slowly or made wrong.
Think about your own operation. A price held a week too long while a competitor moved. A reorder point that lagged real demand, so you either sat on dead stock or ran out. A quote that sat in a queue while the customer got antsy and called someone else. A credit approval that took three days on a deal that needed an answer in three hours. An exception nobody caught until it became a service failure and a phone call from an angry account.
None of those show up as a line item. The daily grind swallows them, invisible, and they are some of the largest operating costs you carry. McKinsey named decision-making one of the largest and least visible costs in a business, embedded in daily activity rather than captured anywhere you can see it. That is exactly why grow for free was so hard for us. The cost we were fighting sat buried in a thousand small decisions, and we had to dig each one out by hand.
The Distribution Decisions Where the Money Actually Sits
Not every decision matters equally. The money sits in a handful of high-frequency, high-consequence calls that your business makes thousands of times a month. In distribution, the list is short and familiar.
Pricing. The right price on the right order, set in the moment rather than off a stale matrix.
Replenishment. Reordering the right SKU in the right quantity before demand shifts under you.
Inventory positioning. Moving stock to where it will sell before a stockout, not after.
Quote prioritization. Working the quotes most likely to close and worth the most, instead of the ones that happen to be on top of the pile.
Credit. Approving good customers fast enough to keep the deal warm.
Exception handling. Catching the order, the shipment, or the account that is about to go sideways while there is still time to fix it.
Every one of those is a decision, made constantly, where speed and accuracy convert straight into margin. That is where the money lives. McKinsey advises companies to start where decisions are both expensive to make and economically important, and points to demand sensing as a natural first move for a distributor. Any operator already knows this in their gut.
How AI Changes Speed, Consistency, and Decision Quality
AI moves three levers at once on these decisions, and they compound.
Speed. McKinsey estimates AI can pull decision timelines from weeks or months down to seconds and cut the cost of deciding by more than 90%. A pricing or replenishment call that used to wait for a weekly cycle can now happen many times a day.
Consistency. Your best decisions stop depending on which person happens to be at the desk. The judgment gets applied the same way every time, at scale, without the good day or bad day variance that comes with any human queue.
Quality. AI can weigh more signals than a person can hold in their head, catching patterns in demand, inventory, and account behavior that a busy team would miss.
Here is the part leaders skip, and it decides whether any of this pays off. McKinsey found that companies layering copilots and dashboards onto their existing processes get only modest financial gains. Companies that redesign the workflow around AI see a 20% Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA) uplift and payback in one to two years. Same technology. The difference is whether you changed how the work flows or bolted a tool onto the old way. We learned that the hard way at Hisco long before AI showed up. You cannot grow for free by speeding up a broken process. You rebuild it first.
How to Measure a Decision Dividend
If the value sits in decisions, hours saved is the wrong scoreboard. McKinsey borrows a better one from the factory floor. You judge a plant by what it produces, not by how many machines are running. Apply the same test to AI. The measure is not how many tools you licensed. It is how many of your decisions are now informed, accelerated, or automated by AI. McKinsey calls that decision throughput and argues it is the metric that matters most.
Track it where it counts. What share of your pricing decisions are AI-informed today versus a year ago? How fast does a quote move from request to response now? How quickly does a credit approval clear? How many stockouts did you catch before they happened rather than after? Those numbers tie directly to margin and to revenue, and they tell you far more than a tally of hours saved ever will.
Where Human Approval Still Belongs
Faster and more decisions are not automatically better decisions. A high-speed engine bolted to a bicycle just wrecks faster. The discipline is knowing which decisions AI can make on its own and which still need a human in the loop.
McKinsey is blunt about this, and so am I. You define, clearly, when AI can act on its own, when it escalates to a person, and who owns the result. Skip those rules and two things go wrong. Either leaders refuse to delegate anything and throughput dies, or the system runs unchecked and the first bad call in a high-stakes lane turns into a real problem. Routine reorders and standard pricing are where AI should run. The large, unusual, or relationship-sensitive calls are where your experienced people keep the final say. Drawing that line is a leadership decision, not a technical one, and it belongs to the business, not to IT.
How Decision Automation Changes Management Roles
When AI takes over the routine decisions, the manager’s job changes. It shifts from making every call to setting the rules the system follows, watching the exceptions, and improving the logic over time. Less doing, more governing. That is a genuinely different job, and the people who thrive in it are not always the ones who were fastest at the old one.
This is where the veteran gets more valuable, not less. AI holds the information. Your twenty-year people hold the meaning, the context, the read on why this account or this pattern breaks from what the model sees. The edge comes from putting those together. The company that wins is not the one that bought AI. It is the one that built an organization able to turn better decisions into better results, week after week.
We chased the decision dividend at Hisco before anyone had a name for it, the slow and manual and painful way, and watched it show up in a share price every employee owned. The goal was never to shrink the team. It was to grow without the cost growing alongside it. Distributors now have tools we would have paid anything for back then, and most are pointing them at the wrong target, shaving minutes off tasks instead of sharpening the decisions those tasks feed.
Here is where to start Monday. Pick the one decision your business makes most often that moves margin the most, pricing on a common order type is a good candidate, and time it end to end. That number is your baseline and your first AI use case. Build from there.
What is the most expensive decision your business makes every single day, and how long does it take you to make it?
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