---
title: Every company has that one decision they have to get right
description: Operations businesses don't need more talk about outcomes. They need one decision made right every day, and most can't see enough to make it.
image: https://www.ontollo.com/hubfs/shipyard-containers.png
---

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 5 min read

# Every company has that one decision they have to get right

[![Picture of Tiffany To](https://www.ontollo.com/hubfs/tiffany.jpeg) Tiffany To](https://www.ontollo.com/blog/author/tiffany-to) •  October 2, 2026

[AI Strategy](https://www.ontollo.com/blog/tag/ai-strategy)

![Every company has that one decision they have to get right](https://www.ontollo.com/hubfs/shipyard-containers.png)

With companies racking up AI bills for their experiments, the conversation has decidedly shifted to measuring outcomes. Boards ask about them, pilot reviews end with "so what did it actually move?", and enough agentic AI vendors now charge per result that Deloitte published accounting guidance on outcome-based pricing this year.

But "outcomes" is a big word. In an operations business it usually comes down to something much more focused and tied to the bottom line: one decision, made every day, often times many times a day, that they have to get right.

Here's the claim I'd defend. For any business that relies on people and assets for their operations, the most valuable thing AI can do is help make that one decision well, every single day. Most of those businesses can't yet, and the reason isn't the AI. It's that businesses themselves and now their AI agents can't see enough of what's really happening to make the decision with confidence.

In this post, I'll cover:

- What the one decision looks like at a container terminal and at a large contractor
- Why making that decision well usually isn't an AI problem at all
- How to find your own, and what it takes to make it every day

Here's the part AI vendors find hard and businesses find easy: identifying the one decision takes deep knowledge of the individual business and organization. Most operators instinctively know it. But in conversations with AI vendors, they walk in with a list of things they’d like AI to assist with from forecasting and staffing to visibility and building out the reports they wish they had.

After an operator takes the time to explain how their business works and responds to my asking "Why does that matter?" a few times, that list collapses into the one decision everything else depends on. Once you find it, getting that decision right can ripple through the whole operation, because nearly every other improvement turns out to be measured against it.

## Real-world examples of what these decisions look like

We met with a company recently that operates a container terminal. Their entire business lives and dies by one key decision. Every afternoon, it orders the longshore labor it needs.

If the company orders too few hours, containers sit in the yard unmoved. Cargo that hasn't moved can't be invoiced, so the terminal ends up carrying a cost it can't bill. On the opposite end, if they order too many, the union crews get paid whether or not there's work and end up standing around waiting on the next ship. Both mistakes ultimately eat into the company’s margin.

This is the reality for most of the US waterfront. The [Pacific Maritime Association](https://www.pmanet.org/what-we-do/) alone submits daily waterfront labor orders across 29 ports.

While everything else on this company’s list to improve with AI was important, including cool vision models and optimizing trucking schedules, this one labor decision was something they understood would drive the highest immediate impact.

![container-terminal-labor-decision](https://www.ontollo.com/hs-fs/hubfs/container-terminal-labor-decision.png?width=800&height=450&name=container-terminal-labor-decision.png)

The same shape shows up in a completely different industry. A large contractor we’re working with in construction has its own one decision: where to move crews, materials, and money to protect margin on a project. And to make that decision, it first needs to know its exposure on the project right now.

This firm has decades of experience. It should be delivering faster and at a higher margin every year, and it isn't. The reason isn't skill, it's lag. Work happens in the field, a project manager eventually enters it into a system, and leadership eventually sees it. By the time leadership can decide, the moment has passed.

Picture a field team that orders a large load of construction materials and gets it approved. That's a real commitment, and the money is spoken for. But a committed cost doesn't show up on a job cost report until the invoice does weeks later, so HQ is looking at a project that seems on budget when it isn't. Leadership can't reallocate crews or materials against a picture that's behind the work, which means the decision that matters most is being made blind, or not made at all.

![field-to-hq-lag](https://www.ontollo.com/hs-fs/hubfs/field-to-hq-lag.png?width=800&height=450&name=field-to-hq-lag.png)

## Why the one decision isn't really an AI problem

Here's what I keep noticing when operators bring us their use cases: a lot of them aren't really AI use cases. They are decisions nobody could make with confidence before, because the systems underneath were never connected and the workflows between them have always run on human glue.

Then someone asks, "Can we plug AI into this?" and the honest answer is not yet. The problem isn't that AI can't help these businesses, it's that AI needs a picture of reality those businesses don't have. The data for the model to see is disjointed without the context of what’s really happening, so there's high risk and high costs involved for it to make recommendations that are likely outdated and plain wrong.

Once the systems are connected with context, much of what feeds the one decision is deterministic: counting, matching, and reconciling what is actually true across the operation. The AI reasons on top of that layer, inside boundaries the customer sets, and every recommendation carries a trail showing how it got there. At Ontollo, we call that picture a System of Reality.

## Finding the decision that matters most

Narrowing things down to one key decision may not always be straightforward, and for many of you, there could even be multiple decisions that all carry the same weight.

Below are a few steps that you can take to help arrive at the decision to start with:

1. **Start with what’s affecting your margin, not what you think AI can help you with.** Which decision, gotten wrong in either direction, costs you the most?
2. **Ask whether you can make this decision today** **with confidence**, before it's due. Do you have the full picture? Are there any missing pieces of the puzzle?
3. **Then ask yourself how often you need to get this decision right.** Making the right call once is a project. Making the right call every day, every hour, is what a System of Reality should give you, and it changes the conversation from "What happened?"—a backwards looking BI data analytics tool—to "What else could we do?" with a System of Action that agents can work in with your teams to make decisions on the rapid schedule that operations demands.

## Making decisions with confidence

Getting to the one decision that matters most is certainly a challenge. The next step is ensuring you have all the information you need to make an informed decision based on your company’s current reality. This requires three things to come together:

1. Connecting the pieces. The information behind the one decision is almost never in one place. It's spread across the systems a business already runs—schedules, financials, project tools—and in what crews see in the field. Simple integrations and connections aren’t enough, because they often sync and update on schedules and don’t show the current state of reality. At Ontollo, we connect and relate these systems from HQ to the field, so the pieces stop living in separate places.
2. Building the full picture in real time. Once the pieces are connected, the picture updates as the work does. Ontollo sees how things change and update in real time, ensuring every recommendation it makes or action it takes is based on the current reality. Variance shows up when it happens, not in next week's report.
3. Getting to a decision that’s backed by data. The middle context layer that Ontollo overlays onto your systems is what tracks what is actually true in the business. AI is able to reason on top of that layer rather than making guesses. Every recommendation carries a trail showing how it was reached.

For the terminal, it means the afternoon labor order is based on data sources from multiple systems brought together. For the contractor, it means leadership moves crews and money based on the project as it is today, not as it was when the last invoice cleared.

If you're working out what your one decision is, we'd be happy to work through it with you and show what making it well every day could look like for your operation. [Click here to meet with our team](https://www.ontollo.com/demo).

## See what your systems can’t see yet.

Connect the financial picture at HQ to what is actually happening in the field — and give your AI the operational context it needs to be useful.

[Book a demo](https://www.ontollo.com/demo)

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