Downside Control

Everyone responsible for AI adoption is searching for the big use cases.

Not the ones that help employees write emails 10% faster. But the ones that transform an important process, create a new revenue stream, or save the company hundreds of thousands of dollars (or euros).

Those opportunities exist. But finding them requires a better definition of what a good AI opportunity looks like.

Because the best opportunities I’ve found didn’t simply have a large upside.

They had a controlled downside above all.

Let’s find out why this mattered so much.

Many promising AI ideas will fail.

That uncertainty can never be fully eliminated before testing.

The goal is therefore not to find projects that are as safe as possible, but to to find projects where failure is survivable and success is relevant.

That is the core idea of an asymmetric bet, also called optionality. Visually, this is how it looks like:

Imagine your AI project is a marble rolling on this curve above. When your initial hypothesis does not work out, the marble will move to the left and create a business loss. But that loss is limited. If it does work out, however, the marble will move to the right and the gains it can generate are literally unlimited. Your upside is open.

This principle matters because higher risk does not automatically produce higher returns. An AI project can be expensive, technically complex, and operationally dangerous while creating almost no business value.

The opportunity must be worth pursuing first.

Then the downside must be controlled.

The downside looks different depending on how AI is used.

To explain downside control in the context of Productivity AI, let me tell you a little story:

For our wedding, we hired a professional photographer.

She took around 5,000 photos of the event. In the end, however, we got about 300.

Getting rid of the 4,700 made the service actually more valuable.

Imagine we had received all 5,000 pictures. That would have transferred the work of screening, selecting, and deleting back to us. More output would have created less value.

Productivity AI creates the same risk inside organizations, and it is happening right now:

When AI makes creation cheap and review expensive, companies generate tons of mediocre work which costs millions and erodes trust between coworkers.

A document may take five minutes to generate and fifty minutes for someone else to verify, correct, and understand. At that point, AI is not removing work. It is creating actually more work downstream.

The controls for productivity AI are therefore editorial and organizational:

Employees should not treat AI-generated output as a deliverable.

They should treat it like a photographer treats a raw image: something to inspect, improve, select, or discard.

High AI adoption does not mean to maximizing AI output.

The goal is to deliver meaningful outcomes in the most efficient way.

The risk profile change when AI is embedded in a system.

An employee using ChatGPT to draft a presentation is unlikely to create a major infrastructure bill overnight.

An autonomous system can.

Agents may run for hours, call models and tools repeatedly, process large volumes of data, retry failed actions, and continue operating without supervision. Every token, request, document, tool call, and retry can increase the cost.

But financial exposure is only one part of the downside.

An engineered AI system may also send incorrect messages, alter records, expose confidential information, make unauthorized decisions, or generally. take actions that are difficult to reverse.

These systems require explicit financial and operational limits.

Before allowing one to run, define:

This is the Cost Cap model and AI Governance in action.

Together, these controls prevent a small technical failure from becoming a larger financial or operational problem.

They also make experimentation easier. Because when the worst-case outcome is known and contained, teams can test more ambitious ideas with greater confidence.

You could understand downside control as an argument for only launching smaller, more cautious projects.

This couldn’t be more wrong.

Ultimately, downside control is what makes ambitious projects possible.

The goal is not to make AI projects smaller. The goal is to design them in a way that preserves most of the upside while keeping the downside under control.

I’ll give you two examples.

In Productivity AI, you could limit the downside by giving AI to only a small group of people. That certainly reduces the risk of poor AI-generated work spreading through the organization. But it also removes most of the potential impact.

A better approach would be to make AI generally available with clear expectations for how AI output is being used. AI-generated work is always a draft, not a deliverable. Hold the person submitting the work accountable for its quality. Make it explicit that responsibility for quality can’t be delegated – not to AI or to anyone else.

For Engineered AI, suppose you want to automate customer support. You could start with an AI that drafts replies for human agents. That feels safe, but it also limits much of the potential value.

Instead, I would recommend building the self-service experience you actually want, but limit where it operates. Give the AI broader capability within a narrow category of customer requests, rather than limited capability across every request. Restrict the actions it can take, monitor the results, and expand the scope only after it has proven reliable.

Instead of asking:

How can we guarantee this will work?

ask:

How can we test this opportunity in a way that keeps the downside small while preserving most of the upside?

You’ll never be able to completely remove uncertainty from AI projects.

But you can make it affordable enough to keep experimenting.

Which is what downside control really is all about.

See you next Saturday,
Tobias