Before You Decide: Finding the Right Moment

Years ago, I worked on a campaign for a pharmaceutical company trying to reach people who were interested in quitting smoking. This was online marketing, a mature channel at the time, so we knew what a good campaign looked like and what the numbers should be. We had good research data. The challenge was using the data to improve our campaign.

So, we did what good research teams do and dug into the data, where we found that receptiveness is not distributed evenly across time. Certain life events such as an upcoming wedding, a new baby, or a move turned out to be a strong indicator that someone was receptive to a message. However, those events did not create the urge to quit. They created a psychological reset, a moment when someone’s narrative shifts and old habits begin to look like they belong to a previous version of themselves. The moment was what mattered.

We built the campaign to reach people who were in those moments, and it more than doubled the standard benchmarks for response and engagement.

What stayed with me is that the data we gathered focused as much on the moment as on the audience or the message. Once we could identify the moments, the audience and the message followed that.

Moments Matter

This idea is not new. Marketers have called it the right message to the right person at the right time for decades. What I have found is that understanding moments is also critical to a strong business strategy.

Most decision frameworks look at macro signals like whether the economy is expanding, if consumer confidence is rising, or how your market share compares with your competitors. These signals can help describe the environment you are making a decision in, but they do not help you understand the moment when action (or inaction) impact your probability of success. In our example, we knew there were people out there who smoked and wanted to quit. That knowledge did not supercharge the campaign. It was when we understood the moments that influenced outcomes, that was when we could act in a meaningful way.

Today, in an AI economy, strategies must be dynamic and ready to answer two questions at any time:

1. Are we able to recognize when we are in a moment that we have been waiting for?
2. Do we understand the action that needs to take place once we recognize it?

What Has Changed

For most of history the first question was answerable only by organizations with deep-pocketed research arms. On our campaign we had analysts, purchased data and had the time to ask the right questions. Yet, most companies don’t have that.

What is different is the availability of data and our ability to use it are changing faster than most leaders have noticed. Regulated prediction markets, exchanges where participants trade on the outcome of specific events, have moved from academic curiosity to working infrastructure, and the questions and outcomes they price are specific and actionable. A year ago, the list included events such as Federal Reserve decisions, GDP releases and elections. Today markets have expanded to include the clearing price of a California carbon allowance auction, the endpoint of a late-stage clinical trial, and whether a state legislature will fix a wage rule before its session ends. That explosion in the variety and size of contracts is something a small or medium-sized company should pay attention to.

When California’s wage exemption for goat herders lapsed effective July 1, a contract appeared on whether the legislature would restore it before the session closed. This question mattered to only a few hundred people while almost nobody else cared at all. One grazing operation used it to price a risk it had no other way to address.

My point is not that I think businesses should get good at prediction markets. What I am saying is that prediction markets now give businesses a view into expectations. Whatever a business decides about the impact of an event, the market has already put a number and a date on the event that until recently would at best have been a guess.

An AI Decision Framework

Market prices are public and generic. It tells everyone the same thing, which is why on its own it says very little about what you in particular should do. Reading it well takes a framework. We have developed an approach with four steps.

First, detect the signals that bear on the decision in front of you, continuously, rather than commissioning a study once the question has already become urgent. Next, test each one before trusting it: did it move by enough to matter, did it move on real participation, and did it hold in a later reading? Then, position whatever survives that test against your own capacity and commitments, because a shift that is urgent for a company with an open decision is noise to another. Finally, have a disciplined method to decide based on probability and a pre-determined moment rather than either trying to predict or waiting for certainty.

The businesses that excel at doing this at scale will be successful.  What changes with AI is that one leader with a framework can run all four steps continuously. Critical support and vital information can now inform the most strategic decisions in near-real time.

Why This Matters Now

Your 2027 planning is well underway. You are deciding now where to invest, which markets to take on, where revenue comes from, and how to protect margin. Some of those decisions turn on events outside your business, a regulatory decision, a competitor’s move, or a weather event. You know your capabilities and what you have done before, and if nothing else changes you will likely run your business the same way you ran it last year.

Having a decision framework is critical. Understanding what moments impact your decisions is just as critical. When you put the two together with the discipline to act when a moment comes, you have a business that decides what its future will be.

For most of business history, the likely choice was to wait until the picture was obvious, because the data to read the signals was out of reach. It is not out of reach anymore.

You can immediately improve your strategic decision process. The question is whether you will.  Reach out to us if you would like to learn how.

We will take the framework apart, step by step, in the next issue.