The Illusion of Precision
Why your forecast is never a number and why that matters for every decision
We were investigating a partnership with another company. The idea was to license one of their products and sell it through our sales force. Given our strong position in the market, we were confident that we could make this deal work.
At the time, I was responsible for building the financial case. As I had done many times before, I spoke with each department and gathered their best estimates on how the product could be marketed and sold. I knew from experience that the first draft would not be perfect, so we challenged assumptions, refined the model, and looked at the numbers from different angles until we felt comfortable with the outcome.
Eventually, we landed on what seemed like a solid case. The deal was expected to add €100m in revenue with a decent profit, and after a few rounds of negotiations, we decided to move forward.
A year later, I revisited the case to understand how things had actually played out. The result was disappointing. The deal was not profitable, and we ended up making a small loss. The main issue was revenue, as the market turned out to be far more volatile and price sensitive than we had anticipated.
When numbers look more certain than they are
Looking back, the numbers themselves were not the real problem. We had modeled €100m in revenue, which felt precise and gave us a clear view of the expected outcome. This made the model easy to communicate and seemingly robust.
But in reality, every forecast comes with uncertainty. What we described as “€100m revenue” was never a single number, but rather a range of possible outcomes. A more honest way to express it would have been to say that revenue was expected to land somewhere between €80m and €110m with a high likelihood.
In our case, the assumptions turned out to be weaker than we thought. Revenue ultimately came in at €80m, which was still within the range we could have expected, but not a scenario we had truly considered in our decision-making. As a result, the deal turned into a loss.
What happens if we are slightly wrong?
In hindsight, the lower revenue was not the real issue. The problem was that we were not prepared for it. We had implicitly assumed that the outcome would be close to our plan, and when reality deviated, it caught us off guard.
This is where the concept of margin of error becomes important. It is not about being pessimistic, but about understanding how far your estimate could be off. Many negative outcomes in business are not the result of poor decisions or bad intentions, but of underestimating uncertainty.
No team can predict the future with precision, whether it is sales, marketing, or finance. The role of finance is not to eliminate uncertainty, but to make it visible so that better decisions can be made despite it. Once uncertainty is transparent, the business can decide how to respond, whether by taking precautions or by reconsidering the decision altogether.
Making better decisions under uncertainty
All business decisions are made under uncertainty, and while negative outcomes cannot be avoided entirely, they can often be anticipated and managed. A strong business case does not only present a single expected outcome, but also considers what might happen if reality turns out differently.
In practice, this often means structuring decisions in a more flexible way. A phased rollout instead of a full launch, staggered investments instead of a large upfront commitment, or hiring in stages rather than all at once can significantly reduce risk. In other cases, it may be worth investing time upfront to reduce uncertainty, for example by running tests, launching a pilot, or gathering additional data.
To support this, forecasts themselves need to reflect uncertainty more clearly. Instead of relying on single-point estimates, using ranges provides a more realistic view of potential outcomes. Scenario thinking, such as best case, base case, and worst case, helps to make these outcomes tangible and easier to discuss.
It is also important to understand which assumptions actually matter. Not all inputs have the same impact. By testing how sensitive the outcome is to changes in key assumptions, you can identify where the real risks lie and where additional caution is needed.
Over time, tracking forecast accuracy can help build a better understanding of your typical margin of error. This makes future estimates more grounded and improves decision-making.
Not every decision requires the same level of precision. When the margin of error is large and the stakes are high, more safeguards are needed. When both are limited, decisions can be made more quickly. The key is to make uncertainty explicit so it can be factored into the decision.
Picking the right project
The concept of margin of error is also useful when comparing different projects. Two investments may appear equally attractive based on their expected outcomes but behave very differently once uncertainty is considered.
Some projects are relatively stable, where small changes in assumptions have only a limited impact. Others are much more sensitive, where even minor deviations can significantly alter the result. This does not mean that the safer option is always the better one, as higher uncertainty can also come with higher upside potential.
What matters is that this trade-off is understood and made explicit. The margin of error helps make risk visible and allows organizations to make more conscious decisions about which opportunities to pursue.
It is not about playing it safe
Margin of error is often confused with simply being conservative. If the expected range for revenue is €80m to €110m, it might seem reasonable to plan with €80m in order to increase the likelihood of outperforming expectations.
However, this approach has its downsides. Conservatism hides uncertainty within a single number, which can make forecasts less useful over time. As people begin to recognize this bias, they start adjusting the numbers mentally, which reduces trust and weakens decision-making.
In contrast, explicitly acknowledging the margin of error keeps the uncertainty visible. This allows the business to actively manage risks rather than implicitly ignoring them. Being conservative may protect you from criticism, but understanding your margin of error helps you make better decisions.
Investing with a margin of safety
In investing, uncertainty is unavoidable. Future growth, competition, and market conditions are all difficult to predict with precision. This is where the concept of a margin of safety comes in, often associated with investors like Warren Buffett.
Instead of paying what they believe a business is worth, investors deliberately pay less in order to build in a buffer. This approach is not about pessimism, but about acknowledging that their estimates may be wrong.
The margin of error reflects how uncertain an estimate is, while the margin of safety determines how you act given that uncertainty. This distinction is important. In business planning, conservatism is often used as a shortcut, whereas in investing, the margin of safety is a deliberate response to uncertainty.
What changed for me
The experience with this deal changed how I approach business cases. I now place much more emphasis on ranges and scenarios rather than relying on a single expected outcome.
One tool I find particularly useful is sensitivity analysis, which shows how results change when key assumptions are adjusted. For example, what happens if prices decrease slightly, costs increase, or growth is slower than expected?
These are not complex questions but answering them provides valuable insight. Instead of focusing on a single number, the discussion shifts toward understanding what could happen and how the business should respond.
That is ultimately what leads to better decisions.
Great post, and I completely agree with what you're saying. All too often we treat forecasts as a specific number and celebrate when we beat them and flip out when we miss them. When in fact all of those experiences were within the range of possibilities. It leads to a lot of bad decisions. However, all too many business people have been trained for too many years to react to that specific number. Education is definitely required to help people understand statistics. I often use weather forecasts as a metaphor to help people understand. Of course, people always blame the weatherman for getting it wrong, but the reality is that weather forecasts can help you prepare for a range of possibilities that could happen. And that's really the point of a business forecast too.
The trap you're describing is subtle because it feels like diligence. The more precisely you model something, the more invested you become in that model being right, which means you start filtering out the signals that would tell you it's wrong. Precision becomes a way of managing anxiety, not a way of understanding reality.
In my work, I see this play out constantly: people come looking for specific answers because certainty feels safer than good judgment. But the answer they're looking for is usually the wrong question made very detailed. A rough framework with honest error margins beats a precise model built on false assumptions.
Every time.