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Experience remains a competitive advantage. But it is no longer enough on its own
For decades, Italian SMEs have built their competitive advantage on experience, market knowledge, and the ability of entrepreneurs to interpret signals from the business environment. This foundation has allowed a significant share of Italy’s industrial base to grow, innovate, and compete.
Today, however, the context is fundamentally different. Greater market volatility, more fragile supply chains, and increasingly frequent external shocks have made strategic decisions more complex. Experience remains essential, but it is no longer sufficient when the assumptions behind it remain implicit.
The point is not to question the judgment of experienced leaders. It is to recognize that, as long as their assumptions remain unspoken, they cannot be tested before capital is committed, shared with the team, or updated as conditions change. They continue to create value while the environment remains familiar, but become far more fragile when circumstances evolve.
Ask an entrepreneur how they decided to open a new manufacturing plant, acquire a competitor, or enter a foreign market, and the answer will often begin with the same words: “I knew the industry,” or “I had a strong instinct.”
That is not improvisation. It reflects years of experience, markets observed, relationships built, and decisions that gradually strengthened the ability to recognize signals that may be invisible to those looking only at the numbers.
The limitation emerges when this knowledge remains personal. Decision-making maturity begins when experience is translated into explicit assumptions that can be tested, discussed, and improved over time.
Looking at the numbers is not the same as making data-driven decisions
Many Italian SMEs already use sophisticated dashboards, monitor KPIs, and produce regular management reports. They track margins, measure variances against budget, and closely monitor operational performance.
That is an essential starting point.
But knowing what happened is not the same as understanding which factors produced that result or, more importantly, what would happen if one of those factors changed.
A company that monitors its EBITDA margin knows how profitability has evolved. It may not know which variables had the greatest impact, how sensitive they are to changes in the external environment, or how the business would respond if one of them moved significantly.
That deeper level of analysis is essential for decisions that can alter a company’s trajectory, including acquisitions, capital investments, international expansion, and the launch of new business models.
An analysis conducted by Vedrai Observatory on the decision-making practices of Italian manufacturing and retail SMEs—covering capital investments, acquisitions, and international expansion—revealed a recurring pattern. The companies that limited the most costly mistakes were not those with the largest volume of data. They were the ones that had made the assumptions behind their decisions explicit before allocating capital.
When an assumption becomes an organizational asset
The difference is not technological. It is methodological.
An implicit assumption might sound like this:
“I believe there is room for our brand in Central and Southern Italy.”
An explicit assumption defines the conditions that would make that belief plausible:
“We estimate that disposable income in the target cities is consistent with our price positioning, that foot traffic in the main commercial areas reaches at least X people per day, and that the number of direct competitors does not exceed Y stores within a Z-kilometer radius.”
The second statement is not necessarily more accurate than the first. It has one decisive advantage: it can be tested.
It can be compared with available data before the investment is made, updated during the first months of operation, and shared across the organization. This makes clear not only what the company intends to do, but also which conditions must hold for the decision to succeed.
This is what turns individual judgment into an organizational asset. Decisions no longer depend exclusively on the experience of one person; they become part of a process that can be understood, improved, and replicated.
A retail case documented by Vedrai Observatory illustrates the point. A clothing retailer with 28 stores in Northern Italy selected five cities in Central and Southern Italy for expansion, relying on site visits and established relationships with real estate agencies.
After 24 months, two of the five stores were still below break-even.
A post-investment analysis showed that both locations had demographic profiles that were inconsistent with the brand’s positioning. That information had already been available through public census data before the stores opened, but it had never been formalized as an assumption to be tested.
The problem was not the initial intuition. It was the absence of a process capable of challenging that intuition before capital was committed.
The cognitive biases behind implicit decisions
Even the most experienced executives are influenced by cognitive biases.
Research in behavioral economics has identified four mechanisms that systematically affect high-impact decisions. Overconfidence leads decision-makers to use the most optimistic estimate as the baseline. Escalation of commitment encourages them to continue investing because of what has already been spent rather than what the investment is likely to generate. Confirmation bias leads them to seek evidence that supports a direction they have already chosen. The availability heuristic causes recent or highly visible events to appear more likely than they actually are.
These mechanisms are not occasional flaws. They are structural features of human thinking under conditions of complexity and uncertainty, which means that awareness alone is rarely enough to neutralize them.
What can reduce their impact is a structured process that separates assumptions from the preferred course of action and subjects them to verification before a final decision is made.
From explicit assumptions to alternative scenarios
Once assumptions have been made explicit, the next step is to build at least one scenario in which they do not hold.
The purpose is not to predict the future with perfect accuracy, but to test the resilience of the decision. A choice that works only under the expected conditions is closer to a bet than to a robust strategic decision.
For a manufacturer, this may mean considering a scenario in which energy costs rise above expectations, demand grows more slowly, or a key supplier experiences a disruption. For a retailer, it may mean testing the impact of lower-than-expected foot traffic or the opening of a nearby competitor during the first six months.
The strength of a strategic thesis depends not only on how well it performs when events unfold as expected, but also on how well it holds when they do not.
This is not pessimism. It is a way to understand, before committing capital, what the cost of being wrong would be and whether the organization could absorb it. Companies that built this capability during periods of stability did not avoid the external shocks of the past five years, but they were able to respond more quickly and limit their impact.
Where to begin: three practical questions
Before making the next high-impact decision, leadership teams should ask themselves three questions.
- What assumptions does this decision depend on? Think about the most recent major investment, expansion, or acquisition. Could you clearly list the five main assumptions on which it was based? When the answer is “not easily,” those assumptions were probably implicit.
- How many assumptions were tested before capital was committed? And how many were verified only afterward? The gap between those two answers says more about the quality of the decision-making process than about the outcome of any single decision.
- What would happen if the most critical variable moved 20% in the wrong direction? When this question cannot be answered quickly, the organization probably does not yet have a sufficiently structured decision model. And decision models are not built during an emergency; they are built in advance, for the decisions that come next.
Conclusion
Experience will remain one of the greatest competitive advantages of Italian SMEs. In an environment characterized by growing uncertainty, however, the quality of strategic decisions will increasingly depend on the ability to turn that experience into explicit assumptions, test those assumptions systematically, and update them quickly as conditions change. The companies that outperform will not necessarily be those that predict the future more accurately. They will be those that are better prepared to make decisions across a range of possible futures. Because the real advantage does not come from having more data, but from using it to build better assumptions.
