The Hidden Cost of Everyday Decisions

Think about the major decisions your organization has made. How confident were you in the process? How many long did it take? And when the outcome finally arrived, were you satisfied with the decision itself— or simply relieved that a decision had been made?

These questions are not intended as critique. They are challenges that holds across industries, geographies, and company sizes. According to the McKinsey Institute of Directors, inefficient decision-making costs a typical Fortune 500 company an estimated $250 million per year.

Many organizations rely on fragmented data, intuition-driven discussions, and lengthy stakeholder alignment processes when making high-stakes decisions. The result is often slower execution, missed opportunities, and avoidable risks.

The Timing Question Most Leaders Get Wrong

Many organizations understand in principle that better decisions lead to better outcomes. What is less clear is when to bring in decision science expertise. The instinctive answer is: “when things go wrong.” A strategy that underdelivered. But, by then, the cost of a poor decision has already been incurred.

The right moment to invest in decision science consulting is when the stakes are high, the complexity is real, and the cost of a subpar decision exceeds the cost of getting the process right.

Decision Science vs. Data Analytics: A Critical Distinction

Before exploring when to engage decision science consulting, it is worth understanding how it differs from the analytical capabilities most organizations already have.

Data analytics asks: what happened, and why? It surfaces patterns, measures performance, and identifies trends. It is descriptive and diagnostic. The output is insight.

Decision science asks: given what we know, what should we do? It evaluates options, models consequences, and recommends actions. It is prescriptive and action-oriented. The output is a decision.

The gap between the two is not a gap in data quality or analytical sophistication. It is a gap in purpose. An organization can have world-class analytics and still make poor decisions because knowing what happened does not automatically reveal what to do next.

Decision science bridges that gap by providing the frameworks, models, and structured reasoning that turn insight into action. This distinction is why decision science consulting is most valuable not when organizations lack information, but when they struggle to act on the information they have.

When Do Organizations Need Decision Science Consulting?

Recognizing the value of decision science is one step. Knowing when it is the right investment is another. The following situations are clear, non-exhaustive signals.

1.     When facing high-stakes decisions under uncertainty

Major decisions such as market expansion, new product launches, mergers and acquisitions, or business transformations often involve significant risk and uncertainty. When Netflix decided to invest $100M in a series before a single episode aired, the decision was not made by intuition, but in structured data modelling using subscriber viewing patterns, director track records, and content performance data.

Decision science helps organizations make these decisions with greater confidence by using scenario modelling, risk assessment, and structured frameworks. Rather than waiting for perfect information, leaders can make informed choices based on available evidence while retaining the flexibility to adapt as conditions evolve.

2.     When your team cannot get aligned

Persistent disagreement across functions is rarely a matter of personality. More often, it reflects the absence of a shared decision framework: no agreed criteria, no common understanding of trade-offs, and no clarity over decision ownership. Decision science consultants establish the structure, language, and governance needed to align stakeholders and move decisions forward.

3.     When past decisions have not delivered

If your organization produces sophisticated dashboards but struggles to translate insights into action, the issue is unlikely to be data quality alone. More often, the missing link is a decision-making process that connects analysis to execution. Ford's $2.8B loss on the Edsel launch is a frequently cited case of a decision that looked data-supported but lacked rigorous evaluation of market fit. Post-mortems revealed that internal consensus pressure overrode analytical dissent.

This is among the most common and most correctable gaps consultants address. Decision science addresses this gap directly through tools such as structured devil's advocacy and explicit evidence-weighting. It builds a process that protects good judgment from the organizational dynamics and past decisions.

4.     When entering a period of prolonged uncertainty

Regulatory shifts, technological disruption, and macroeconomic volatility have made it increasingly difficult to plan with confidence. During the COVID-19 pandemic, McKinsey documented how organizations using structured scenario planning and probabilistic decision frameworks, rather than single-point forecasts, were significantly faster to reallocate capital, adjust supply chains, and reposition strategy.

Decision science provides these tools to make high-quality decisions under uncertainty, creating the range of possible outcomes explicit rather than obscured.

The Compounding Return on Better Decisions

Here is the thing about decision quality that rarely gets discussed in boardrooms: it compounds.

One better decision creates better options. Better options lead to better subsequent decisions. Over time, the gap between organizations that decide well and those that do not becomes vast. Not because of any single choice, but because of the cumulative direction set by hundreds of them.

“Companies that excel at decision-making generate returns more than 4x their peers.”  —  McKinsey & Harvard Business Organization research

Beyond financial returns, there is a cultural benefit that is harder to quantify but just as real.

Google's Project Oxygen is one of the most cited examples of decision science applied to organizational culture. They identified the specific behaviours that distinguished high-performing managers from low-performing ones, then rebuilt every people management decision based on an evidence-based framework of eight measurable behaviours. Manager quality scores improved by approximately 75%, and internal surveys showed significantly higher employee satisfaction.

Teams collaborate more effectively when decisions are made with greater clarity, and leaders gain the confidence to act decisively even in uncertain environments. The result is an organisation that is not only more data-informed, but also more resilient, agile, and prepared for future growth.

A Final Thought

The signals outlined in this article are conditions most senior leaders will recognize in their own organizations, often more than once in a given year. Organizations that bring in decision science support early, when the stakes are high but the decision is still open, extract the most value.

The right time is not when the answer becomes obvious. It is when the cost of getting it wrong is still worth avoiding. Knowing when to seek decision science consulting is itself a form of strategic advantage; and organizations that know this are already operating at a higher level.

Ultimately, sustainable competitive advantage is built one decision at a time. At M & Sullivan, we believe the best time to improve decision quality is before the cost of a poor decision becomes irreversible. Our Decision Science Consulting practice supports organizations at the moments when decisions are high-stakes, complex, uncertain, or difficult to align across teams.