Integrating Uncertainty into Business Cases for Better Investment Decisions

Learn How to Realize Opportunities, Anticipate Risks, and Make Informed Go/No-Go Decisions for Innovation and Novel Initiatives

How should you deal with uncertainty in business cases? This article helps to understand the opportunities, anticipate risks and – most importantly – make better investment decisions.

Getting funding for a new business idea sooner or later requires a persuasive business case. A good business case should not only explain the need for the initiative, but also forecast how it will benefit the company, either financially or with regard to other company objectives. These initiatives could include developing new products, services, or business models, investing in new facilities or machines, introducing new IT tools, or changing processes and the ‘ways of working’. However, what all these initiatives have in common is that organizations can never be entirely certain that the anticipated value will be realized in the future.

Although the future is uncertain, leaders have to make approval and investment decisions for new business ideas today. To deal with uncertainty, it is essential to have the right tools that allow you to realize the opportunities ahead, anticipate the risks and avoid being ambushed by them.

Despite being a standard tool for describing and evaluating new business ideas/innovation, traditional business cases do not prove useful when making decisions in the face of high levels of uncertainty – a common characteristic of innovation and other novel initiatives. In this article, we will describe what it takes to integrate uncertainty into business cases and how it can help make better and more informed go/no-go decisions.

The Flaws of the Traditional Business Case

At the core of the problems with traditional business cases is their financial forecast. Forecasting financials is often seen as akin to gazing into a crystal ball. A global survey of more than 500 senior executives involved in forecasting showed that only 1% of companies hit their financial forecasts over three years, with 70% being outside a 5% range [1]. To provide context, this low forecasting accuracy is derived from companies with an established business and historical data, highlighting the difficulties of forecasting the financials of a new business idea.

Business cases represent the state of knowledge at the early stages of a new idea, and thus when uncertainty is at its peak. Nonetheless, most financial forecasts use a deterministic model based on a set of single point input variables to calculate a set of output variables. This creates a pseudo-accuracy in which insufficient heed is paid to uncertainty. Typically, decision-makers expecting a thoughtful analysis of the associated financials and risks are aware of these flaws and don’t take the numbers at face value. Nevertheless, they lack information about how uncertain the numbers might really be. As a result, companies neither seize the opportunities nor manage the risks that arise in highly uncertain situations [2].

To account for uncertainty in a business case, companies usually rely on the calculation of worst and best case scenarios. However, the value of these calculations is limited:

  • Worst and best case scenarios are often presented at the outcome level of the business case, ignoring the fact that uncertainty also exists at the input level. In other words, premiums and discounts are used to account for the uncertainty of revenue and other outcome metrics, rather than looking at the uncertain input factors that affect the outcomes.
  • The presentation of worst and best case scenarios as fixed values pretends certainty, excluding the possibility that worse or better outcomes could occur in the future. In reality, this certainty does not exist.
  • Scenarios might show a huge opportunity or a big loss, but without information about the likelihood of these scenarios, it remains unclear whether they are even realistic. Consequently, information remains incomplete and potentially misleading.

Alongside financial forecasts and scenario calculations, business cases typically contain a risk section outlining the assumptions of a new initiative and explaining what may not go as planned [3]. Often, these risks are not explicitly part of a financial forecast. Risks presented as a narrative leave decision-makers unable to properly assess the financial implications of uncertainties. In many cases, the relationship between uncertain factors and financial outcomes is non-linear. A simple extrapolation of the financial forecast is not helpful, and assuming a linear relationship leads to incorrect decisions [4]. Moreover, business cases rarely list just one uncertain factor, but a bundle of them. Assessing the combined financial impact of several uncertain factors becomes an impossible task in traditional business cases.

In summary, traditional business cases are flawed when it comes to integrating uncertainty into the financial forecast of a new business idea. Thus, crafting a compelling story about the idea and making informed investment decisions becomes more difficult. A method that helps to solve these problems and that is gaining increasing attention in ​​innovation accounting [5], and also in risk and decision analysis [6] is range-based modeling.

Quantifying Uncertainty in Range-Based Financial Models

Range-based models are useful for calculating financial outcomes under varying conditions of uncertain input factors. In range-based models, input variables are defined as ranges with a specific level of confidence – such as 90% – that the true value will be within this range. Therefore, for all uncertain input factors, the respective range reflects the perceived uncertainty and incorporates it into the financial model. To run the financial model, random numbers are generated by applying probability distributions for the defined ranges (e.g. normal distribution) [7].

Example: A deterministic model would use a cost reduction of 7.5% as an input factor for a business case. In contrast, a range-based model would use values where people involved are 90% confident that the actual cost reduction will be within a certain range, such as between 5% and 10%.

Nobody can predict exactly what will happen in the future, especially when preparing a business case that involves a high degree of uncertainty. No one should have to pretend that an exact outcome can be known. By using ranges and probabilities, people don’t have to commit to anything they don’t know for a fact. Instead, ranges increase the precision and transparency of the assumptions underlying the business idea. In light of the globally rising uncertainty and volatility [8], using ranges instead of single numbers is also gaining increasing attention in other business areas, such as goal setting and planning processes [9].

Creating a  business case is typically a team effort involving experts from various functions. The use of ranges helps capture the diversity present. This way, a financial model not only reveals the assumptions themselves but also the different perspectives and valuations of the team members. Therefore, the team’s uncertainty is illustrated by the size of the ranges that the team has ultimately agreed on.

One of the most well-known tools for dealing with uncertainty in financial models is the Monte Carlo simulation. It is a random number-based method in which the business case runs through a large number of simulations. As a rule of thumb, at least 1,000 simulations are run, but depending on the level of uncertainty and the specific use case, many more simulation runs may be needed. For uncertain input factors, random numbers are generated in each simulation, and the resulting outcome is calculated. The result of a Monte Carlo simulation is a frequency distribution of possible outcomes. Running Monte Carlo simulations with range-based financial models provides a reliable basis for decision-makers to make a go/no-go decision based on the likelihood of relevant outcomes.

Decision-Making with a Probabilistic Mindset

Selecting new initiatives for investment and development is essential yet challenging. Decision-making is often influenced by personal biases, risk aversion of decision-makers, and the company [10]. The human mind, being naturally deterministic, hampers sound decision-making in situations of high uncertainty that are typical of early phases of new business initiatives [11].

Range-based financial models use probabilities, such as of revenue, cost-savings, or profits, at the center of the decision-making process. They foster a probabilistic mindset for those involved in building the business case and decision-makers. This widens the perspective on a business idea from possible to probable. By letting go of the deceptive certainty of single point estimations, range-based models reduce the craving for absolute certainty in decision-making. People no longer need to pretend to be sure. It makes the business case and its communication more accurate, honest and testable [12]. Probabilities avoid misinterpretation, unlike words such as “relatively high chance” or “maybe” [13]. Thus, data obtained through Monte Carlo simulations based on range-based models provides a valuable basis for presenting a compelling story about the business idea.

The variety of possible outcomes of Monte Carlo simulations enables decisions to be made based on probabilities and risk metrics:

  • Histogram or tornado chart displays the distribution of outcomes.
  • Statistical measures such as mean, extreme values, and quantiles indicate the location.
  • Measures of outcome distribution such as variance and specific risk measures such as Value-at-Risk help analyze the associated risks of a business idea.
  • Probability based go/no-go criteria can act as unambiguous criteria for approval decisions. Investments can be bound to a specific likelihood of achieving a particular outcome. At the same time, a business idea could be stopped based on clear no-go criteria, such as exceeding a particular likelihood of loss [14].
  • Information about expected outcomes and risks of different projects allow clearer portfolio decisions while selecting among competing projects and allocating resources.

The successful implementation of a new initiative requires an understanding of critical assumptions and uncertainties. By uncovering the most critical areas of an initiative, range-based financial models help focus and prioritize actions. To support a systematic reduction of uncertainty, many companies have established phased and metered funding processes. In this case, a decision is not made for funding the entire project, but only for the next phase. The idea is to give decision-makers proof that critical assumptions of the business idea are valid [15]. Range-based models enhance the objectivity of this process. They make it easier to feed newly gained information back into a financial forecast by updating input ranges. This leads to more informed and confident decisions with increased accuracy and speed.

Lessons learned

Uncertainty is the elephant in the room of every new business idea. Neglecting uncertainty in business cases and financial forecasting creates an incomplete and inaccurate picture of the initiative. Shifting from point estimates to ranges and thinking about the potential outcomes of a business idea as a probability distribution may not come naturally at first, but the effort is worth it. Range-based financial models, enabled by Monte Carlo simulations, create better business cases and support making smarter approval decisions by:

  1. improving transparency about the uncertainty underlying a new idea,
  2. aligning the project team and decision-makers around the involved uncertainty, chances, and risks,
  3. supporting a phased funding process to proactively and systematically reduce the uncertainty of the idea,
  4. delivering a profound, data-driven basis for creating a convincing story about the project, and
  5. providing data to make decisions based on probabilities of success and allocate resources to the most promising endeavors.



[1] KMPG, Forecasting with confidence: Insights from leading finance functions,, 2007.

[2] Hugh Courtney, 20/20 Foresight: Crafting Strategy in an Uncertain World, 2001.

[3] Raymond Sheen / Amy Gallo, HBR Guide to Building Your Business Case: Tell a Compelling Story. Identify Stakeholders. Analyze Risk and Return, 2015.

[4] Bart de Langhe / Stefano Puntoni / Richard Larrick, Linear Thinking in a Nonlinear World, Harvard Business Review,, May-June 2017.

[5] Tristan Kromer / Elijah Eilert, Innovation Accounting in Practice,, 2022.

[6] Douglas W. Hubbard, The Failure of Risk Management: Why It’s Broken and How to Fix It, 2nd Edition, 2020.

[7] Douglas W. Hubbard, How to Measure Anything: Finding the Value of „Intangibles“ in Business, 3rd Edition, 2014.

[8] Nicholas Bloom / Hites Ahir / Davide Furceri, Visualizing the Rise of Global Economic Uncertainty, Harvard Business Review,, September 29, 2022.

[9] Nadya Zhexembayeva, 3 Ways to Bring Flexibility to Budgeting, Harvard Business Review,, September 28, 2022.

[10] Thorsten Grohsjean / Linus Dahlander / Ammon Salter / Paola Criscuolo, Better Ways to Green-Light New Projects, MIT Sloan Management Review,, Vol. 63, No. 2, December 07, 2021.

[11] Mike Walsh, Develop a “Probabilistic” Approach to Managing Uncertainty, Harvard Business Review,, February 20, 2020.

[12] Tristan Kromer / Elijah Eilert, Innovation Accounting: The Failure of the Business Case,, 2021.

[13] Andrew Mauboussin / Michael J. Mauboussin, If You Say Something Is “Likely,” How Likely Do People Think It Is?, Harvard Business Review,, July 03, 2018.

[14] Tristan Kromer, How to Make “Pivot or Persevere” Decisions in Your Innovation Accounting,, 2022.

[15] Raymond Sheen / Amy Gallo, HBR Guide to Building Your Business Case: Tell a Compelling Story. Identify Stakeholders. Analyze Risk and Return, 2015.

Authors: Marcus Liehr und Elijah Eilert

Marcus is an entrepreneur, consultant, trainer and facilitator. As managing director of zagmates, he helps companies to thrive on their journey of strategic growth and innovation. In addition, Marcus is CEO of - the leading independent portal for online workshop templates. He is passionate about strategic foresight, business modelling and measurement, making ideas tangible and focusing on their impact. Prior, he had been working for more than 15 years in leading positions in international industrial companies focused on marketing, sales, business development and product management.

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