A few weeks ago, an executive working in financial services wrote me the following:

“One challenge I’ve consistently encountered is that many strategy engagements culminate in well-written reports that are insightful but not sufficiently data-driven or repeatable. I’ve been searching for a robust methodology that can systematically evaluate strategic options, account for changing market conditions, competitor positioning and other external factors, and ultimately support better strategic decision-making.”

This statement got me thinking for a while. The challenge resembles what I have seen CEOs, product leaders, and innovation managers deal with. Bringing new technology to market requires making better innovation investment decisions when the future is uncertain.

The innovation dilemma: returns under uncertainty

One of the hardest decisions for a leader is deciding how much of today’s profit should be reinvested into tomorrow’s growth. Should the company continue optimizing the existing business, return cash to shareholders, or invest in new products and technologies that may create future opportunities?

In a way, the dilemma is similar to taking new supplements. You want to obtain long-term benefits, but you might be skeptic because you do not know exactly how your body will react to them and you do not want to harm yourself. Innovation investment works in the same way. Companies want to embrace innovation but in the face of uncertainty, returns become blurry. The challenge is to find the right balance between capturing future opportunities and protecting the value already created in a world where outcomes are uncertain.

Innovation decisions need to account for uncertainty. Uncertainty is different from risk. Risk describes situations where possible outcomes can be estimated. Uncertainty describes situations where we do not know exactly what will happen, or even which outcomes are possible. Innovation is dominated by uncertainty.

From a financial perspective, capturing future opportunities without taking too much risk comes down to one question:

“For every euro invested on innovation, how much additional value can we realistically create, despite of uncertainty, and when will we see the actual monetary returns?”

From a strategic perspective, this translates into taking the right sequence of decisions, in the face of uncertainty, to reach a desired outcome.

Traditional expected returns do not work with innovation

The traditional way to find how much to invest in risky projects involves a few hours on a spreadsheet to produce a Net Present Value (NPV) calculation. It returns an “expected return on investment”, a single number that compares investments, revenues, costs, and returns under different scenarios.

This approach is useful because it provides a common financial measure and helps compare alternatives. However, when applied to innovation, NPV has two important limitations.

First, it does not systematically deal with uncertainty. And innovation and new product development are uncertain by default. You cannot accurately predict the future revenue of a product that does not exist yet. The way to compensate for uncertainty in NPV calculations is to apply very high discount rates, sometimes even 40–60%. This compensation usually does a good job of modeling risk but it comes at the expense of drastically penalizing the value of “moonshots”. These are the high-risk, high-reward, long-term projects that can ultimately make a difference to the future growth of a company.

Second, NPV assumes that once you invest, you simply follow the plan. It does not consider that managers can learn from new information acquired along the way and opportunistically change direction. Companies do not run innovation projects on autopilot. They manage them by accelerating promising projects or killing initiatives that no longer make sense. They also react to external events.

Ignoring these limitations can lead companies to invest massive amounts during development, hoping to reach the expected break-even. The problem is that once you commit to a project and start heavily investing, it becomes difficult to kill the project and scrap the investment halfway through. Yet, the harsh reality is that a staggering 80% of product development projects don’t succeed. They either fail or become unprofitable. In the best case, they get killed before they ever reach the market.

The problem is that innovation is different from R&D. R&D requires commitment and stubbornness. But when it comes to commercializing a promising idea, innovation requires flexibility.

Traditional contingency management plans do not work, because they deal with risk, not uncertainty.

Innovation should embrace deviations from the original plan

Successful innovation rarely reaches the outcomes expected on day one. If it did, we probably would not have discovered penicillin as an antibiotic, and Coca-Cola might still be sold as a medicinal product.

It is not just a matter of positioning a new technology as a product. The reality is that consumer behavior evolves, regulations change, competitors react, new technologies emerge, patents are granted or rejected, trials succeed or fail, and wars and pandemics happen. Many of these events can hugely impact the path to commercialization of a promising idea. Some of these events are predictable but uncontrollable. Some others are just impossible to predict. So, there is no point in using decision frameworks that assume the path from idea to commercialization is a predictable roadmap.

Moreover, innovation should encourage structured exploration and deviations from the original path. This leads to find better ideas, fix mistakes, alter preconceptions, and adapt to what users actually want. This is why, sadly, traditional business plans don’t work with innovation.

Yet managers are responsible for making investment decisions when it is impossible to predict every possible scenario. This means that investment decisions cannot be “fire and forget”. They must evolve over time as new information becomes available.

Treat investments as sequential options rather than upfront commitments

A more suitable way to evaluate innovation investments is to avoid committing the entire investment upfront after a single major decision, and instead treat innovation as a series of decisions that evolve as uncertainty unfolds.

This allows management to decide what to do next whenever a contingency occurs.

For example:

• If customer adoption is stronger than expected, accelerate investment.
• If technical challenges emerge, change direction or reduce scope.
• If market conditions deteriorate, stop and preserve resources.

Basically, instead of asking “Should we invest €1 million in this project?” the question becomes: “What is the smallest investment we can make today to learn enough to decide the next step?”

The core investment principle becomes “Where and how much should we invest today to have a choice tomorrow?”

For some companies, delaying a major investment decision may sound counterintuitive. It often seems preferable to select a small number of initiatives early, commit significant capital to them, and focus on making them successful. If one project fails, there are usually other initiatives, and new budgets, to absorb the loss.

For startups and SMEs, however, resources are much more limited. Committing smaller amounts of capital to delay larger investment decisions can reduce risk across the entire innovation portfolio. After all, investing a little to learn more allows them to make better-informed decisions, move quickly, and allocate scarce resources where they are most likely to generate value.

How to de-risk innovation decisions upfront

Although you can split a major investment decision into smaller ones, you still need to evaluate the decision today.

To actively de-risk innovation, shift away from static planning, where uncertainty is buried in one number, toward a framework that explicitly models decisions under uncertainty. This process allows you to look at the future and roll it back to the present, so you can decide what to do today.

1) Build a baseline case to establish the “happy path”. This requires you to describe the traditional business case assuming everything goes as planned. The baseline needs to account both costs and benefits, quantifying qualitative benefits when possible. The NPV method or any other quantitative method to establish a business case still works well at this stage.

2) Define uncertainty. Identify the crucial milestones and external events that could drastically change your path and create alternative paths. These can span technical feasibility, intellectual property clearances, competitive changes, and compliance delays.

3) Order events. Select the top few, high-impact events and map them onto a strict chronological timeline. Selecting events that matter most should not be a single-person exercise. It requires consensus from decision makers with different stakes in the business.

4) Embed decisions. Consider how management can act to mitigate risks and leverage opportunities when events materialize. For each event there are usually multiple options available. Pick the most obvious ones. Always consider inaction as a possible basic option.

5) Actualize decisions. Once you have considered the possible uncertain events and managerial responses, evaluate all the possible resulting scenarios. Roll the economic outcome of each scenario back to the present to calculate the current value of starting the initiative. This value must be at least positive for the project to provide any return on investment.

You can use this process to compare different initiatives and choose a preferred one. For instance,  you can run this process to compare alternative business models around a new value proposition. You may compare whether it is better to develop a key resource in-house, outsource a turnkey solution to a partner, or personalize the front-end of a third-party commercial solution.

This process allows you to move forward today without committing the full capital and without being constrained by what happens in the future. Should things change (and they certainly will) during the project, you add another uncertainty to the tree and calculate the value as of today, based on what you know today, regardless of what happened in the past.

Agile decisions outperform accurate predictions

In projects where uncertainty leads, rather than predicting the future, what wins is making better decisions while accepting that the future will inevitably change.

This is especially true for innovation initiatives. But uncertainty does not mean companies should invest blindly, trusting that management will take the right decisions and adjust outcomes. Some paths can lead to outstanding results. Others are flawed from the outset. Choosing the right investment from the beginning is essential.

On the other hand, expecting an innovation initiative to reach the exact outcome expected is too idealistic.

This is why decision-making needs the flexibility to choose the right outcome without making later decisions to adjust the outcome based on contingencies.

By treating innovation as a sequence of decisions rather than a single irreversible bet, you can limit downside risk while keeping the opportunity to capture unexpected upside.

This does not only apply to R&D projects but to every strategic initiative that deals with uncertainty.

For CEOs and innovation leaders, the objective is not to predict the future perfectly. It is to make better innovation investment decisions with the information available today, while keeping future choices open.

In uncertain times, we do not need better predictions, but better ways to model decisions under uncertainty.

Credits

This post’s image is designed by Magnific.