Whilst not always the case, ‘Return on Investment’ (ROI) is usually viewed through a financial lens. The significance of social, economic and environmental value creation is, disappointingly, usually viewed as less important.

Investors expect a financial return so when it comes to investment in innovation there will always be a demand for some form of financial modelling of the return.  

But there is a danger that attempts to define the value that is created from innovation using a handful of crude measures can give misleading results about investment decisions. When it comes to investment analysis, the results of the financial model are actually less useful than the thinking, insights, and shared experience the process of investment analysis brings together. 

“At the beginning of each year, while many individuals make New Year’s resolutions, organizations make forecasts about the year ahead. Both have high fallibility. Forecasts are predictions about what will happen in the future based on information currently available. As such, they are exercises of imagination, which studies have shown are rarely correct in their particulars.

Despite being reliably incorrect, savvy leaders can find strategic value from forecasts. When reviewed in aggregate, they capture the zeitgeist. We just need to ask the right questions: What do experts believe is important and likely enough to forecast? Where do they agree, and more interestingly, where do they disagree? Even if forecasts are specifically wrong, what do they indicate about the underlying trends and pivotal issues?“

Martin Reeves, Suvasini Ramaswamy, and Annelies O’Dea Harvard Business Review 2022

The application of textbook models and traditional approaches to analysing market size, customer adoption, costs and price can, by virtue of their seductive complexity, create a false sense of comfort and – even worse – a failure to look at the bigger picture. There can be no doubt that monte-carlo risk valuation, discounted cash flow analysis and net present value calculations provide valuable insight – but the complex nature of innovation means it is context that matters most.

Many of the limitations of quantitative analysis can be overcome by a different approach that involves looking at the impact of ‘what-if’ scenarios and their links to the different types of value creation, not just financial.

This type of whole-system scenario modelling and wider assessment of non-financial value creation provides greater insight into the trends, issues and triggers that influence levels of success and failure.

One of the biggest limitations of ROI analysis is that the greatest influence on innovation outcomes are the innovation system’s actors – the people creating the supply of ideas, developing solutions and their narratives and, ultimately, the actual innovations that solve problems and create value.

This is why, when it comes to the business of innovation, and the development of business cases, understanding the difference between complication and complexity is important. ROI analysis can be complicated, but complication does not mean greater certainty when it comes to modelling the complex factors that affect innovation and the turning of ideas into sustainable value.

Real-world complexity is the Achilles heel of forecasting ROI.

The Innovation Forecasting Challenge

Creating sustainable value from innovation is not guaranteed. Product development often fails to deliver a forecast ROI despite what appears to be comprehensive analysis that presents an unquestionable outcome that a proposed investment will be a success.

This is because the valuation of forecast outcomes can be somewhat arbitrary.

Financial Crysis Recession Economic concept

Known but often overlooked shortcomings include:

  • Assuming prices and margins in real-terms will remain the same or grow forever – market dynamics affect pricing. Competitor discounting, disrupted value chains and existential events such as inflation or climate change will happen. Economies are cyclical – change is a matter of when, not if and this affects prices and costs.
  • Assuming the timing of key events is known – one of the key variables in every investment forecast is that of when sales take-off, and the shape of the customer sales curve. But timing is affected by a huge number of factors. A case in point is the impact of the Covid-19 pandemic and working from home, where a shift that was always anticipated as inevitable accelerated from decades to months. MS Teams users grew from 2 million in 2017, to 75 million in 2020 and 270 million in 2022. This could never have been predicted in any business investment case, but Microsoft were ready.
  • Sometimes things don’t work – innovation is not a zero-risk game. Projects fail, and it is not just something that happens to smaller companies. Google’s plans to deliver global internet access to remote areas via a fleet of floating balloons in the stratosphere, ironically named Loon, was shut down in January 2021 despite almost ten years of development that’s included over 20 balloon crashes. The Google X Laboratories Director said “Sadly, despite the team’s ground-breaking technical achievements over the last 9 years […] the road to commercial viability has proven much longer and riskier than hoped.”

Estimating financial return when costs, sales and margins are all subject to so much uncertainty – despite extensive analysis – means forecasting ROI can be near impossible.

The Making Innovation Happen Challenge

Innovation outcomes vary due to complexity and context.

This is undeniably true since people are a part of the innovation system – variations in behaviours, competency, capacity, experience, and not least culture, lead to wholly different outcomes, even in what appear to be similar conditions.

Context means that some teams perform better than others when faced with similar problems as the path along the infinite innovation journey is laid over time. Future steps are influenced by history, the acquisition of new knowledge and the information gathered along the way. And how it is used is a critical success factor.

And it is important to understand that complexity does not mean volatility. There may be long deceptive periods of stability followed by a sudden tipping point, triggered by an organisational change or existential event, that leads to a rapid and significant irreversible transformation and period of instability. And this pattern is then repeated again and again over time.

In a complex system, the level of certainty about future scenarios varies over time, and this itself leads to changes in the interaction between people, processes, technology, and culture – which in turn affects innovation. Uncertainty is different to risk – it cannot be managed or mitigated, but it can provide inspire creativity and generate opportunities for innovation. 

The importance of context cannot be overstated since context plays a critical role in determining if and when innovation-driven whole-system change may happen.

Markets might be on the edge of a tipping point and ‘ready for change’, or largely fixed until disrupted by an existential influence – government policy, social perspectives, economics, and exponential developments in technology can make systems susceptible to transformational change.

In essence, looking at the bigger picture using scenario modelling of future system states is more informative than using investment analysis based on fixed assumptions about what appears to be the most likely scenario. This is especially true when looking further into the future to forecast medium-term and long-term returns.

Summary

Innovation is complex and that complexity exists at multiple levels – industries, businesses, teams, and individuals. It endures within a network of users and suppliers, each adopting different mixes of processes and technology in different cultures and micro-cultures that vary across those same industries, businesses, and teams.

In reality the greatest influence comes from the system’s actors – the people creating both the supply of ideas and the market demand for innovative solutions. Given similar size investments, similar teams and similar circumstances, some businesses and teams will outperform others in terms of innovation outcomes.

When it comes to doing the actual work, people’s relationships, beliefs and motivations are representative of the culture of innovation – and a truly innovative culture is hard to create – it takes time. Industry logic, routines, behavioural norms, networks, power hierarchies and mixed incentives, all play their part.

These create complex innovation ecosystems that can stall innovation – or make innovation happen.

What does this mean when it comes to making decisions about investment in innovation?

  • Don’t rely on quantitative analysis and complicated spreadsheets as the ultimate test of success or a source of comfort. As an alternative, use scenario modelling to look into the future and explore how context might change, and what might trigger that change.
  • Only use ROI calculations to provide insight and inform future decisions about investment, not as the main tool to make decisions based solely financial criteria.
  • Test the most important assumptions including those about long-term pricing movements, the timing of key events and the fact that sometimes things just don’t work.