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Principles of an Innovation Accounting System

Although a driving force for growth in most industries, innovation differs from company to company. But be it in banking, in automotive or FMCG, the uncertainty associated with innovation is still something that needs to be managed. And in order to be able to manage something, it first needs to be measured and understood.

Unfortunately,  traditional financial accounting metrics have not evolved to address the needs of innovation management. And it’s unlikely that accounting standards will change in the near future. Therefore, organizations often revert to using lagging financial indicators such as  profit and loss (P&L) and return on investment (ROI) to measure the progress in innovation.

While these might be useful indicators for the core business where the degree of uncertainty is small. For innovation they feel like trying to steer a car on a highway, looking in the rear-view mirror. This is due to the fact that the results occur far too late in the idea’s life-cycle to inform its actual development. Thus, according to Eric Ries, an innovation accounting system becomes a mechanism of evaluating progress when all the metrics typically used for mature businesses are effectively zero.

With innovation differing from company to company, but the common need for measuring it staying the same, an innovation accounting system needs to be rooted in principles transcending industry peculiarities.

Company wide framework

Firstly, an IA system needs provides a company-wide framework of chained leading indicators. Each of which predicting the possible success of the ventures being evaluated. Every link in the chain is essential and, when ‘broken’,  the entire venture is red flagged.

Having the system deployed company wide, enables apples-to-apples comparisons between two or more ventures. So that an evaluator can decide which venture is most worthy of continuing investment. Basically this provides a way to see any innovation project in the portfolio as a form of financial option with a clear potential revenue, volatility and associated cost.

Abstraction

Secondly, an IA system needs to be able to abstract information. The concept of abstractions is imported from computer science. In computer science, the abstraction principle is used to reduce complexity and allow efficient design and  implementation of complex software systems. Abstraction is the act of representing essential features without including the background details or explanations.

Applied to innovation management, this essentially means translating what innovation projects report on daily/weekly to insights that executives require to take strategic decisions quarterly/yearly. Executives or shareholders shouldn’t be expected to have the time to look into, for example, a particular team’s learning velocity. But the learning velocity of team, has massive impact on the time-to-market and survivability of that idea. Therefore the IA system needs to be design and deployed in a way that ensures the seamless upstream flow of actionable data from product teams to the board.

Innovation ecosystem improvements

Thirdly the design of an IA system should inform innovation ecosystem improvements. A company’s innovation ecosystem is made up by more than just the processes and the idea portfolio. An innovation ecosystems is encompassing human resource capabilities,  partnerships and culture. Thus, the role of an IA system is to uncover the causal relationships between inputs and outcomes. In doing so the insight drawn from the IA system will prevent investments in low impact activities.

Specific use of the firm’s growth assets

The IA system needs to outlines the specific use of the firm’s growth assets. And the strategies deployed to extract value from the accounting recognized assets. This forth principle is going to mitigate the drop in usefulness of financial reports identified by professor Bruch Lev. While at the same time placing more emphasis on intangibles and non-accounting recognized assets such as people and processes.

Enterprises that are constantly at the top of their game distinguish themselves from competitors by having secured a ‘sustainable competitive advantage’. To do that the companies need to focus on strategic resources like patents, brand, organizational culture or unique process. An example would be Neflix’s customer recommendation algorithm – that combined with other  elements is differentiating Netflix from other content streaming companies.

However, ironically, most of these strategic resources and growth assets are not reported in the financial-accounting system [lev 129]. Since the investments made in these are immediately expensed and listed under costs (mainly OPEX).

The role of the IA system would be to surface these spendings and mark them as investments rather than just costs. And in the process flagging any potential inefficiencies.

Disruption risk

Last but not least, the IA system needs to be designed so that the company’s risks of disruption is clearly painted.

With  entrepreneurial successes such as Airbnb, Warby Parker or SpaceX  taking over established industries, the desire to disrupt a sector via technological design is on the rise. All happening under the eyes of corporate leader hoping they are not going to be the next in line to be interrupted or made obsolete while holding tightly to  the structures in place where they dominate the market.

Clayton Christensen first proposed ‘The Disruptions theory’ in his book Innovator’s Dilemma. The concept is that an outsider, usually a startup or SMB comes into an  established industry and shakes things up with usually a digitally advanced alternative or subcategory of an existing model. In the  process, they are threatening or replacing the complacent legacy players (incumbents). Examples of this phenomena infiltrate business, politics and even national security.

The Disruption theory has seen its fair share of criticism for being biased towards success stories supporting the theory.

Regardless  of where you stand on the disruption theory debate, change is  inevitable and is impending across all industries as digital 4.0 takes effect globally. The fact that Uber became a market leader without owning any traditional assets (cars) can’t be ignored.

Disrupting  an industry means more than just startups gaining market share from  incumbents. Disruption is a shift in ‘business as usual’. It’s a change  in the money flows and value propositions. Broadly speaking disruptions squeeze out the inefficiencies and those profiteering from lack of  transparency, pushing the industry forward and offering a more convenient — usually -digital option. Outages brings a new wave of competition to a stagnant market transitioning to Industry 4.0.

‘The  Disruption Theory’ is meant to serve both as a chronicle of the past  and as a model for the future.

Corporate  leaders need to understand – aided by an IA system – that if their company is under threat of  disruption they need to deploy immediate countermeasures.

We’re in the early stages of a corporate innovation revolution, and the principles outlined above are simply the foundation of a new way of managing growth though innovation. A way that is more fact based than faith based. A way that puts data in the centre of the decision making process through a system that’s designed to complement the short coming that the financial-accounting system has when it comes to measuring innovation.

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