Preparing to be hyper-innovative in an environment of nimble start-ups and non-corporate behavior

For much of the 20th century, “experimentation” took place in R&D labs, vast multi-billion dollar departments where engineers and chemists steadily invented the future.

That situation has changed. There is now a more pervasive need to change, with all the risks that accompany it. Companies need to ramp up innovation; they need to change the behavior of employees, cast the net wider for talent and plot a way through the multiple theories of how a firm innovates, to find exactly the right process for their unique needs.

All this carries risk, not least in the opportunity cost of getting it wrong.

What, precisely, is changing in the need for innovation?

1) Traditionally, large companies sustained their market share by controlling the flow of innovation. Very often they did so by making innovation expensive. Financial institutions are trying to do this today via their captive funds and fintech investments. It is necessary but not likely to work as a way of controlling change. Innovation is now pervasive because it is cheap.

2) Innovation comes from companies that create products “in the flow,” launching early, gathering customer feedback and then improving. This is in stark contrast to the days when, say, a consumer electronics company would systematically draw technologies down from a seven-year development cycle, bring it into the two-year go-to-market phase and then ready a perfect product for launch.

3) The direction of innovation is structural. We are seeing a significant geographical rebalancing of power. Layered onto that are new horizontal technologies like AI, digital money, data and platforms that will enhance a firm’s ability to manage at scale.

It is also abundantly clear now that “service” and “product” are integral (the car and the ride: Uber; the property and the rental: Airbnb; the smartphone and the app: Apple, Google).

This means that smart leaders know there are telling changes to be made as to how customers are served and retained. That, in turn, means companies have to face up to process innovation.

In this latter area there is a rapid stream of new technologies that will enhance the efficiency of the economy and improve customer outcomes: the Internet of Things, cognitive or behavioral computing, data, improved tools for user experience design, the integration of services in new platforms, microservice architectures in software design, and so on.

Companies that lead these technologies often take an experimental approach to their markets, services and products. That idea of experimental capitalism is not yet well embedded. But from a risk point of view it raises numerous issues:

Raising the bar on innovation

While large institutions are, of necessity, going to interact more with smaller ones, they also need to improve their own ability to innovate in appropriate ways. By appropriate, I mean that there are many choices for how to innovate and where. This is the area where people talk of “fail fast, fail cheap,” or of iterative innovation. These terms need to be understood.

In addition, there are many innovation techniques that enterprises need to master: design thinking, Six Sigma or continuous improvement, software-enhanced processes, the software-business dialogue, lean innovation, kanban or visual process development, and so on.

Decision-making and non-corporate behavior

More people are seeking wealth-generating opportunities, as evidenced by the rise of fintech start-ups. More people want the power to shape their destinies. That’s a difficult demand for financial institutions to meet right now, given the constraints imposed by regulators. However, without some liberation, institutions are going to lose more people to the start-up community or to less stressful jobs elsewhere.

The main feature of empowerment really centers on decision-making. In entrepreneurial environments leaders make decisions in different ways from inside corporations, but, critically, they also acquire resources differently.

In smaller enterprises, decision-making is more inclined to be “effectual.” That term, becoming more widely used in entrepreneurial literature, embraces how resources are sought and orchestrated. Literally it means to pursue an exploratory logic, rather than a predictive one. This can be very difficult for large institutions that rely on replicable ways of getting things done. Effectual decision-making is centered on the entrepreneur’s personal motivation, incentives and relationships. This gives rise to an unpredictable mix.

By contrast, traditional business plans embody the ideal of prediction, even though this is misguided. By and large they pretend to be probabilistic when there is in fact no hope of creating a business plan based on probabilities.

Successful entrepreneurs, on the other hand, think more of building the relationships that will give them resources to pursue an emerging opportunity. They can function easily with ambiguity or lack of clarity, and they seek to expand the network of collaborators as an opportunity grows. They invite the possibility of the unexpected.

The big question for large institutions is just how much of this can be controlled, tied into risk mitigation metrics, or be used as a basis for understanding the overall strategic soundness of the firm.

These same questions arise when an institution wants to deal with any small company. The durability of a potential partner rests on the entrepreneur’s ability to deliver some stability over time.

Improving responsiveness to disruption

Finally, institutions need to tie their relationship to start-ups or smaller suppliers, and their own internal innovations more closely into the disruptions around them. That means re-investing in strategic analysis and the tools that go with it, a topic covered in the previous article.

If those are the requirements, then what can risk officers do to support their colleagues? Taking each of the above sets of issues one at a time . . .

Raising the Bar

Financial institutions, in my experience, pursue far too little innovation. In the early 2000s, Procter & Gamble would have 400 projects under way at anyone time. That now seems like a small number.

Samsung, in its OLED development program, had 50 major innovation projects as an umbrella for thousands of strategic and technical “questions” it wanted to address. What’s more, in the tech space it is not untypical for companies to deploy two or three competing teams to see which comes up with the best answer.

Separately, there is a tendency for banks to misunderstand innovation technique. The IT department might talk about fail fast, fail cheap, or the minimum viable product (MVP). The business will typically interpret the MVP as a finished product that they can launch, when in fact it can mean something as rudimentary as a paper prototype.

This tendency to misunderstand basic terminology is costing banks dear because they are drawing on techniques they don’t understand, and they are creating tension where they need cooperation. Banks need to alight on terminology that can help build bridges rather than destroy. In my experience, the language of proof of concept (PoC) sits more easily than MVP. A small difference but a vital one.

In addition, few financial institutions have organized their innovation efforts in a way that allows them to expand the innovation mindset and strategy.

Companies with a high innovation competency have hundreds of innovation projects, all rooted in a clear strategy. They have clear, ambitious goals. At P&G the strategy was to create 12 new $1 billion businesses. Think about that — 12 new unicorns from inside a major corporation. It worked. They doubled the number of £1 billion businesses. They did it inside a clearly defined architecture.

The diagram above shows a coherent innovation framework or architecture. It is based on interviews with 30 companies undergoing some form of transformation. The interviews took place in 2013.

The innovation office in the center has responsibility for some investment decisions (fintech, new external technologies or solutions), developing a cogent learning environment so innovation competency can grow, and accelerating digital business. On the right are external activities such as maintaining a 360-degree view of change in adjacent markets. At the bottom are internal innovation activities such as improving innovation technique, liaison with customers, managing open innovation relationships and managing new decision processes.

This type of architecture is vitally important if innovation is going to make a strategic contribution.

Some banks are addressing the innovation deficit by launching variations of intrapreneurship programs, allowing employees time and resources to work on new projects that could eventually become stand-alone companies. At best, these number in the dozens of projects. Usually it is much fewer, and rarely is it designed with a comprehensive framework. Not having these tools is a disadvantage that is easily overcome with better know-how.

Decision-making

New projects have to sit alongside new ways of making decisions. In the study mentioned earlier, we found that the vast majority of companies try to enact change while applying the decision disciplines of business-as-usual.

No doubt it is difficult to give entrepreneurial liberty to people inside large organizations, but it is necessary. Financial institutions have to let effectual decision process grow.

The decision quandary is not just about investment and risk. It is about character, behavior and trust. Entrepreneurs seek new relationships. They gather resources from willing participants. They use these resources to test hypotheses about the market. They advance when they have evidence that the market is likely to be responsive and when they have some degree of confidence that they will not let people down.

So decision-making is really about relationships, resource gathering and orchestration, testing, advancing, testing again, against the background of an underlying conviction that the market has unmet needs. Projects like this will not take a linear route and cannot be squeezed into conventional stage-gate decision processes.

That is not to say, however that they cannot be measured. Any investments a bank is making in intrapreneurial projects (whether incubators, accelerators or moonshots) can be assessed in new ways.

A good entrepreneurial project will have six major strengths (which I have formalized in a new metrics package, should any reader be interested to follow up):

  • The team’s behaviors will demonstrate adaptability; most projects/businesses fail, and adaptability is an important defense against outright failure.
  • There will be a bias towards scale and the competency to deliver.
  • The project will have strong resource management principles, some of which will be explicit.
  • It will have a novel approach to the product/service mix; typically being design-led with an emphasis on strong UX as a self-service mechanism that reduces the cost of scale.
  • There will be a low bias against inconsistency. All projects and entrepreneurs keep decision-making options open so inconsistency is inevitable, but it should be low.
  • The technical organization of the project should meet generally accepted standards.

In the metrics-suite I am working on, it is possible to score a project and its team against some 80 indicators that test for these characteristics. The same tests can be applied to fintech investments.

Disruption Responsiveness

The last article in this short series was about how to improve disruption analysis. Having data available for this should be part of the innovation architecture and the innovation office’s remit. Rather than repeating those points, see Analyzing Risk During Economic Transformation.

Conclusion

Yesterday’s capitalism was experimental in a very particular way — the laboratory. Today, experimental capitalism is out in the open and is often the preserve of small and growing companies.

For a variety of reasons, big enterprises have to be hyper-innovative in this new environment where behavior is often very non-corporate.

Risk managers will hear people talk about fail fast, fail cheap. Or their organizations might be invested in a number of start-ups that will be failing fast and cheap. Or they may be running moonshot programs to generate new ideas and an innovative entrepreneurial spirit internally. All of this can appear chaotic and divorced from strategy. It needn’t be. There is a role for being organized, systematic and measured.