Innovation Models For The Digital Age

innovation_modelsEnterprises and organizations of all kinds — banks and financial services companies among them — are finding they have to innovate more to keep up with changes in the turbulent external business environment. They also need to adapt their internal organizational structures to deal with this.

However, very few organizations grasp the full extent of change facing them. We have heard for a long time about an increased pace of change but that has been a nebulous idea. We can now make it more concrete.

Today digital technology  permeates life. It affects our social and domestic lives faster than it does business, leaving companies with the task of catching up with their frustrated employees. Digital technology arrives as mobile technology and apps; big data and analytics; social media and social business; and changed relationships — people have far more control.

But the technologies we see today are only the start. Before we have absorbed this phase of digitization we will enter the machine-to-machine (M2M) phase, with interconnection between devices of all types. iBeacons from Apple, which enable microlocation, will connect people and devices in a very precise way. M2M is also already changing the way we pay for goods and services.

The scale of today’s technology platforms — what some call web scale — means we can now support vast peer ecosystems. For example, Google’s investment in Lending Club could allow the search engine giant to enable mutual lending between many millions of people.

Coming fast on the heels of digital technology is the Maker movement, which is supported by new low-cost design tools and manufacturing techniques such as 3-D printing. Larger companies will struggle to retain relevance unless they can adapt to an ultra-low-cost environment and the commerce associated with it.

At the same time, we’re seeing new ways of funding change. The success of crowdfunding sites like Kickstarter and Indiegogo shows that members of the public want to be involved in product development, at a certain level of risk. That willingness is leading companies to think more deeply about how they can make use of the crowd to validate product ideas.

In response to this, companies in the digital domain, and those with strong technology cultures, are adapting through three main strategic avenues: radical adjacency, externalization and fluid core philosophies.

Radical adjacency

Over the last 30 years companies have been discouraged from taking what became known as adjacency moves. They were told to stick to the core and improve core competency. That is now changing.

Companies are making radical adjacency moves to liberate themselves from market compression in the core and, in the process, to change organizational structure and culture. IBM periodically transforms in this way.

In 1991 IBM was a $64.8 billion company, of which less than $6 billion was derived from non-maintenance services. Ten years later, information technology services alone generated more than 40% of IBM’s $86 billion in sales and had become the single largest source of revenue in IBM’s portfolio. IBM is a go-to case study of a large hardware-dominated organization transforming itself into a services behemoth.


Companies that manage radical adjacencies well often externalize large parts of their processes. Externalization is a very important element of creating a more adaptive, elastic enterprise.

CAD/CAM specialist Autodesk, for example, has developed a huge external community of 120 million consumer users of its software and services. The company regularly taps into this crowd to learn about next-generation enterprise needs, based on the principle that consumers want what the enterprise of tomorrow wants. Note that Autodesk is not outsourcing its community development; it is successfully inserting itself into an ecosystem of consumer users.

Fluid Core

Together these trends are leading to the development of a fluid core competency. This fluid core is not a denial of core competency but a recognition that it needs to be adaptive and under the control of executives rather than being a fixed entity. Today online storage company sees its core as engineering. But CEO Aaron Levi sees a day when the core will be marketing.

The Rise of Systematic Innovation

These new concepts are both cause and effect in the proliferation of innovative activity in the modern organization. Now that proliferation needs to be managed.

Two systematic innovation methods have emerged in the past five years to deal with the complexities of scaling innovation. I call them “computational innovation” and “algorithmic innovation.” They both imply systematic step-by-step procedures. The difference between the two is simple. In the first, the development process is more flexible and defined by the project. The latter is more like following a decision tree.

Algorithmic innovation is also referred to as “TRIZ,” a Russian acronym that stands for “theory of inventive problem-solving.” Although TRIZ emerged in the 1950s it is now enjoying a revival and has been adopted by U.S. giants like Intel in the past few years. TRIZ consists of a number of tools that innovators can use to simulate options. It may seem archaic to think of innovation in terms of structure and rules. However Genrich Altshuller, the inventor of TRIZ, realized that most people approach problem solving with an inert mind-set, or at best based on what they already know. He wanted to give people ways to broaden their access to ideas and solutions.

These two methods allow companies to run multiple projects, at relatively low cost, with different techniques for product validation. In computational innovation the customer validates the product; in TRIZ, the algorithm solves the problem of innovation.

Google and Samsung provide good examples of these approaches.

Computational Innovation

At its core Google is an ads business but it seeks to insulate itself against risk by promoting a wide range of alternative revenue sources. One of these is Google Glass.

Glass is a heads-up display, rendered as a pair of ordinary-looking glasses; it’s one of the innovation stories of 2013. To develop and launch it Google offered the prototype to developers and to members of the public — at a price of $1,500. The proviso is they had to have a good idea of what to do with it.

Then Google teamed up with two venture capital firms, KPCB and Andreessen Horowitz, to fund the Glass ecosystem. So you have a product that isn’t yet a product and VCs backing an ecosystem that doesn’t yet exist.

In computational innovation it is important to solve a problem, and typically that problem will relate closely to market acceptance. How would a market react to a heads-up display for everyday use? The first principle in solving that problem is to create a minimum viable product or prototype that people can actually use, and from which the innovator can begin to start testing.

The second principle is to collect data, to establish feedback loops so that the product specification and design grow in response to real users. Google is doing that. All of the Explorer edition of the Glass products are connected to its data centers. It will know precisely how they have been used over what period of time, as well as relying on first-hand accounts. It will have some science to use to make decisions, but it will also have a broad assembly of data that will need creative interpretation.

Algorithmic Innovation

Since the 1960s companies in the visual content business such as TV, and latterly computing, have dreamed of flat screens. By 1977 the Japanese dominated LCD. And in the 1990s the Japanese sub-contracted some of the manufacturing of LCDs to Korean companies Samsung and LG.

This turned out to be a dangerous move. From the outset Samsung looked for ways around Japanese patents. To support that work it began hiring Russian innovation experts who had studied and worked with Altshuller.

Samsung had been reliant on Six Sigma as its development methodology. This approach is almost wholly incremental. Samsung sought out advantages by adopting other companies’ innovations and looked for minor changes that it could patent. It had a reputation as a fast follower.

Although the company continued to use Six Sigma it began to use TRIZ as well, for conceptual development work — that is, for the ingenuity and “imagineering.” Drawing on the TRIZ framework of possible solutions it used an iterative approach, running through a formalized analysis of solution models, to create its market-leading AMOLED (active matrix, organic light emitting diodes) products. OLED is becoming a display of choice for high-end phones; it can lead to thinner phones and longer battery life. And Samsung is now a world leader in display technology.

Although TRIZ is very programmatic, it represented a fundamental shift for Samsung, away from incremental gains to its emergence as a conceptual innovator capable of taking new technologies through a varied and complex development cycle.

A New Way of Making Decisions: The Ecosystem approach

Innovation is extremely important to every organization but it is shifting from a purely executive strategy concern to an everyday event. In the chaos of the Web world small companies continue to be at an advantage over larger entities simply because they can access decision support more easily. That support comes informally from the companies and peers that they interact with, and from a peer-like approach to potential customers. The computational approach to innovation mirrors that small company process. It revolves around collecting data, either through interaction or through devices that customers use, but its principles reflect what you can do if you think small and big at the same time. Integral to that process, more and more, is some form of crowd behavior — crowdsourced and increasingly crowdfunded.

And a form of algorithmic innovation, not as prescriptive as TRIZ but nonetheless thorough, lies at the heart of many technology companies whose engineering cultures seek out, first, the best use cases of a technology, and then, all the incremental improvements available once the use cases are decided.

Social decision models introduce new demands for managers. For a product or service to benefit from new innovation models, ideally, it means that the enterprise needs to learn how to attract resources rather than just provide investment. This ability to attract and retain audiences willing to invest in your future is a new management skill many will need to learn.

In sum, innovation is about the broadest possible constituency (expert groups, web-based data, big data, and crowds) yet it has to remain, at some point, a closed loop. Perhaps the most difficult aspect of change for enterprises to grasp is that it also requires different decision-making processes. That is the challenge for banks. And the opportunity.

Haydn Shaughnessy has been working on enterprise innovation for the past 25 years. He writes the popular Re:Thinking Innovation column at, which is read by over half a million people monthly. Haydn has spent the past four years thinking how innovation works, research that resulted in his book The Elastic Enterprise and in a series of papers on new decision-making requirements for firms. He led  two sessions decision-making in finance projects during Sibos, an annual conference that gathers over 8,000 banks from the around the globe.




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