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How Corporations Can Manage New Ventures and University Projects as Extensions of their Advanced Product Development

Professor Ikhlaq Sidhu and I recently started talking about how the interest of corporations in the innovations created by startups is leading to changes in corporate R&D models, an area he has been studying for some time. As we continued our conversations we felt that it will be important to start publishing some of our thoughts. This is the first of what we hope to be a series on posts on how startup innovation is impacting corporate R&D models.  Please also see.

The World of Innovation Has Changed

A great deal has changed in corporate innovation since the days of Bell Labs and Xerox PARC.   While these models of advanced work led to so many innovations and created tremendous broad economic value, though not always to the lab’s corporate owner, it is clear that large scale, insulated corporate research is no longer the most common model for entering new markets or developing technologies of the future. Even Alphabet is re-evaluating the mission of Google X.

What has changed? For most companies, open innovation and new venture acquisitions have become extensions of the firm’s advanced R&D portfolio. At the same time entrepreneurs and their investors have become much more effective and skilled at efficiently creating new startups and bringing technology and business model innovations to market. And finally, a significant fraction of University lab work has now evolved from the traditional “publish or perish” model to one that is today closer to demonstration, design-oriented, and more applied than ever before.

All of these changes are now converge towards a new model for creating and managing portfolios of innovation.

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Automakers Must Partner Around Big Data

By extensively utilizing data, and paying attention to detail Tesla has changed the conversation on the type of personalized experience car owners (drivers and passengers) should expect from an automaker. In the process, it is building strong loyalty with the owners of its cars who appear willing to support it through thick and thin. Tesla has taken a lesson from Apple, Google, Facebook and Amazon, four companies that obsess about connecting pieces of data and using it to better understand their consumers and tailor their services to provide the right experience. It is this personalized experience that Tesla offers that has allowed it to build a brand that delights its customers. The exploitation of big data that is generated by vehicles, consumers and companies across the entire automotive value chain must become a key competence of all automakers. But as I discussed in previous posts of this series, with the possible exception of GM through its OnStar service, (and here) only recently have started to collect and utilize these types of big data (and here). As a result, they don’t capture data of sufficient scale and they are not best in class yet at exploiting big data. In this post I argue that automakers should accelerate their partnerships with companies that have strong data collection and exploitation DNA as Tesla has already demonstrated is possible. As mobility services are starting to play an increasingly important role in transportation solutions, companies that offer such services become ideal partners to automakers. By partnering with them, automakers will be able to better understand their customers in far greater detail than they do today, as well as mobility services, which threaten to disrupt them. Ridesharing and carsharing companies represent the best initial candidates for such partnerships because these companies a) are collecting and utilizing consumer big data with the same attention and rigor as Apple, Google, Facebook, and Amazon and b) have already collected impressive data sets due to the scale they have achieved. Apple’s just announced investment in Didi Chuxing (and here), in addition to the broad implications to Apple’s services in China, e.g., ApplePay, is a further indication that data partnerships even among companies that are some of the best in class, can be essential for developing next-generation transportation solutions, including autonomous vehicles.

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The Automotive Industry’s Big Data Challenge (Part 2)

In this first part of this two-part series, I discussed why the automotive industry, particularly the incumbent OEMs, is facing a big data challenge.  This challenge is becoming extremely acute as a result of the increasing adoption of EAC vehicles combined with Mobility Services (EAC+MS) and the torrent of data that will be generated as a result of this adoption.  

In this post, I present how the incumbent OEMs can address this challenge.  To do so, automakers must:

  1. Think strategically and own the big data strategy.  They must then drive the execution of this strategy instead of relying on their suppliers for partial solutions
  2. Revamp the vehicle’s computer system architecture to create a unified computing and big data architecture.
  3. Establish and enforce data ownership rights among the appropriate constituencies.
  4. Create a data-sharing culture.

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The Automotive Industry’s Big Data Challenge (Part 1)

In the not too distant future, automakers won’t be evaluated just on the physical, safety and performance characteristics of their vehicles.  Instead incumbent and next-generation automakers will be evaluated based on the completeness of their solution along five dimensions: Electric, Autonomous, Connected, Mobility Services (EAC+MS), and Information.  We read about the progress automakers and their suppliers are making along the first four dimensions.  There is much less conversation about the fifth dimension.  In this two-part series, we will discuss the big data challenge facing the automotive industry.  The pieces are the result of my work in the industry helping corporations with their innovation and big data strategies.  The first post provides the why and makes two points:

  1. Automakers must be in the information business.  To be effective in the information business, automakers must change their perspective and start thinking about an overall process for big data in and around the car.
  2. Information in EAC+MS implies big data and incumbents in the automotive ecosystem must become serious about big data.  Newcomers to the automotive industry such as Google, Tesla, Faraday Future, and likely Apple, but also Uber, and Lyft, realize this imperative.

The second piece will provide the how to try to address this challenge.

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How to Set Up a Corporate Innovation Outpost That Works

This is the fourth in a series about corporate innovation co-authored with Steve Blank. Steve and I are working on what we hope will become a book about the new model for corporate entrepreneurship. Read part one on The Evolution of Corporate R&D, part two on Innovation Outposts in Silicon Valley, and part three The 6 Decisions to Make Before Setting up an Innovation Outpost.

In our last post, we addressed the six key questions that senior management should address to determine if an Innovation Outpost makes sense for a company. If the answer is yes, here’s a step-by-step guide to help set one up.

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How to Avoid Innovation Theater: The Six Decisions To Make Before Establishing an Innovation Outpost

This is the third in a series on the changing models of corporate innovation co-authored with Steve Blank. Steve and I are working on what we hope will become a book about the new model for corporate entrepreneurship. Read part one on the Evolution of Corporate R&D and part two on Innovation Outposts in Silicon Valley. 

Corporate Leadership’s Innovation Outpost Decision Process

Today, large companies are creating Innovation Outposts in Innovation Clusters like Silicon Valley in order to tap into the clusters’ innovation ecosystems. These corporate Innovation Outposts monitor Silicon Valley for new innovative technologies and/or companies (as emerging threats or potential tools for disruption) and then take advantage of these innovations by creating new products or investing in startups.

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Innovation Outposts in Silicon Valley – Going to Where the Action Is

This is the second in a series on the changing models of corporate innovation co-authored with Steve Blank. Steve and I are working on what we hope will become a book about the new model for corporate entrepreneurship. Read part one on the Evolution of Corporate R&D.

Innovation and R&D Outposts

For decades, large companies (see Figure 1 below) have set up R&D labs outside their corporate headquarters, often in foreign countries, in spite of having a large home market with lots local R&D talent. IBM’s research center in Zurich, GM’s research center in Israel, Toyota in the U.S are examples.

These remote R&D labs offered companies four benefits.

  • They enabled companies to comply with local government laws – for example, to allow foreign subsidiaries to transfer manufacturing technology from the U.S. parent company while providing technical services for foreign customers
  • They improved their penetration of local and regional markets by adapting their products to the country or region
  • They helped to globalize their innovation cycle and tap foreign expertise and resources
  • They let companies develop products to launch in world markets simultaneously

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