It has been nine years since the publication of our book “8 steps to innovation: going from jugaad to excellence”. In a fast-paced world where technology becomes obsolete every two-three years, nine years is a long time. My co-author Prof. Rishikesha Krishnan has been nudging me and suggesting that we should re-look at the framework, especially in the context of the digital era. Is the framework still relevant? Here is an attempt to sketch some initial thoughts on this topic. The attempt is clearly biased and criticism is more than welcome.
Relevance of pipeline-velocity-batting average: The framework addresses the question, “How to become more innovative systematically irrespective of strategy, size, sector, and culture?” This question is more relevant for organizations and teams and less relevant for individuals. The framework divides the main question into three sub-questions: How to build an idea pipeline? How to improve idea velocity? And how to enhance batting average? The pipeline problem addresses the generation of a constant stream of business-relevant ideas. Velocity problem explores validation of various assumptions associated with the ideas and finding relevant resources including investors for the promising ideas. Batting average problem looks at increasing the chance of success for big bets while building a margin of safety.
Are pipeline, velocity, and batting average problems still relevant in the digital era? I have been presenting these sub-questions to MBA students and corporate executives. And nobody has questioned the relevance of any of these sub-questions. These questions were relevant when Thomas Edison was running his invention factory more than a hundred years ago and are relevant for the innovation engine at Mahindra today. Even a corner grocery shop if it plans to do systematic innovation would have to address these questions. So then, what has changed?
The eight steps are responses to these three questions. First three steps address the pipeline problem, the next three steps address the velocity problem, and the final two address the batting average problem. Let’s see how the relevance of each step changes in the digital era.
Pipeline problem: (Step-1, 2, 3)
Step-1: Laying the foundation: This step involves setting up the core processes like idea management process, buzz creation process, and learning and development process. It also involves establishing clarity on the scope, source, and sponsorship of innovations. I feel these things are not affected in the digital era. Any organization that is serious about innovation has these in place in some form or the other.
Step-2: Create a challenge book: This step emphasizes the creation of a challenge book and establishing collective clarity around it. The digital era has created new metaphors like Uber (marketplace), Tesla (EV, semi-autonomous, over the air upgrades), Zomato (home delivery), Amazon (shopping convenience), Paytm (mobile wallets), etc. In the past few years, we have seen new waves like the pandemic, sustainability-related regulatory norms, electric vehicles, cryptocurrency, machine learning, etc. gaining momentum. All these metaphors and waves contribute to building a challenge book. However, in my opinion, the relevance of challenge book doesn’t go away. In fact, it becomes more relevant because in a world of ever-increasing distractions, challenge book can bring focus to the innovation efforts.
Step-3: Build participation: This step assumes that navigating complex challenges may benefit from participation, the way it happens in a café or a conference. The assumption remains relevant in the digital era. However, the digital era highlights the importance of the customer experience dimension. With steps like search, discovery, comparison, selection, payment, delivery, and returns associated with online shopping, end-to-end experience has become increasingly important. Moreover, this cuts across the shopping of products like mobile phones, grocery items, and services like blood testing and banking. Hence, a methodology like design thinking which puts experience design at its center and weaves empathy, participative problem solving, and experimentation in an iterative manner has gained significance.
Velocity problem: (Step-4, 5, 6)
Step-4: Experiment at low-cost with high speed: The digital era has seen the emergence of new tools – computational modeling tools, simulators, 3D-printers, etc. Many of these tools are now available on cloud making them easily accessible at low-cost. They are helping idea authors test their ideas or at least some assumptions associated with their ideas with less cost and at high speed. For consumer-facing digital applications, A/B testing – a form of randomized controlled experimentation – has become an important mechanism for testing ideas. No matter what the technology or tools, the relevance of low-cost high-speed experimentation hasn’t diminished over the years.
Step-5: Find a champion: This step is based on the assumption – An idea either finds a champion or dies. Is the assumption still valid? Very much. Finding a strategic customer who endorses the idea or an investor who supports the development of the idea continues to be important today. Social media has helped ideas authors find champions by publicizing their idea through videos. Programs like Shark Tank are creating platforms for start-ups to find investors and/or mentors.
Step-6: Iterate on the business model: As the relevance of data increased, so did the importance of business models that leverage data. Dental insurance company Bento partnered with Philips which manufactures electric toothbrushes. This is because having the data on how many times a person brushes his teeth would help determine his dental insurance. EV companies like Ather Energy began to unbundle their product offering and started selling batteries separately as a subscription. Banks began to offer Buy-Now-Pay-Later (BNPL) payment option as an alternative to credit cards. Business model innovation continues to be an important lever for digital businesses.
Batting average problem: (step-7, 8)
Step-7: Build an innovation sandbox: Exploring big bets is inevitable for any company that is serious about survival. Google explores self-driving cars, Amazon experiments with Just-walk-out stores, and Facebook bets big on virtual/augmented reality. Small firms may have to consider automation and analytics seriously. The challenge is you can’t bet on all the big trends, you will have to choose. And even after choosing a trend, you may not know how this trend may lead to a new offering. You need to identify a few use-cases, invest in building experimentation infrastructure, and perform a large set of experiments to see what is both meaningful in your context and promising enough. In short, you need to build an innovation sandbox, unless you choose to acquire the innovation. Building an innovation sandbox is neither low-cost nor a short-term project. Technology platforms may speed up the process and open innovation may help in connecting ideas from remote corners of the world. I haven’t seen its relevance diminished.
Step-8: Build a margin of safety: Big bets bring risky exposures. You can’t have one and not the other. Datacenter outages are a given once you adopt the cloud. If you are a bank and if you don’t worry about managing data center outages you will be in trouble sooner or later. HDFC Bank learned it the hard way. Shakespeare knew that a pound of flesh is a risky promise for the Merchant of Venice. And V G Siddhartha, the founder of Café Coffee Day was expected to know how much debt is enough. This step – building a margin of safety – could very well be the most challenging step to internalize. And it is evergreen.In short, from my biased perspective, the core problems raised in the book – pipeline, velocity, and batting average are still relevant in the digital era. And 8-step responses are relevant too. However, your input is welcome and it is possible that I am missing something here.
Adding comments from friends given on LinkedIn platform:ReplyDelete
Zunder Lekshmanan: My observation is knowing when to quit is part art and part science. That is the toughest part in any kind of innovation journey.
Muthuswamy M R:
While velocity is important, the quality is more critical. Ideas which addresses a specific problem are more successful. Many times I find that people are not able to even define a proper use case for the idea. You may want introduce a robust and unbiased validation process after foundation.