Embedding AI in strategy
Big Data and Internet of Things (IOT) have been buzz words for the past decade. What is Big Data? With the growth of the internet, the data that is at the disposal of a corporation has also grown manifold. Big Data theorists say when data passes the three “V” test — volume, velocity and variety — it is called Big Data. There is a huge amount of data, it flows continuously and there is a great variety of data (images, words, numbers, voice, video and so on). At an event hosted by the IIM Calcutta Alumni Association, I heard Professor Sudha Ram of the Eller College of Management, University of Arizona speak about two other dimensions of
Big Data: Time and geographic stamp. Not only are corporations and government bodies getting a deluge of 3V data but they are also getting data that is location and time stamped.
I was remembering Professor Ram’s words as I started reading the book Fusion Strategy: how real-timed at a and ai will Power the industrial future by Vijay Govindarajan and Venkat Venkatraman. Professor Govindarajan is widely regarded as one of the world’s leading experts on strategy. His books Reverse Innovation and Thethree-boxsolution are must-reads for any student of strategy. Professor Venkataraman is considered one of the foremost global experts on what may be called “strategy meets digital technology”. His book Digitalmatrix— New rules for business transformation is a terrific primer on how strategy needs to be crafted keeping technology and digital technology at the centre.
All of us know that digital native companies such as Amazon, Google, Facebook and Netflix are constantly collecting and analysing a huge volume of data. They know what we are looking for and can deliver a product or a message even before we ask for it. They also know where we are and when we entered their matrix. Their ability to gather data and analyse them has been both praised and criticised.
Gathering data, analysing it, using it to sell products or getting more attention is something that made digital native companies successful. These tools are employed by e-commerce merchants, banks and financial institutions that deal with us through the digital mode.
Professors Govindarajan and Venkataraman present cases of how companies that you may call belonging to the old economy are using data and artificial intelligence (AI) tools to make lives better. Their premise is that data and AI will become essential for ensuring success in the industrial future.
Companies such as Tesla, Rolls Royce and John Deere feature multiple times in this book. Why? Simply because they are standout examples of what we know as “old economy” products that are using data and AI to deliver better value to their customers.
Every Tesla car is collecting data and sending it to the Tesla cloud all the time. This data, collected from a million-plus Tesla vehicles are analysed and Tesla cars get updated software installed when their owners are fast asleep. “The core product of automobiles is fast becoming a digital industrial product, with powerful systems-on-chip driven by millions of lines of software code,” the authors write.
What about John Deere? Isn’t that a tractor and farm equipment company? What do they have to do with data and AI? Well get ready to be surprised. John Deere is using technology to enable farmers to deliver weed killers at the right place, at the right time in a sprawling farm. John Deere’s farming equipment has cameras that scan a huge field to identify the exact places that need a dose of a powerful weed-killer.
Rolls Royce makes luxury cars. But they also make those engines that power the biggest of big aircraft. The book tells us that Rolls Royce engines are constantly sending information about their performance to the cloud. This is then analysed using AI tools to predict fuel usage and other engine parameters. Rolls Royce can help an airline reduce fuel consumption by advising on flight parameters. Three products that can belong to the old economy, but which are behaving like they are digital native organisations. The book is full of many such examples drawn from what may be construed as “industrial” companies.
The core of the book is the 2 by 2 grid: Richness of data vs reach of data graphs, the latter being graphs that “capture relationships, links and interrelationships between a company and its customers through product-in-use data and are fundamental building blocks of fusion strategy”. The grid consists of four quadrants: Fusion Products, Fusion Services, Fusion Systems and Fusion Solutions.
Fusion Products are single products that collect data to provide machine efficiency. Fusion Services are single products that offer better results to customers, customer outcomes get impacted for the better. Fusion Systems are where there are multiple interlinked products that together offer smart systems. Finally, Fusion Solutions are where we see multiple interlinked products that work together to offer custom solutions.
The authors present a strong case for Fusion Strategy and how it is different from what we see as traditional strategy. Data and AI will enable corporations to deliver better results to their customers and in the bargain score over those competitors who are not able to harness the amazing potential of Data and AI. This compact book is a must read to understand how Data and AI can be of help to you, irrespective of the industry in which you operate.