One of the unintended consequences of the COVID-19 pandemic has been that we all got to experience a dry run of what I call a Machine First™ future, wherein we have been forced to ask automation and artificial intelligence (AI) technologies to help us conduct the important human endeavours of treating, feeding, creating, educating, collaborating, communicating and surviving.
People across demographics, cultures and countries have worked, studied, shopped, entertained and even sought medical assistance (via telehealth visits with their physician) using the power of connectivity, and new communication and collaboration platforms. This dry run has offered us a sneak preview into our digital future but it has also teased out, in stark sharpness, the work that needs to be done to move artificial intelligence and machine learning from the realm of proof-of-concept and pilot projects into robust, industry-scale production.
Take the example of the airlines industry, which has been so badly hit by the current pandemic. Dynamic ticket pricing should be a source of competitive advantage for airlines. However, six months into the pandemic, the machine learning algorithms of most airlines’ ticket pricing are still using historic, pre-pandemic travel patterns to churn out ticket prices, despite a period of reduced travel. This is a classic example of how AI does not, on its own, necessarily create value. We must look at AI-driven digital operating models through the lens of value creation and the interplay of the famous “people-process-technology” triad.
The Fabulous Five Elements of a Machine First Operating Model
There are five distinct elements that need coherent and holistic treatment in building out and sustaining a Machine First operating model.
The first element is strategy, because the distance between business technology and business strategy has changed to the point where, for growth-minded companies, they are inextricably linked. For a progressive, forward-looking business, technology has become a crucial element of the company’s strategy, manifested in the form of vision, goals and organization design.
Architecture — the second element — includes the “business canvas” model of a company, leveraging modern architecture patterns such as microservices in the context of security-minded design.
The third element is an agile, iterative automation, analytics and AI roadmap that is owned and sponsored by the leadership team of the company in letter and spirit.
The fourth element is governance, which spans value management, ethics and organization change management. The importance of governance in a modern digital enterprise is vastly underrecognized. A key reason many AI pilot projects do not cross over into the land of production is that the value from the pilot is not captured or articulated well — or the change management effort to make the pilot relevant to the business environment has not been realistically calibrated. (See my airline ticket price example above.) This negligence will need to change as digital acceleration picks up speed in the new beginning that will emerge post-pandemic.
The fifth element is talent and workforce management of a hybrid workforce of humans and machines. Every firm has the critical task to look at the evolution of roles in all job families as repetitive, rule-based work gets picked up by machines and bots, while the innovative jobs requiring creativity, judgement and a contextualized response benefit from human involvement. The natural human reaction to equate job change with job loss needs to be recognized and addressed with data, cross-skilling, training and — most importantly — empathy.
The time to act is now because speed-to-AI will determine the survival of the ‘Digital Fittest’.
About the author(s)
Nidhi Srivastava is Vice President & Global Head, Google Cloud Business, Tata Consultancy Services (TCS). In this role, she leads TCS’ Google Cloud partnership, guiding companies to accelerate value from their cloud transformation initiatives and cultivating digital transformation of legacy business models. She provides strategic guidance on new and emerging use-cases for enterprise cloud, helping companies achieve agility, efficiency and scale.
Nidhi has over 25 years of experience in delivering consulting solutions across industries. Prior to her current role, she led TCS’ Enterprise Intelligent Automation and AI Practice, where she guided companies to transform into agile enterprises. Nidhi has also worked with leading banking and financial services organizations to drive their digital transformation.
Nidhi has been an advisory member with the Software Engineering Institute for CMMI for Services, a member of the International Process Research Consortium at Carnegie Mellon University, and a member of the Women’s Leadership Network with AMCF. She currently resides in Chicago, Illinois.