The biggest changes from data and AI will come where you least expect it

The focus with artificial intelligence has been on its potential impact for knowledge workers in sectors where digital technology is already well-entrenched such as financial services and research and logistics. But two experts in innovation and digital strategy argue the biggest impact will be where we aren’t paying much attention: Traditional manufacturing, mining, transportation, construction, logistics and health care, which account for about 75 per cent of the world’s gross domestic product.
“Those sectors haven’t been significantly changed by digital technology yet. But they will be affected. Soon,” Vijay Govindarajan, a professor at Dartmouth College, and Venkat Venkatraman, a professor at Boston University, write in Fusion Strategy: How Real-Time Data and AI Will Power the Industrial Future Hardcover.
The profound changes of recent decades have been primarily in asset-light, high-information sectors, where businesses sell to consumers. But now we are likely at a new inflection point, where advances in hardware, cloud data, algorithms, generative AI and mixed reality will transform asset-heavy industries. We have been told, of course, for years this will happen – aren’t our refrigerators by now supposed to be automatically ordering milk or lettuce from the supermarket when we run out? – but the academics believe after several false starts the industrial world’s digitalization will now begin in earnest.
Industrial products will be infused with sensors, software and real-time telematics. The two academics say it is already happening in large companies they have studied and will gain momentum. “Going forward, industrials will need to combine what they do best (create physical products) with what digitals do best (use AI to parse enormous, interconnected, product-in-use datasets) to make strategic combinations that would otherwise be impossible,” they write.
That combination led them to dub it fusion strategy. They point as an example to John Deere, where its See & Spray device has allowed targeted spot spraying rather than just blanket application across fields. A self-propelled machine that moves through fields at up to 25 kilometres an hour, its 36 cameras scan at high speeds and the vision processing units use deep learning to distinguish crops from weeds. A command is sent to a nozzle and the weed is killed. “The innovation here is not in industrial machinery. It’s in merging the digital and industrial domains with data and AI – a significant shift for a company like Deere that has designed only big industrial machines in the past,” they stress.
At the heart of this effort is data – real-time information about products currently in use. It will be assembled in “datagraphs,” which will capture the interrelationships between the company and customers. The information is dynamic – live – not static nor historical, and assisted by algorithms will enable greater understanding and action.
Managers have to become adept at overseeing this new interlocking of the physical and digital business domains. “The modern automobile is a computer on wheels that’s connected to the cloud. Tractors are becoming industrial machines driven by intelligent agronomists. The latest buildings are architectural marvels with autonomous control systems,” Profs. Govindarajan and Venkatraman write. Agriculture’s future, they predict, will be sustainable farming enabled by sensors and software. In medicine, we will see personalized health care with biomarkers and customized cures. Education will be transformed by personalized tutoring supported by AI. New advances are making it all economically and technologically possible.
Karl Marx highlighted labour as the key factor of productions, but interestingly China, led by a communist party, in 2020 recognized data as a new factor of production. “All industrial products will become digital sooner than companies think,” the academics warn.
Executives have been taught over the past 40 years through Harvard professor Michael Porter’s research that they need to seek success through one of three routes: Beating competitors on costs, differentiating themselves significantly from others or being highly specialized with a niche product. Profs. Govindarajan and Venkatraman suggest instead the new strategic choice leaders must make will be between four fusion battlegrounds, picking one as the main area to pursue this blending of machine and data even while they explore the others:
If we are at a takeoff point, executives in the sectors that have been less affected so far by the digital revolution will have to get up to speed, using these ideas and frameworks to rethink their strategy.
Cannonballs
Harvey Schachter is a Kingston-based writer specializing in management issues. He, along with Sheelagh Whittaker, former CEO of both EDS Canada and Cancom, are the authors of When Harvey Didn’t Meet Sheelagh: Emails on Leadership.