4IR: The Invisible Revolution – Part Three

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4IR and IA

Part 3: 4IR and business transformation

Types of AI

There are roughly two categories of artificial intelligence. One is weak, the other is strong. Both are transformational technologies. Weak AI mimics human cognition to automate mundane, repetitive tasks, eliminating human error and labor costs. In order to make computing indistinguishable from human intelligence, strong AI must include learning, recognizing external objects, and being self-aware. Strong AI is still an aspiration, and some would argue that it is unattainable. Those companies whose business models are inextricably linked with 4IR, are on the path to strong AI.

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4AI in the real world

4IR has led to the development of smart factories by combining IoT, cloud computing, analytics, AI, and machine learning. To support the production process, enterprise resource planning software integrates planning, purchasing inventory, sales, marketing, finance, human resources, and more. The use of sensors on factory floor can still revolutionize workflow, eliminate errors, suggest improvements, predict maintenance needs and conduct quality control through automated visual inspection. Aircraft, for example, have up to 6 million parts. It doesn’t take many of them to be faulty or late for a factory to grind to a halt. Managing all business functions at this level of complexity is impossible without 4IR technologies.

In traditionally low-tech industries like construction, project workflows are increasingly assessed using similar models. As a result, project managers are able to track progress in real time, compare minute details and measurements to architectural plans, and suggest where to place human resources to maximize efficiency. As a result, advanced robotics will be increasingly used in the construction industry.

A report for the Harvard Business Review argues that ‘after hundreds of years of incremental improvements to the industrial model, the digital firm is now radically changing the scale, scope, and learning paradigm. AI-driven processes can scale up much more rapidly than traditional processes can.’ So it would appear that the law of diminishing returns may not apply to 4IR processes. That is truly revolutionary.

Case Study

Amazon has embraced this process more than any other company. Videos of its smart warehouses where robots faultlessly process your orders are familiar to everyone. But Amazon’s place in the world’s top five most valuable companies reflects its early adoption of an AI model that overturned the traditional silo approach of corporate computing. There, data is segmented based on the needs of the immediate users instead of flowing throughout the entire enterprise. From its beginnings as a product recommendation service, Amazon utilizes each of its hundreds of millions of collective customers’ data points to offer an effective, customized service. Insights from one part of the organization are injected into the bloodstream of the entire organization, allowing it to branch out into what might seem counterintuitive business activities.

With its purchase of Whole Foods and creation of Amazon Go stores, Amazon is bringing its online activities back into the physical world. Using 4IR, a seamless experience is created before, during, and after each visit. Shoppers are guided and automatically charged by the stores using a wide variety of technologies. Sensors and cameras are present to regulate the process, and AI analyzes the data to provide insights into how to improve efficiency and customer experience. These sensors and cameras also act as a laboratory for data mining, from inventory control to product trends to yet more data points.

Amazon smart warehouse

Cooperation for the benefit of all

Amazon has further profited by selling this technology to others, including competitors. This trend in co-opetition is not new. Despite a perfect climate for Despite its vineyards, Australia was mostly a country of beer drinkers. Wine Australia was created to promote the industry. Before the 1960’s, Australian wine drinkers were relatively unsophisticated, having become used to sweet, low-quality wines. Producers also introduced industry standards for quality and consistency of products. In this way, they established a symbiotic relationship between the advancing palates of Australian wine consumers and the advancing quality of Australian wine. The wine industry in Australia is now global, and Australians are among the highest consumers of wine per capita.

In the tech world, such tendencies can be seen between bitter rivals such as Apple and Samsung, which provide Apple with their iPhone screens. A disadvantage of 4IR, especially for smaller companies, is the time required to gather and analyze data to get meaningful conclusions from their investment. While leaders such as Amazon assemble data from many sources and use it across their businesses, partner organizations, and competitors to draw deeper insights from their algorithms.

As a result of 4IR, traditional organizational and management practices are also being revolutionized as decision-making becomes increasingly automated and precise. This results in the democratization of 4IR, which shares insights and leads to previously unexplored places. It is unfortunate, however, that this very democratization, which involves the dismantling of silos, cloud computing, and other technologies, leaves them open to possible interference.

Read the 4th and last part of the 4IR story

About the author:

Gaurav Sharma is the Vice President of North America at HUB Security. Gaurav is responsible for developing go-to-market strategies and partnerships in the field of Confidential Computing Security. He has advised large and high-growth businesses on emerging trends in cybersecurity during his tenure at Deloitte, CA, IBM and various startups. He holds a master’s degree in Computer Science and is passionate about establishing digital trust to unlock the potential of new technologies. His other love for music has brought him to Los Angeles.

Gaurav Sharma

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