In a five year multistage study the authors learned how leaders can get the most out of . The secret to making this work, they found, is the business model itself, where machines and humans are integrated to complement each other.
Copyright by hbr.org
Too many business leaders still believe that is just another ‘plug and play’ incremental technological investment. In reality, gaining a competitive advantage through requires organizational transformation of the kind exemplified by companies leading in this era: Google, Haier, Apple, Zappos, and Siemens. These companies don’t just have better technology — they have transformed the way they do business so that human resources can be augmented with machine powers.
How do they do it? To find out, we conducted a multistage study over five years, beginning with a survey of senior managers and executives, followed by interviews and surveys across a wide range of industries to identify technology implementation strategies and barriers, and in-depth studies of five leading organizations. Our key takeaway is counterintuitive. Competing in the age of is not about being technology-driven per se — it’s a question of new organizational structures that use technology to bring out the best in people. The secret to making this work, we learned, is the business model itself, where machines and humans are integrated to complement each other. Machines do repetitive and automated tasks and will always be more precise and faster. However, those uniquely human skills of creativity, care, intuition, adaptability, and innovation are increasingly imperative to success. These human skills cannot be “botsourced,” a term we use to characterize when a business process traditionally carried out by humans is delegated to an automated process like a or an algorithm.
How do leaders get the most out of ?
From our research we have developed a four-layer framework that shows organizational leaders how they can create a human-centric organization with super-human intelligence. The four layers are not “steps,” which would imply a sequential progression. The four layers of intentionality, integration, implementation, and indication (the Four I model) must be stacked all together, or else the use of will fail to deliver a sustainable competitive advantage. Here’s how it works.
The first layer of the Four I model is intentionality of purpose, beyond the mere pursuit of profits. An intentional organization knows why it matters to the world, not just its shareholders. A good example of intentionality in the use of comes from Siemens, which evolved from a shareholder-profit-maximizing power generation and transmission company into a leading provider of electrification, automation, and digitalization solutions with energy-efficient, resource-saving technologies driven by and the Internet of Things (IoT) in service to society. This cultural shift toward a higher human-centric purpose impacted not just marketing and product design but also the strategic decision to, as Scott D. Anthony, Alasdair Trotter, and Evan I. Schwartz wrote for HBR, “divest its core oil and gas business and redeploy the capital to its Digital Industries unit and Smart Infrastructure business focused on energy efficiency, renewable power storage, distributed power, and electric vehicle mobility.” While financial performance and shareholder value will always be important, creating human-centered, technology-powered organizations will actually drive financial performance in the age of .
To that end, Siemens is launching a combination of hardware and software that enables throughout its Totally Integrated Automation (TIA) architecture, an approach that aligns Siemens’ mission with its strategy. The TIA architecture uses as a bridge that spans from corporate headquarters out to industrial end users. Siemens’ proprietary “MindSphere” is a cloud-based IoT operating platform that reaches into Siemens’ industrial user-operated controller and field device products. The MindSphere’s neural processing unit module allows human users to benefit from Siemens’ in-house capabilities, while also enabling human users to impart their own experience to train the machines. According to Siemens Factory Automation specialist Colm Gavin, “With we are able to train, recognize, and adjust to allow more flexible machinery. Because, do we want 10 machines to package 10 different types of products, or a tool that accommodates different packages and different sizes and automatically adjusts to the new format?” Smarter machinery with TIA architecture leverages to advance the company’s intentionality, while increasing flexibility, quality, efficiency, and cost-effectiveness for its end users. […]
Read more: hbr.org