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Even when machine intelligence outperforms human workers, when it replaces a human colleague, group productivity falls, a new study found. In addition, the output of people who are simply observers is negatively affected. Managers need to think carefully about the impact that AI has on team sociability, motivation, and trust.

AI is intimidating employees. As machines perform intellectually demanding tasks that were previously reserved for human workers, people feel more excluded and less necessary than ever. The problem is only getting worse. Eighty percent of organizations say their main technological goal is hyperautomation-or the complete end-to-end automation of as many business processes as possible. Executives often pursue that goal without feedback from employees-the people whose jobs, and lives, will feel the greatest impact from automation. In this article the author examines what keeps leaders from involving rank-and-file employees in AI projects, how they should model inclusive behavior, and what organizations must do to develop employee-inclusive AI practices. Those practices will make companies more likely to improve long-term performance-and to keep their employees happy, productive, and engaged.

Despite all the Terminator scenarios, many commercial applications of artificial intelligence are really just as an essential business tool.

What do organizations that successfully unlock AI's strategic potential have in common, according to recent research? Executives with high AI literacy. Many leaders will be uncomfortable with the need to continually learn about artificial intelligence as the technology evolves, but executives and board members need to embrace that discomfort and make it a habit. This article shares three ways to put that habit into action.

The year was 2021. At his Gurugram office in Haryana, India, Anil Chaudhry, Zone President and Managing Director of Schneider Electric India Private Limited (SEIPL), sat working on his keynote address for a conference. He had been invited to present a globally relevant business case on energy-efficient practices for digital transformation and their application in the larger business ecosystem. SEIPL was a subsidiary of Schneider Electric (SE). The French industrial giant SE was a global leader in energy management and industrial automation and delivered solutions spanning hardware, software, and services. SE was also among the Fortune Global 500. Chaudhry was keen on showcasing the company's success in implementing digital transformation. The SE audit team found that SEIPL's Hyderabad factory (in Telangana) pioneered automated tools in 2016 when terms such as "Industry 4.0" and "digitalization" were thin on the ground. The factory successfully converted a brownfield facility into a smart factory in 2019, implementing EcoStruxure, a platform for accelerating digital transformation.Chaudhry had to address multiple questions based on the learnings and takeaways from the implementation at Hyderabad: Why did the need for creating a smart factory arise? How did this lead to digital transformation across the value chain? Were such implementations scalable? Could other industry players replicate this success by converting their non-IIoT -enabled facilities into smart factories? What aspects did organizations need to focus on while planning for such a transformation?

The year was 2021. At his Gurugram office in Haryana, India, Anil Chaudhry, Zone President and Managing Director of Schneider Electric India Private Limited (SEIPL), sat working on his keynote address for a conference. He had been invited to present a globally relevant business case on energy-efficient practices for digital transformation and their application in the larger business ecosystem. SEIPL was a subsidiary of Schneider Electric (SE). The French industrial giant SE was a global leader in energy management and industrial automation and delivered solutions spanning hardware, software, and services. SE was also among the Fortune Global 500. Chaudhry was keen on showcasing the company's success in implementing digital transformation. The SE audit team found that SEIPL's Hyderabad factory (in Telangana) pioneered automated tools in 2016 when terms such as "Industry 4.0" and "digitalization" were thin on the ground. The factory successfully converted a brownfield facility into a smart factory in 2019, implementing EcoStruxure, a platform for accelerating digital transformation.Chaudhry had to address multiple questions based on the learnings and takeaways from the implementation at Hyderabad: Why did the need for creating a smart factory arise? How did this lead to digital transformation across the value chain? Were such implementations scalable? Could other industry players replicate this success by converting their non-IIoT -enabled facilities into smart factories? What aspects did organizations need to focus on while planning for such a transformation?

Why can automated systems that rely on artificial intelligence be a deadly liability in situations unforeseen by the designers?

Since the launch of ChatGPT, interest and investments in generative AI technologies have surged. Which players stand to gain the most from the growth and adoption of this technology? Analyzing the factors underlying generative AI allows us to identify points of differentiation. Given that the technology is resource and data intensive and benefits from network effects, incumbents have the greatest advantage; however, there are some areas where new entrants can create and capture value.

The author, Honeywell's CEO until June 2023, writes: "There's zero chance that we would have made it through Covid, inflation, the upswing in geopolitical risk-all the challenges we faced over the past several years-had we not developed a transparent and coherent digital strategy." In this article he describes the company's multifaceted transformation from a diversified industrial conglomerate into an integrated company. Internally, the goal was to improve productivity, transparency, and agility with centralized data to guide business decisions. Externally, it was to harness all the information generated by systems Honeywell sold in order to offer clients a whole new kind of value: a means to track emissions, save energy, improve safety, and more. First the company had to simplify and reorganize its infrastructure, going from 150-plus enterprise resource planning systems (ERPs) to 10, from 1,700 websites to fewer than 100, and from 2,700 applications to fewer than 1,000. Next it had to define master data for products, employees, customers, and many other things. Then it developed a strategy for putting all that data to work. By running the monthly meetings and the operating reviews himself, Adamczyk sent a message to everyone: "I care about this transformation personally, and we're committed to this path."

When it launched GPT-4, in March 2023, OpenAI touted its superiority to its already impressive predecessor, saying the new version was better in terms of accuracy, reasoning ability, and test scores-all of which are AI-performance metrics that have been used for some time. However, most striking was OpenAI's characterization of GPT-4 as "more aligned"-perhaps the first time that an AI product or service has been marketed in terms of its alignment with human values. In this article a team of five experts offer a framework for thinking through the development challenges of creating AI-enabled products and services that are safe to use and robustly aligned with generally accepted and company-specific values. The challenges fall into five categories, corresponding to the key stages in a typical innovation process from design to development, deployment, and usage monitoring. For each set of challenges, the authors present an overview of the frameworks, practices, and tools that executives can leverage to face those challenges.

Despite a multitude of initiatives in cross-sector collaboration and open innovation, businesses still struggle to support the development of game-changing ideas. The authors argue that the solution to this problem starts by recognizing an important truth: Innovation is about more than an idea-it is a long, collaborative journey. Innovation is more likely to succeed when it is a curated process in which an intermediary takes responsibility for sparking and sustaining collaboration among the people involved. In this article, the authors demonstrate how intermediaries can support collaboration throughout the entire innovation journey. First, they provide opportunities for innovators to make connections. They don't simply bring people together; they carefully select participants and curate the experience to generate the best results. Second, they help innovators develop relationships, using structured follow-ups and offering holistic mentoring. And third, they help entrepreneurs sustain their relationships and manage the team over time as the collaborations evolve and team members change.

Until recently most incumbent industrial companies didn't use highly advanced software in their products. But now the sector's leaders have begun applying generative AI and machine learning to all kinds of data-including text, 3D images, video, and sound-to create complex, innovative designs and solve customer problems with unprecedented speed. Success involves much more than installing computers in products, however. It requires fusion strategies, which join what manufacturers do best-creating physical products-with what digital firms do best: mining giant data sets for critical insights. There are four kinds of fusion strategies: Fusion products, like smart glass, are designed from scratch to collect and leverage information on product use in real time. Fusion services, like Rolls-Royce's service for increasing the fuel efficiency of aircraft, deliver immediate customized recommendations from AI. Fusion systems, like Honeywell's for building management, integrate machines from multiple suppliers in ways that enhance them all. And fusion solutions, such as Deere's for increasing yields for farmers, combine products, services, and systems with partner companies' innovations in ways that greatly improve customers' performance.

Tesla, an extraordinary innovator in the automotive industry and pace-setter for manufacturing electric vehicles (EVs), was founded in 2003 by Martin Eberhard and Marc Tarpenning with the vision of creating a car manufacturer that was also a technology company. Tesla set out to integrate innovative battery technologies, computer software, and a proprietary electric motor system to produce highly attractive, zero-emission vehicles. The case introduces the history and challenges faced by the early EV industry and how Tesla reshaped it, revolutionary innovations initiated by Tesla, and its sustaining strong performance. The challenges of traditional automotive companies' transitions to EVs is discussed, especially with regard to driving range and charging, and how Tesla changed the game by introducing its Roadster in 2008. During the next 14+ years Tesla achieved the distinction of producing the first best-selling global vehicle powered by batteries. Included in the case is how Tesla questioned traditional assumptions in the automotive business model - and how it leveraged digital technology capabilities to introduce the next practices in vehicle design, manufacturing, sales and service, pricing, and customer engagement. The case explores the transformation in customer experience and vehicle management as Tesla made its automobiles "connected" with software and sensors, and introduced new capabilities in remote tracking, vehicle intelligence, remote servicing, and the potential for autonomous driving. The case concludes by highlighting some of the challenges Tesla faced with wide adoption of EV technology including the market valuation of its stock, and the emergence of several competitors such as new EV manufacturers and traditional automakers switching to EV.

As workforce demographics skew older, managers must attend to the well-being and job performance of workers age 60 and over. Age-related cognitive changes and complex technology, in particular, can hinder older workers’ job performance, but managers can take a number of actions to better support a multigenerational workforce.

Spreadsheet for case 622048. Includes data that can be used for students to conduct data analysis on.

Past research into enterprise collaboration tools suggests they suffer because not enough people contribute. The authors point to the opposite problem: Too many people mimic consumer social media and post mainly to enhance their reputation, while too few are willing to engage with the contributions of others. The authors identify four types of participants and propose strategies for encouraging more social interaction and less self-promotion.

When COVID-19 struck in 2020, Singapore responded swiftly with containment and mitigation measures to curb community spread. Underlying the city-state's quick public health response was an all-of-government approach characterised by decisive actions, rigorous surveillance, and prompt adaptation. Additionally, harnessing advanced healthcare technologies, such as artificial intelligence (AI) and data analytics, supported these efforts. Chatbots and automated instant messaging communications, as well as dissemination of information via traditional and social media, helped the public make sense of the uncertainties during the early days of the outbreak. As the pandemic progressed, digital contact tracing and even a robot dog were roped in to complement community surveillance measures as the country fought the war against COVID-19. More importantly, AI-enabled technologies and analytics played a vital role in disease diagnosis and prognosis as well as in supporting the research community in understanding the epidemiology of the novel coronavirus, predicting its evolution, and planning healthcare capacity. In 2023, WHO finally declared the end of COVID-19 as a global health emergency. However, the enduring effects of the pandemic persisted and continued to take a toll on the healthcare sector and on non-COVID-19 patients who had delayed medical care. Part of the burden of endemicity entailed living with the consequences of decisions made to prioritise hospital resources for COVID-19 patients, while non-urgent surgeries were either cancelled or postponed. Addressing the post-pandemic collateral damage became a pressing need. But how? Could AI, data analytics and other advanced technologies contribute to resolving this new healthcare challenge?

In 2019, the director of the newly minted Digital Services Lab at JOB Co. found himself on the brink of a crucial meeting with the company's chief executive officer. The focal point of their impending discussion rested on the stagnation of progress in the Mentor 2.0 project-an initiative conceived to embody an agile paradigm in digital transformation, championed by an inventive Scrum team. Despite the project's noble intentions, the journey towards agility had proven to be riddled with formidable challenges for the team. The director, acutely aware that the destiny of Mentor 2.0 was intricately intertwined with the prosperity of the Lab, the linchpin of JOB Co.'s digital metamorphosis, grappled with the necessity of explaining the situation accurately. The imminent meeting stood as a decisive moment for him to carefully consider the most effective strategy. He understood that the future path of the Mentor 2.0 project held significant implications for the Lab-a pivotal force guiding JOB Co.'s digital evolution.

Launched in 2019 by Indian technology giant Infosys, Helix reduced administrative costs associated with insurance claims in the US healthcare industry. The vast US healthcare industry required a seamless insurance membership experience-one that comprised administrative functions that were easily disrupted during the COVID-19 pandemic. Digitising such functions saved insurers administrative costs, saw revenue growths, and helped during erratic surges in demand. Most insurers tried incremental upgrades to digitise ageing platforms that were at least 20 years old. However, by the time the upgrades were completed, the platform was considered outdated once again. As a solution, many technology companies offered either 'one-size-fits-all' or extremely niche solutions. Noting the gap in the market for adequate solutions, Helix developed an AI-driven, 'software-as-a-service' (SaaS), modular technology. With this, independently-functioning administrative processes could be integrated into ageing systems, one process at a time. Eventually, Helix aimed to replace the client's core administrative functions. Despite its capabilities, getting client's employees to implement the Helix software into their workflow was hard. Nevertheless, the company persevered, and by May 2023, 5,000 client employees were using Helix in their workflow. Now, Vadiraj Guttal, Infosys Healthcare's AVP and Head of Platforms wondered how his team could better convince potential clients that Helix was an invaluable aid, and a truly effective partner in the healthcare insurance ecosystem.

Rodney Yap, co-founder and CEO of Usertip, a digital adoption platform tech start-up, had completed a pre-seed funding round for the company. Yap and his co-founders had leveraged Singapore's tech hub status and government support for digital innovation to develop software that would help train users on new technologies, thereby improving a firm's productivity. Along with private individuals, venture capital firm Protégé Ventures was one of Usertip's early investors. Protégé Ventures was founded by the Singapore Management University's Institute of Innovation & Entrepreneurship and Kairos ASEAN to allow students at tertiary institutions in Singapore to try their hand at venture investing. With Usertip asking for a S$3 million (US$2.22 million) post-money valuation, Protégé Ventures invested S$30,000 (US$22,000) using a Convertible Agreement Regarding Equity (CARE). While the individual investors chose to take a direct equity stake, Protégé Ventures' CARE included a 20% discount rate but without a valuation cap. Did Yap get a good deal?