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About Harvard Business Publishing

Headquartered in Boston, Massachusetts, Harvard Business Publishing (HBP) is the leading provider of teaching materials for management education.

HBP was founded in 1994 as a not-for-profit, wholly-owned subsidiary of Harvard University, reporting into Harvard Business School. HBP's mission is to improve the practice of management in a changing world. This mission influences how they approach what they do and what they believe is important.

With approximately 450 employees, primarily based in Boston, with offices in New York City, India, Singapore, Qatar and the United Kingdom, HBP serves as a bridge between academia and enterprises around the globe through its publications and multiple platforms for content delivery, and its reach into three markets: academic, corporate, and individual managers. HBP has a conventional governance structure comprising a  Board of Directors , an internal  Executive Committee , and Business Unit Directors.

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The Case Centre distributes a comprehensive range of materials including the complete collection of more than 7,500 Harvard Business School case studies, teaching notes, background notes, case videos, and a selection of software ancillaries.

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Focusing on vital issues like validity, reliability and quality in research, the author helps you ensure your research is rigorous and methodologically sound. This third edition:

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This GEEP e-book Environmental Education in Action: Learning from Case Studies Around the World explores the world of environmental education (EE) case studies—studies that reflect the complexity, messiness, beauty, and diversity of EE programs across the planet that strive to create a more sustainable future for us all.

In these pages, we explore what a case study is and how to create one, and examine the ways in which you can use case studies as teaching tools. The examples we offer can be used in university courses and professional development workshops for educators, and each includes discussion questions and activities designed to promote critical conversations about EE. We hope that these chapters will prompt you to reflect on and enhance your own work in the field, too, as you educate and mentor the next generation of environmental stewards.

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Please note you do not have access to teaching notes, using an e-book platform as a learning resource and information management tool: the case study of the “k” e-book platform at an english business school.

On the Horizon

ISSN : 1074-8121

Article publication date: 7 February 2019

Issue publication date: 12 March 2019

This paper aims to present an insight into the introduction of an e-textbook’s learning platform for online students residing in Asia by an English Business School. This platform also generated a range of data analytics that provided teachers with an understanding of how their students interact with learning materials.

Design/methodology/approach

This paper interrogated the data generated through students’ interaction with the e-books learning platform, as well as students’ feedback via a questionnaire. The data generated are presented in a series of diagrams.

This paper makes links between self-regulated theory and how e-textbooks are used by students. The findings also offer tentative support for the use of e-books platforms as both a learning resource and a mechanism to obtain learning analytics.

Research limitations/implications

This paper contributes to the wider discourse of the use of learning analytics and how information technology may align with self-regulated learning.

Originality/value

This paper represents a contribution to the developing field of research related to learning analytics and how students manage their use of information technology.

  • Online learning
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  • E-books platform

Stoten, D.W. (2019), "Using an e-book platform as a learning resource and information management tool: the case study of the “K” e-book platform at an English business school", On the Horizon , Vol. 27 No. 1, pp. 12-23. https://doi.org/10.1108/OTH-06-2018-0021

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Cases in Electronic Commerce is a collection of 25 full-length case studies written by professors at the renowned Richard Ivey School of Business. Each case study is centered around a real-world company and provides sufficient detail on the implications associated with e-commerce initiatives. This managerial-oriented approach allows students to fully understand and appreciate the strategic,tactical,and operational challenges faced when launching an e-commerce-based business.

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Case Study: E-Drive System Development

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  • First Online: 02 December 2020
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case study electronic book

  • Thomas Rösch 5 ,
  • Thorsten Bürger 5 ,
  • Antoine Tan-Kim 5 ,
  • Konstantin Walter 5 &
  • Katrin Wand 5  

Part of the book series: Powertrain ((POWERTRAIN))

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This case study focuses on the development of an electric drive (e-drive) system, which is part of an integrated electric axle and was developed for a premium manufacturer’s high performance, off-road, all-wheel drive passenger car. The e-drive consists of an e-motor, an inverter, and the required control software. In order to achieve the development targets, a holistic system approach was necessary. The development approach considered aspects from customer requirements to a fully tested product that was validated for high volume production. Specific examples show development challenges regarding inverter and e-motor design, and the integration of this hardware and the required control software to an e-drive. Therefore, a systems engineering approach including simulation, refinement of requirements, and design adjustments to achieve the best overall performance in alignment with the customer’s requirements is described.

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ASPICE (2015) VDA QMC Working Group 13/Automotive SIG; Automotive SPICE Process Assessment/Reference Model Version 3.0. VDA QMC, 07-2015

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Deiml M, Eriksson T, Schneck M, Tan-Kim A (2019) High-speed electric drive unit for the next generation of vehicles. ATZ, 6. https://www.springerprofessional.de/en/high-speed-electric-drive-unit-for-the-next-generation-of-vehicl/16750388 . Online accessed 25 May 2020

Dörsam T, Kehl S, Klinkig A, Radon A, Sirch O (2013) Die neue Spannungsebene 48 V im Kraftfahrzeug; Energieeffiziente Antriebstechnologien. Springer, Wiesbaden. ATZ/MTZ-Fachbuch. ISBN: 978-3-658-00790-4

Engstle A, Deiml M, Schlecker M, Angermaier A (2013a) Entwicklung eines heckgetriebenen 800-V-Elektrofahrzeugs Energieeffiziente Antriebstechnologien. Springer, Berlin. ATZ / MTZ-Fachbuch; Wiesbaden; ISBN: 978-3-658-00790-4

Engstle A, Deiml M, Angermaier A, Schelter W (2013b) 800 Volt für Elektrofahrzeuge Eine applikationsgerechte Spannungslage. ATZ Automobiltechnische Zeitschrift 115:688–693. https://doi.org/10.1007/s35148-013-0245-6

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Mehrgou M, Gracia de Madinabeitia Merino I, Pohn J, Garmendia Gutierrez J, Priestner C, Zieher F (2020) Robustness and variability prediction of electric machine noise using CAE. In: Siebenpfeiffer W (ed) Automotive acoustics conference 2019. Springer, Wiesbaden. https://doi.org/10.1007/978-3-658-27669-0_14

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Pohl K, Rupp C (2015) Basiswissen requirements engineering: Aus-und Weiterbildung nach IREB-Standard zum certified professional for requirements engineering foundation level. dpunkt, Heidelberg. ed. 4. ISBN: 978-3-86490-283-3

Tochtermann J, Ackerl M, Schreier H, Walter K (2017) Integrierte elektrische Lkw-Achse für den innerstädtischen Verteilerverkehr. ATZ Automobiltechnische Zeitschrift 119:60–65. https://doi.org/10.1007/s35148-017-0132-7

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Thomas Rösch, Thorsten Bürger, Antoine Tan-Kim, Konstantin Walter & Katrin Wand

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Rösch, T., Bürger, T., Tan-Kim, A., Walter, K., Wand, K. (2021). Case Study: E-Drive System Development. In: Hick, H., Küpper, K., Sorger, H. (eds) Systems Engineering for Automotive Powertrain Development. Powertrain. Springer, Cham. https://doi.org/10.1007/978-3-319-68847-3_35-2

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DOI : https://doi.org/10.1007/978-3-319-68847-3_35-2

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  • Electronic and Photoelectron Spectroscopy

Electronic and Photoelectron Spectroscopy

Fundamentals and case studies.

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Book description

Electronic and photoelectron spectroscopy can provide extraordinarily detailed information on the properties of molecules and are in widespread use in the physical and chemical sciences. Applications extend beyond spectroscopy into important areas such as chemical dynamics, kinetics and atmospheric chemistry. This book aims to provide the reader with a firm grounding of the basic principles and experimental techniques employed. The extensive use of case studies effectively illustrates how spectra are assigned and how information can be extracted, communicating the matter in a compelling and instructive manner. Topics covered include laser-induced fluorescence, resonance-enhanced multiphoton ionization, cavity ringdown and ZEKE spectroscopy. The volume is for advanced undergraduate and graduate students taking courses in spectroscopy and will also be useful to anyone encountering electronic and/or photoelectron spectroscopy during their research.

Review of the hardback:'… this book is a light, readable and valuable introduction to the detailed and exciting world of electronic and photoelectron spectroscopy.'

Source: Chemistry World

Review of the hardback:'This book is not only a useful book for a reasonable price, it is a very good book for graduate students in physical chemistry and all those who want to be introduced in modern spectroscopic techniques, and the results which may be obtained by these techniques. This book is a very helpful introduction into modern spectroscopy which guides the reader properly from the basic concepts to advanced principles. The book can be recommended not only for graduate students but also for all those who are engaging in teaching spectroscopy.'

Source: Zeitschrift für Physikalische Chemie

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Frontmatter pp i-iv

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Contents pp v-x

Preface pp xi-xii, list of journal abbreviations pp xiii-xiv, part i - foundations of electronic and photoelectron spectroscopy pp 1-2, 1 - introduction pp 3-6, 2 - electronic structure pp 7-11, 3 - angular momentum in spectroscopy pp 12-14, 4 - classification of electronic states pp 15-23, 5 - molecular vibrations pp 24-39, 6 - molecular rotations pp 40-50, 7 - transition probabilities pp 51-64, part ii - experimental techniques pp 65-66, 8 - the sample pp 67-74, 9 - broadening of spectroscopic lines pp 75-77, 10 - lasers pp 78-86, 11 - optical spectroscopy pp 87-101, 12 - photoelectron spectroscopy pp 102-110, part iii - case studies pp 111-112, 13 - ultraviolet photoelectron spectrum of co pp 113-119, 14 - photoelectron spectra of co 2 , ocs, and cs 2 in a molecular beam pp 120-128, 15 - photoelectron spectrum of no – 2 pp 129-137, 16 - laser-induced fluorescence spectroscopy of c 3 : rotational structure in the 300 nm system pp 138-143, 17 - photoionization spectrum of diphenylamine: an unusual illustration of the franck–condon principle pp 144-149, 18 - vibrational structure in the electronic spectrum of 1,4-benzodioxan: assignment of low frequency modes pp 150-156, 19 - vibrationally resolved ultraviolet spectroscopy of propynal pp 157-164, 20 - rotationally resolved laser excitation spectrum of propynal pp 165-170, 21 - zeke spectroscopy of al(h 2 o) and al(d 2 o) pp 171-179, 22 - rotationally resolved electronic spectroscopy of the no free radical pp 180-186, 23 - vibrationally resolved spectroscopy of mg + –rare gas complexes pp 187-196, altmetric attention score, full text views.

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7.3: Case Study- Electronic Waste and Extended Producer Responsibility

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Electronic waste, commonly known as e-waste, refers to discarded electronic products such as televisions, computers and computer peripherals (e.g. monitors, keyboards, disk drives, and printers), telephones and cellular phones, audio and video equipment, video cameras, fax and copy machines, video game consoles, and others (see Figure \(\PageIndex{1}\)).

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In the United States, it is estimated that about 3 million tons of e-waste are generated each year. This waste quantity includes approximately 27 million units of televisions, 205 million units of computer products, and 140 million units of cell phones. Less than 15 to 20 percent of the e-waste is recycled or refurbished; the remaining percentage is commonly disposed of in landfills and/or incinerated. It should be noted that e-waste constitutes less than 4 percent of total solid waste generated in the United States. However, with tremendous growth in technological advancements in the electronics industry, many electronic products are becoming obsolete quickly, thus increasing the production of e-waste at a very rapid rate. The quantities of e-waste generated are also increasing rapidly in other countries such as India and China due to high demand for computers and cell phones.

In addition to the growing quantity of e-waste, the hazardous content of e-waste is a major environmental concern and poses risks to the environment if these wastes are improperly managed once they have reached the end of their useful life. Many e-waste components consist of toxic substances, including heavy metals such as lead, copper, zinc, cadmium, and mercury as well as organic contaminants, such as flame retardants (polybrominated biphenyls and polybrominated diphenylethers). The release of these substances into the environment and subsequent human exposure can lead to serious health and pollution issues. Concerns have also been raised with regards to the release of toxic constituents of e-waste into the environment if landfilling and/or incineration options are used to manage the e-waste.

Various regulatory and voluntary programs have been instituted to promote reuse, recycling and safe disposal of bulk e-waste. Reuse and refurbishing has been promoted to reduce raw material use energy consumption, and water consumption associated with the manufacture of new products. Recycling and recovery of elements such as lead, copper, gold, silver and platinum can yield valuable resources which otherwise may cause pollution if improperly released into the environment. The recycling and recovery operations have to be conducted with extreme care, as the exposure of e-waste components can result in adverse health impacts to the workers performing these operations. For economic reasons, recycled e-waste is often exported to other countries for recovery operations. However, lax regulatory environments in many of these countries can lead to unsafe practices or improper disposal of bulk residual e-waste, which in turn can adversely affect vulnerable populations.

In the United States, there are no specific federal laws dealing with e-waste, but many states have recently developed e-waste regulations that promote environmentally sound management. For example, the State of California passed the Electronic Waste Recycling Act in 2003 to foster recycling, reuse, and environmentally sound disposal of residual bulk e-waste. Yet, in spite of recent regulations and advances in reuse, recycling and proper disposal practices, additional sustainable strategies to manage e-waste are urgently needed.

One sustainable strategy used to manage e-waste is extended producer responsibility (EPR), also known as product stewardship. This concept holds manufacturers liable for the entire life-cycle costs associated with the electronic products, including disposal costs, and encourages the use of environmental-friendly manufacturing processes and products. Manufacturers can pursue EPR in multiple ways, including reuse/refurbishing, buy-back, recycling, and energy production or beneficial reuse applications. Life-cycle assessment and life-cycle cost methodologies may be used to compare the environmental impacts of these different waste management options. Incentives or financial support is also provided by some government and/or regulatory agencies to promote EPR. The use of non-toxic and easily recyclable materials in product fabrication is a major component of any EPR strategy. A growing number of companies (e.g. Dell, Sony, HP) are embracing EPR with various initiatives towards achieving sustainable e-waste management.

EPR is a preferred strategy because the manufacturer bears a financial and legal responsibility for their products; hence, they have an incentive to incorporate green design and manufacturing practices that incorporate easily recyclable and less toxic material components while producing electronics with longer product lives. One obvious disadvantage of EPR is the higher manufacturing cost, which leads to increased cost of electronics to consumers.

There is no specific federal law requiring EPR for electronics, but the United States Environmental Protection Agency (USEPA) undertook several initiatives to promote EPR to achieve the following goals: (1) foster environmentally conscious design and manufacturing, (2) increase purchasing and use of more environmentally sustainable electronics, and (3) increase safe, environmentally sound reuse and recycling of used electronics. To achieve these goals, USEPA has been engaged in various activities, including the promotion of environmental considerations in product design, the development of evaluation tools for environmental attributes of electronic products, the encouragement of recycling (or e- cycling), and the support of programs to reduce e-waste, among others. More than 20 states in the United States and various organizations worldwide have already developed laws and/or policies requiring EPR in some form when dealing with electronic products. For instance, the New York State Wireless Recycling Act emphasizes that authorized retailers and service providers should be compelled to participate in take-back programs, thus allowing increased recycling and reuse of e-waste. Similarly, Maine is the first U.S. state to adopt a household e-waste law with EPR.

In Illinois, Electronic Products Recycling & Reuse Act requires the electronic manufacturers to participate in the management of discarded and unwanted electronic products from residences. The Illinois EPA has also compiled e-waste collection site locations where the residents can give away their discarded electronic products at no charge. Furthermore, USEPA compiled a list of local programs and manufacturers/retailers that can help consumers to properly donate or recycle e-waste.

Overall, the growing quantities and environmental hazards associated with electronic waste are of major concern to waste management professionals worldwide. Current management strategies, including recycling and refurbishing, have not been successful. As a result, EPR regulations are rapidly evolving throughout the world to promote sustainable management of e-waste. However, neither a consistent framework nor assessment tools to evaluate EPR have been fully developed.

The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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