importance of information technology essay pdf

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Essay on Information Technology in 400 Words

Updated on
Apr 26, 2024

Essay on Information Technology: Information Technology is the study of computer systems and telecommunications for storing, retrieving, and transmitting information using the Internet. Today, we rely on information technology to collect and transfer data from and on the internet. Say goodbye to the conventional lifestyle and hello to the realm of augmented reality (AR) and virtual reality (VR).

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Paragraph on Information Technology

Short Essay on Information Technology

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Ans: Information technology is an indispensable part of our lives and has revolutionized the way we connect, work, and live. The IT sector involves the use of computers and electronic gadgets to store, transmit, and retrieve data. In recent year, there has been some rapid changes in the IT sector, which has transformed the world into a global village, where information can be exchanged in real-time across vast distances.

Ans: The IT sector is one of the fastest-growing sectors in the world. The IT sector includes IT services, e-commerce, the Internet, Software, and Hardware products. IT sector helps boost productivity and efficiency. Computer applications and digital systems have allowed people to perform multiple tasks at a faster rate. IT sector creates new opportunities for everyone; businesses, professionals, and consumers.

Ans: There are four basic concepts of the IT sector: Information security, business software development, computer technical support, and database and network management.

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Information Technology and the U.S. Workforce: Where Are We and Where Do We Go from Here? (2017)

Chapter: 7 conclusion, 7 conclusion.

Progress in many of the basic computing and information technologies has been rapid in recent years, and the committee does not expect the pace of change to slow down in the foreseeable future. While some technologies are reaching maturity now, many important technologies have enormous future potential. As more of the world’s information is digitized and more people and things are networked, the economics of the digital, networked economy will become ever more important. This includes the ability to make copies of goods and services at almost zero cost and deliver them anywhere on the planet almost instantaneously. Furthermore, digitization of products, services, processes, and interactions makes it possible to measure and manage work with far more precision. Data-driven decision making and machine learning provide vast opportunities for improving productivity, efficiency, accuracy, and innovation.

The committee expects important innovations to come in the area of artifical intelligence (AI) and robotics. Several decades ago, humans were unable to converse with machines using ordinary speech; now it is done routinely. Machines are learning to effectively translate from one language to another, a task once seen only in science fiction. We are moving from an era where machines were blind, unable to recognize even simple objects, to an era where they can distinguish faces, read street signs, and understand the content of photographs as well as—or better than—humans. They are being put to work reading X-ray and MRI images, advising doctors on potential drug interactions, helping lawyers

sift through documents, and composing simple stories about sports and finance for newspapers. Machines are becoming much better at reasoning and can now defeat the best humans at most games of skill, from checkers and chess to trivia and Go. Machines are learning to drive cars, which could potentially save thousands of lives in the United States and millions worldwide. Bipedal robots are learning to navigate stairs and uneven terrain, while their cheetah-like brethren can outrun even the fastest humans. Many of the technologies with the greatest impact will likely look unlike any human or animal, but will transport shelves of inventory throughout warehouses, assemble basic electronics in factories, fly to disaster zones with medicine, swim beneath the waves to gather data for oceanographers, and haunt computer networks in search of cyberattacks. In fact, many of these exist in some form already, although they are likely to become more widespread and more competent.

While there are undoubtedly important technological breakthroughs to come, it is critical to note that the technologies that exist today and those under active development have important implications for the workforce. They create opportunities for new products, services, organizational processes, and business models as well as opportunities for automating existing tasks, even whole occupations. Many cognitive and physical tasks will be replaced by machines. At the same time, we expect new job opportunities to emerge as increasingly capable combinations of humans and machines attack problems that previously have been intractable.

Advances in IT and automation will present opportunities to boost America’s overall income and wealth, improve health care, shorten the work week, develop new goods and services, and increase product safety and reliability.

These same advances could also lead to growing inequality, decreased job stability, increasing demands on workers to change jobs, and changes in business organization. There are also important implications for other aspects of society, both intended and unintended, not the least of which include potentially profound changes in education, privacy, security, social relationships, and even democracy.

The ultimate effects of these technologies are not predetermined. Rather, like all tools, computing and information technologies can be used in many different ways. The outcomes for the workforce and society at large depend on our choices. Technology can be a powerful tool. What do we want for our future society? How do we decide this?

Potential future technological capabilities and innovations are largely unpredictable, and their implications and interactions are complex. Investing in extensive and effective data gathering, a robust infrastructure for analyzing these data, and multidisciplinary research will enable a deeper

understanding of emerging changes in technology and the workforce. The results of this research will inform the adoption of policies that will help maximize the resilience and prosperity of the institutions, organizations, and individuals in our society.

Recent years have yielded significant advances in computing and communication technologies, with profound impacts on society. Technology is transforming the way we work, play, and interact with others. From these technological capabilities, new industries, organizational forms, and business models are emerging.

Technological advances can create enormous economic and other benefits, but can also lead to significant changes for workers. IT and automation can change the way work is conducted, by augmenting or replacing workers in specific tasks. This can shift the demand for some types of human labor, eliminating some jobs and creating new ones. Information Technology and the U.S. Workforce explores the interactions between technological, economic, and societal trends and identifies possible near-term developments for work. This report emphasizes the need to understand and track these trends and develop strategies to inform, prepare for, and respond to changes in the labor market. It offers evaluations of what is known, notes open questions to be addressed, and identifies promising research pathways moving forward.

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Special Topic: Silicon-compatible 2D Materials Technologies
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Published: 29 May 2024
Volume 67 , article number 160400 , ( 2024 )

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Hao Qiu 1 , 2 na1 ,
Zhihao Yu 3 na1 ,
Tiange Zhao 4 na1 ,
Qi Zhang 5 na1 ,
Mingsheng Xu 6 na1 ,
Peifeng Li 7 na1 ,
Taotao Li 1 , 2 , 7 na1 ,
Wenzhong Bao 8 , 9 ,
Yang Chai 10 ,
Shula Chen 11 ,
Yiqi Chen 12 ,
Hui-Ming Cheng 13 ,
Daoxin Dai 14 ,
Zengfeng Di 15 ,
Zhuo Dong 16 ,
Xidong Duan 17 ,
Yuhan Feng 15 ,
Jingshu Guo 14 ,
Pengwen Guo 19 ,
Yue Hao 20 ,
Jun He 21 ,
Xiao He 22 ,
Jingyi Hu 23 ,
Weida Hu 4 ,
Zehua Hu 1 ,
Xinyue Huang 24 ,
Ziyang Huang 13 ,
Ali Imran 6 ,
Ziqiang Kong 15 ,
Jia Li 17 ,
Qian Li 25 ,
Weisheng Li 1 , 2 , 7 ,
Lei Liao 26 ,
Bilu Liu 13 ,
Can Liu 18 ,
Chunsen Liu 8 ,
Guanyu Liu 15 ,
Kaihui Liu 27 ,
Liwei Liu 28 ,
Sheng Liu 29 ,
Yuan Liu 26 ,
Donglin Lu 26 ,
Likuan Ma 26 ,
Feng Miao 30 ,
Zhenhua Ni 5 , 31 ,
Jing Ning 20 ,
Anlian Pan 11 ,
Tian-Ling Ren 19 ,
Haowen Shu 32 ,
Litao Sun 33 ,
Yue Sun 29 ,
Quanyang Tao 26 ,
Zi-Ao Tian 15 ,
Dong Wang 20 ,
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Jialong Wang 23 ,
Junyong Wang 16 ,
Wenhui Wang 34 ,
Xingjun Wang 32 ,
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Yuwei Wang 6 ,
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Longfei Xia 20 ,
Baixu Xiang 38 , 39 ,
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Qihua Xiong 38 , 39 ,
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Over the past 70 years, the semiconductor industry has undergone transformative changes, largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional (2D) materials like transition metal dichalcogenides (TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3D integration. We review the important progress in the application of 2D materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2D materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2D materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2D material research.

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The Roadmap of 2D Materials and Devices Toward Chips

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Acknowledgements

We appreciate the support of the National Natural Science Foundation of China and the Ministry of Science and Technology of the People’s Republic of China, and other funds and projects for the development of 2D information materials.

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These authors contributed equally.

Authors and Affiliations

School of Electronic Science and Engineering, Nanjing University, Nanjing, 210023, China

Hao Qiu, Taotao Li, Zehua Hu, Weisheng Li, Tao Zhang, Yi Shi & Xinran Wang

School of Integrated Circuits, Interdisciplinary Research Center for Future Intelligent Chips (Chip-X), Nanjing University, Suzhou, 215163, China

Hao Qiu, Taotao Li, Weisheng Li, Tao Zhang & Xinran Wang

School of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

State Key Laboratory of Infrared Physics, Shanghai Institute of Technical Physics Chinese Academy of Sciences, Shanghai, 200083, China

Tiange Zhao & Weida Hu

School of Physics and Key Lab of Quantum Materials and Devices of the Ministry of Education, School of Electronic Science & Engineering, Southeast University, Nanjing, 211189, China

Qi Zhang, Zhenhua Ni & Ting Zheng

College of Integrated Circuits, State Key Laboratory of Silicon and Advanced Semiconductor Materials, Zhejiang University, Hangzhou, 310027, China

Mingsheng Xu, Ali Imran & Yuwei Wang

Suzhou Laboratory, Suzhou, 215004, China

Peifeng Li, Taotao Li, Weisheng Li & Xinran Wang

State Key Laboratory of Integrated Chips and Systems, School of Microelectronics, Fudan University, Shanghai, 200433, China

Wenzhong Bao, Chunsen Liu, Peng Zhou & Yuxuan Zhu

Shaoxin Laboratory, Shaoxing, 312000, China

Wenzhong Bao & Peng Zhou

Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, 999077, China

Key Laboratory for Micro-Nano Physics and Technology of Hunan Province, College of Materials Science and Engineering, Hunan University, Changsha, 410082, China

Shula Chen & Anlian Pan

College of Integrated Circuits, Zhejiang University, Hangzhou, 311200, China

Yiqi Chen, Hongzhao Wu, Yang Xu, Bin Yu & Yuda Zhao

Shenzhen Geim Graphene Center, Shenzhen Key Laboratory of Layered Materials for Value-Added Applications, Tsinghua-Berkeley Shenzhen Institute & Institute of Materials Research, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China

Hui-Ming Cheng, Ziyang Huang & Bilu Liu

State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, International Research Center for Advanced Photonics, Zijingang Campus, Zhejiang University, Hangzhou, 310058, China

Daoxin Dai & Jingshu Guo

National Key Laboratory of Materials for Integrated Circuits, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, 200050, China

Zengfeng Di, Yuhan Feng, Ziqiang Kong, Guanyu Liu, Zi-Ao Tian & Haomin Wang

CAS Key Laboratory of Nanophotonic Materials and Devices & Key Laboratory of Nanodevices and Applications, i-Lab, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences, Suzhou, 215123, China

Zhuo Dong, Junyong Wang, Liu Yang & Kai Zhang

College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, China

Xidong Duan & Jia Li

Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education), Department of Physics, Renmin University of China, Beijing, 100872, China

Yu Fu & Can Liu

School of Integrated Circuits, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, 100084, China

Pengwen Guo, Tian-Ling Ren & Yi Yang

The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology, Shaanxi Joint Key Laboratory of Graphene, School of Microelectronics, Xidian University, Xi’an, 710071, China

Yue Hao, Jing Ning, Dong Wang, Haidi Wu, Longfei Xia, Jincheng Zhang & Xinbo Zhang

Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, 430072, China

Jun He, Hao Wang, Yao Wen & Hui Zeng

School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, 430074, China

Xiao He & Lei Ye

Center for Nanochemistry (CNC), Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China

Jingyi Hu, Jialong Wang & Yanfeng Zhang

State Key Laboratory for Artificial Microstructure & Mesoscopic Physics and Frontiers Science Center for Nano-Optoelectronics, School of Physics, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China

Xinyue Huang

State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics, Frontier Science Center for Quantum Information, Tsinghua University, Beijing, 100084, China

Qian Li, Hongyun Zhang & Shuyun Zhou

Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education, School of Physics and Electronics, Hunan University, Changsha, 410082, China

Lei Liao, Yuan Liu, Donglin Lu, Likuan Ma & Quanyang Tao

State Key Laboratory for Artificial Microstructure & Mesoscopic Physics and Frontiers Science Center for Nano-Optoelectronics, School of Physics, Peking University, Beijing, 100871, China

Kaihui Liu & Yu Ye

School of Integrated Circuits and Electronics, MIIT Key Laboratory for Low-Dimensional Quantum Structure and Devices, Beijing Institute of Technology, Beijing, 100081, China

Liwei Liu & Yeliang Wang

School of Physics and Technology, Wuhan University, Wuhan, 430072, China

Sheng Liu, Yue Sun & Ting Yu

Institute of Brain-Inspired Intelligence, National Laboratory of Solid State Microstructures, School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing, 210093, China

Feng Miao & Yuekun Yang

Purple Mountain Laboratories, Nanjing, 211111, China

State Key Laboratory of Advanced Optical Communications System and Networks, School of Electronics, Peking University, Beijing, 100871, China

Haowen Shu, Xingjun Wang & Luwen Xing

SEU-FEI Nano-Pico Center, Key Laboratory of MEMS of Ministry of Education, Southeast University, Nanjing, 210096, China

Litao Sun & Tao Xu

Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing, 211189, China

Wenhui Wang

Electrical Engineering Institute, Institute of Materials Science and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH-1015, Switzerland

Zhenyu Wang

State Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, 100083, China

Jiangbin Wu

School of Integrated Circuits and Beijing Advanced Innovation Center for Integrated Circuits, Peking University, Beijing, 100871, China

Yanqing Wu & Xiong Xiong

State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics, Frontier Science Center for Quantum Information, Collaborative Innovation Center of Quantum Matter, Tsinghua University, Beijing, 100084, China

Baixu Xiang & Qihua Xiong

Beijing Academy of Quantum Information Sciences, Beijing, 100193, China

Huawei Technologies Co., Ltd., Shenzhen, 518129, China

Jeffrey Xu & Chunsong Zhao

Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, 100190, China

Guangyu Zhang

Songshan Lake Materials Laboratory, Dongguan, 523808, China

School of Materials Science and Engineering, State Key Laboratory of Silicon and Advanced Semiconductor Materials, Zhejiang University, Hangzhou, 310027, China

Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, 999077, China

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Corresponding authors

Correspondence to Deren Yang , Yi Shi , Han Wang or Xinran Wang .

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Qiu, H., Yu, Z., Zhao, T. et al. Two-dimensional materials for future information technology: status and prospects. Sci. China Inf. Sci. 67 , 160400 (2024). https://doi.org/10.1007/s11432-024-4033-8

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Received : 31 March 2024

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Published : 29 May 2024

DOI : https://doi.org/10.1007/s11432-024-4033-8

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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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