research issues in strategic management

Research Methodology in Strategy and Management: Volume 1

Table of contents, list of contributors, introduction.

Welcome to the first volume of Research Methodology in Strategy and Management. This book series’ mission is to provide a forum for critique, commentary, and discussion about key research methodology issues in the strategic management field. Strategic management relies on an array of complex methods drawn from various allied disciplines to examine how managers attempt to lead their firms toward success. The field is undergoing a rapid transformation in methodological rigor, and researchers face many new challenges about how to conduct their research and in understanding the implications that are associated with their research choices. For example, as the field progresses, what new methodologies might be best suited for testing the developments in thinking and theorizing? Many long-standing issues remain unresolved as well. What methodological challenges persist as we consider those matters? This book series seeks to bridge the gap between what researchers know and what they need to know about methodology. We seek to provide wisdom, insight and guidance from some of the best methodologists inside and outside the strategic management field. In each volume, renowned scholars will contribute chapters in their areas of methodological expertise.

THE STATE OF STRATEGIC MANAGEMENT RESEARCH AND A VISION OF THE FUTURE

The field of strategic management has advanced substantially in both theory and empirical research over the last 25 years. However, there are “cracks” beginning to occur in the methodology “dam.” To grow as a discipline, strategic management research must meet and deal effectively with methodological challenges in several areas. We address these challenges in each of the following areas: research questions, data collection, construct measurement, analysis of endogenous relationships, and applications. We present a concise view of the future suggesting ways in which these challenges can be overcome and explain the benefits to the field.

REFLECTING “KNOWLEDGE” IN STRATEGY RESEARCH: CONCEPTUAL ISSUES AND METHODOLOGICAL CHALLENGES

Strategy researchers have become fascinated with the possibilities for developing theoretical perspectives rooted in knowledge and intellectual assets as drivers of superior performance. However, there have been many different schools of thought, each with its own conceptualization lenses and operationalization approaches. In this chapter, we focus on three schools of thought: (1) knowledge as stocks; (2) knowledge as flow; and (3) knowledge as a driver of an organizational capability. We use them to: (a) lay out the distinct approaches to conceptualization and operationalization of strategy-related concepts; and (b) identify specific ways to enhance theory-method correspondence. We believe that considerable progress could be made towards developing a knowledge-based view of strategy but only when accompanied by serious attention to measurement and methodological issues.

THE SOCIAL NETWORK APPROACH IN STRATEGY RESEARCH: THEORETICAL CHALLENGES AND METHODOLOGICAL ISSUES

In recent years, the network perspective has become highly influential in the strategy research. A number of strategic phenomena and outcomes have been studied successfully by adopting the methodology of social network analysis and taking a relational perspective on firm behavior and outcomes. However, while the social network methodology provides a powerful research tool for strategy researchers, it is fraught with both theoretical and methodological challenges. In this paper, we argue that many of the issues related to using the social network approach in strategy research derive from the use of an essentially individual level methodology being applied to the level of the organization. Organizations being large, complex, and nested entities, the social processes that are implied in network research at the level of the individual are often questionable at the interorganizational level. We identify ten specific issues, grouped under three major heads: issues relating to network structure, to network ties, and to network actors and action. We discuss the theoretical and methodological challenges associated with each issue and conclude with some suggestions for using the network perspective in strategy research.

MODELING LIMITED DEPENDENT VARIABLES: METHODS AND GUIDELINES FOR RESEARCHERS IN STRATEGIC MANAGEMENT

Research on strategic choices available to the firm are often modeled as a limited number of possible decision outcomes and leads to a discrete limited dependent variable. A limited dependent variable can also arise when values of a continuous dependent variable are partially or wholly unobserved. This chapter discusses the methodological issues associated with such phenomena and the appropriate statistical methods developed to allow for consistent and efficient estimation of models that involve a limited dependent variable. The chapter also provides a road map for selecting the appropriate statistical technique and it offers guidelines for consistent interpretation and reporting of the statistical results.

LONGITUDINAL ANALYSIS IN STRATEGIC MANAGEMENT

Longitudinal regression analysis is conducted to clarify causal relations and control for unwanted influences from actor heterogeneity and state dependence on theoretically important coefficient estimates. Because strategic management contains theory on how firms differ and how firm actions are influenced by their current strategic position and recent experiences, consistency of theory and methodology often requires use of longitudinal methods. We describe the theoretical motivation for longitudinal methods and outline some common methods. Based on a survey of recent articles in strategic management, we argue that longitudinal methods are now used more frequently than before, but the use is still inconsistent and insufficiently justified by theoretical or empirical considerations. In particular, strategic management researchers should use dynamic models more often, and should test for the presence of actor effects, autocorrelation, and heteroscedasticity before applying corrections.

CURRENT AND POTENTIAL IMPORTANCE OF QUALITATIVE METHODS IN STRATEGY RESEARCH

The study of strategy is the study of how firms gain and maintain a competitive advantage in the marketplace. It is an examination of both the types of strategy that appear to be most successful in a given situation, as well as the organizational resources, systems, principles, and processes that create, transform, and carry out strategic action in competitive arenas. Since its development as a distinct disciplinary area, strategy research has focused primarily on large, cross-sectional studies of quantitative data gathered through questionnaires, archival sources such as financial reports, and commercial data bases such as PIMS and COMPUSTAT. These analyses have focused on, and revealed, patterns of strategy content, formulation processes, and competitive interaction that exist across firms within a given competitive context and that explain variations in performance across firms. These results have led to the development of several basic theoretical frameworks that help us to understand and predict competitive activity and organizational performance.

MAPPING STRATEGIC THINKING WITH METRIC CONJOINT ANALYSIS

Behavioral scientists have long sought to capture how individuals’ understandings, perceptions and beliefs affect their decisions, often through examining the underlying cognitive processes that drive action (Schendel & Hofer, 1979). Economists, for example, are interested in how individuals’ utility functions influence their actions. Marketing researchers investigate how consumers’ preferences are reflected in their purchase behaviors. Organization researchers examine individual characteristics that influence outcomes such as job satisfaction, promotion, and turnover ( Aiman-Smith et al., 2002 ).

CONDUCTING SURVEY RESEARCH IN STRATEGIC MANAGEMENT

This paper considers threats to the internal validity of field studies that utilize survey data. Compared to laboratory experiments and field experiments, field surveys should be strong in realism, practical significance, and normative quality. However, there are substantial threats to internal validity that fall into the general categories of sampling and measurement. We consider these issues and how to deal with them. We pay special attention to the existence and impact of common method variance including strategies for avoiding it, methods for assessing it, and approaches to correcting for it. Our objective is to provide a road map for better use of survey methods.

WHEN DOES THE AGE OF DATA MATTER? NOTES ON THE SELECTION OF DATA FOR STRATEGY RESEARCH

Strategy researchers typically avoid using data more than a few years old for estimation of cross-sectional models. However, problems that might be caused by older data generally reflect more basic weaknesses in research design. This chapter develops criteria for evaluating the importance of the age of data used in cross-sectional research and indicates ways that better research design may be more effective than the substitution of newer data sets.

STRATEGIC MANAGEMENT RESEARCH AND THE CUMULATIVE KNOWLEDGE PERSPECTIVE

In this chapter we ask a simple question: how can we tell if strategic management research is making progress? While other limitations are noted, we argue that it is the absence of metrics for gauging research progress that is most limiting. We propose that research should focus on measures of effect size and that “precision” and “generalizability” in our predictions of important phenomena represent the core metrics that should be used to judge whether progress is occurring. We then discuss how to employ these metrics and examine why existing research practices are likely to hinder efforts to develop cumulative knowledge.

STRUCTURAL EQUATION MODELING METHODS IN STRATEGY RESEARCH: APPLICATIONS AND ISSUES

The objective of this chapter is to provide strategy researchers with a general resource for applying structural equation modeling (SEM) in their research. This objective is important for strategy researchers because of their increased use of SEM, the availability of advanced SEM approaches relevant for their substantive interests, and the fact that important technical work on SEM techniques often appear in outlets that may not be not readily accessible. This chapter begins with a presentation of the basics of SEM techniques, followed by a review of recent applications of SEM in strategic management research. We next provide an overview of five types of advanced applications of structural equation modeling and describe how they can be applied to strategic management topics. In a fourth section we discuss technical developments related to model evaluation, mediation, and data requirements. Finally, a summary of recommendations for strategic management researchers using SEM is also provided.

AN ASSESSMENT OF RESEARCH DESIGNS IN STRATEGIC MANAGEMENT RESEARCH: THE FREQUENCY OF THREATS TO INTERNAL VALIDITY

The authors content analyze 76 empirical Strategic Management Journal articles to determine how studies control for threats to internal validity, a common source of flaws in research designs. Results indicate that most studies fail to control for one or more threats to internal validity. In particular, selection effects were the most frequently appearing threat, followed by history effects, ambiguity about the direction of causal inference, changing data sources and subject mortality. In general, the results suggest that strategy researchers need to more carefully account for threats to the internal validity of their research designs. Suggestions for addressing these problems are provided.

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Issue Management: The Strategic Approach

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The strategic approach to issue management is informed by a hierarchical mode of governance that gives centrality to the firm and priority to the attainment of corporate goals. The strategic approach to issue management is a managerial function focused on identifying and planning for the impact of external negative influences on a firm’s strategy and its implementation. This strategic approach to issue management is an organizationally mediated and subjective process of identifying and interpreting issues that is informed by strategic cognition. It aims at promoting and protecting a firm’s interests by identifying issues, evaluating their strategic implications, and analyzing the motivations and strengths of external entities promoting them as issues. This approach involves three major activities: issue identification, corporate proaction, and inclusion of public affairs issues in established decision-making processes and managerial functions. The implementation of strategic...

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René Schmidpeter

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Davis, S.T. (2023). Issue Management: The Strategic Approach. In: Idowu, S., Schmidpeter, R., Capaldi, N., Zu, L., Del Baldo, M., Abreu, R. (eds) Encyclopedia of Sustainable Management. Springer, Cham. https://doi.org/10.1007/978-3-030-02006-4_1175-1

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Strategic Research and Development (R&D) Issues

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5.3 Strategic Issue Identification

So, what happens in strategic management once all the external and internal analysis is done and the SWOT framework is complete? Is it time to start developing strategies? No, not yet. One more thing needs to happen: defining the strategic issue or issues the firm needs to be sure to address.

What is a strategic issue ? First, it is an issue, something that needs to be addressed and resolved. Second, it is strategic. It is a long-term issue whose resolution will help move the organization toward its vision. Resolving the strategic issue will have a major impact on the direction and success of the firm (Ambler, 2020). The strategic issue is derived from the facts and data provided by the external and internal analysis and its synthesis through the SWOT framework. The business decision makers do not define the strategic issue(s) at the beginning of the strategic management process, through a hunch or guess, but after the analysis is completed. Once defined, the strategic issue helps drive the strategies that the organization develops and pursues. A strategic issue, when identified correctly and used effectively, becomes the strategic focus of the organization. In this process, more than one strategic issue may surface. Generally, decision makers will condense these into a single statement, or deal with less important strategic issues when establishing strategies or lower order goals.

The word “issue” often connotes a negative situation that a firm may be facing. For example, Southwest Airlines was faced with much lower passenger volumes as a result of the COVID-19 pandemic that started in 2020. However, the Subway example discussed at the beginning of this chapter demonstrates that the strategic issue was framed as capitalizing on an opportunity—how to move into untapped international markets.

Ideally, the strategic issue is reduced to one concise sentence, so that it is easily captured and understood. Amplifying information may be provided to further explain the situation and justify the choice of the strategic issue. Often, the strategic issue starts with the word how . In the Southwest Airlines example, the strategic issue could be: “How does Southwest Airlines adjust to long-term, lower passenger volumes and remain the preferred, low-cost leader in the industry?” For Subway, it may have been “How does Subway enter untapped international markets?” Once defined, these companies would develop strategies that move their organization towards its vision, while addressing the strategic issue.

The strategic issue will change over time, as the external, competitive and internal dynamics change. For organizations working through the strategic management process, defining the strategic issue may not be simple. The planning team members may interpret data differently or through the lens of their own perspective. The CFO may see the strategic issue in financial terms, the marketing director as a marketing issue, and the human resources director as an issue with manpower and training. One process organizations can use to determine the strategic issue is for planning team members to study the data from the analysis and each draft and share their idea of the strategic issue. The team then has a process to prioritize these, dropping some, combining some, until they arrive at a consensus on the wording of the strategic issue (Ambler, 2020).

Section Video

Strategic Issues  [01:57]

The video for this lesson discusses Strategic Issues.

You can view this video here: https://youtu.be/Zj_dxbJpCqo .

Key Takeaway

• It is important to define the strategic issue of an organization using the information and data from the external and internal analysis and the SWOT framework. The strategic issue sets the strategic focus for the development of strategies. The strategies will address and attempt to resolve the strategic issue and move the organization toward accomplishing its vision.
• Suppose internal and external analysis data from Apple show an upcoming slump in sales of desktop and laptop computers and tablets for years to come. What might the strategic issue for Apple be?
• You are a college senior preparing to graduate in six months. The COVID-19 pandemic has caused massive furloughs and layoffs nationwide. What might be your strategic issue?

Ambler, T. E. (2020). Strategic issues: The pivotal process for strategic success . The Center for Simplified Strategic Planning, Inc. https://www.cssp.com/CD0799/ProcessForStrategicSuccess/ .

Image Credits

Figure 5.2: Morillo, Christina. “People on conference room.” Pexels license. Retrieved from https://www.pexels.com/photo/people-on-conference-room-1181427/ .

Video Credits

Gregg Learning. (2018, June 9). Strategic issues [Video]. YouTube. https://youtu.be/Zj_dxbJpCqo .

The primary matter faced by an organization that must be addressed for the organization to survive, excel, or achieve a major strategic initiative

Strategic Management Copyright © 2020 by Reed Kennedy is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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The Strategic Advantage Omnichannel Retailers Have Over Amazon

• Rodney Thomas,
• Travis Tokar,
• Remko Van Hoek

New research suggests that encouraging in-store pickup over home delivery could lead to more profitable sales, higher market shares, and sustainability-performance gains.

The authors’ research found that by providing consumers with information on the environmental and social impact of home deliveries, omnichannel retailers could capture $100 billion worth of market share from Amazon. It would also allow them to increase margins and make their own businesses more sustainable.

The allure of online shopping is undeniable. As Amazon has shown, consumers can receive delivery of almost any product with the simple click of a mouse or touch of a screen. No wonder e-commerce sales have more than doubled over the past five years and continue to grow.

• Rodney Thomas is an associate professor of supply chain management and advisor to the Customer Centric Leadership Initiative at the University of Arkansas’s Sam M. Walton College of Business.
• Travis Tokar is a professor of supply chain management at Texas Christian University’s Neeley School of Business.
• Remko Van Hoek is a professor of supply chain management at the University of Arkansas’s Sam M. Walton College of Business and advises companies on procurement transformation . He previously served as a chief procurement officer at a number of companies.

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How strategic connection enhances employee retention and engagement.

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Portrait of a group of diverse businesspeople smiling while standing arm in arm together in an ... [+] office corridor

Ask any leader about their key challenges, and they will tell you that engagement and retention within an increasingly remote and hybrid workforce is a major hurdle. Practical steps to foster genuine human connections can significantly enhance employee retention. Instead of relying on short-term solutions like spot bonuses or extravagant perks, which rarely yield long-term loyalty, companies should focus on building deeper relationships among employees.

Effective connection strategies include facilitating meaningful interactions beyond digital messages, promoting shared experiences, and ensuring that all communications within the workplace foster real, impactful relationships.

To help leaders effectively foster connection, here are some practical steps to consider :

Get Personal

Contrary to the emphasis on office perks like free snacks and foosball tables, what employees miss most when working remotely are spontaneous interactions with coworkers. Sixty percent of employees say these interactions are the top benefit of being in the office. Nearly two-thirds state that coworkers and peers significantly impact their sense of connection.

To build human connection, embrace the personal aspect, allowing employees to find commonalities and share experiences. At a recent company off-site for a Fortune 500 company, we brought all employees together in person for the first time since Covid. We introduced a networking tool where everyone shared a little-known fact about themselves. It resulted in tripling connections in 24 hours.

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Even in a remote setting, make sure to acknowledge significant moments—celebrate marriages, birthdays, and other milestones. Ensure that all employees, regardless of their location, experience the same level of connection through automated workflows and prompts.

These small nudges pay off. Employees with strong work friendships are seven times more likely to be engaged in their jobs, better at engaging customers, and produce higher-quality work.

Impress Your New Hires

Ensuring connection from day one is crucial, yet current practices often fall short. Up to 30% of new hires leave within the first 90 days .

To counteract this trend, personalize the onboarding process. Send automated, hyper-personalized communications to new hires. Learn about your new hires and show them you’re listening. For instance, ask each new hire about their preferred snack and ensure it’s on their desk on their first day. Imagine sharing your coffee preferences in a preboarding questionnaire and having your manager prepare it just how you like it on your first day.

These gestures are more than just polite; they show genuine care. Proper onboarding can reduce first-year turnover by 50%. This retention rate leads to significant dividends; an employee who experiences a stellar onboarding process becomes a brand ambassador, attracting better applicants and reducing recruitment costs.

Build Cross-Functional Connection

While many teams have effective internal connection strategies, silos often exist between different teams and functional groups within the same company.

One effective method to bridge these gaps is through mentoring programs. Such initiatives can significantly impact learning; the majority of employees say that discussing new ideas or changes with someone helps them learn and adapt.

For maximum impact, pair mentors and mentees of different ages and experience levels . Use technology to send reminders and prompts about potential discussion topics to ensure adherence to the plan. This reverse technology mentoring helps bridge generational and experiential divides.

Empower Your Managers

Managers have a significant influence on company culture. Gallup Research shows that direct managers account for up to 70% of the variance in employee engagement.

Support your managers by providing just-in-time nudges about having regular check-ins, giving feedback, and sharing praise. While people management comes naturally to some leaders, most managers either lack this intuition or are too busy to remember the basics, especially concerning remote employees.

This principle is particularly evident in learning environments. Instead of isolating employees with fast-paced learning management system videos, make learning a connected experience. Assign learning buddies to employees so they can apply their knowledge in real-time. This approach turns learning into a team activity, further strengthening organizational connections.

Addressing the war for talent requires a multifaceted approach centered on genuine human connection. Business leaders can create a work environment where employees feel valued and engaged by personalizing interactions, fostering cross-functional relationships, and empowering managers. These strategies not only improve retention but also enhance overall organizational performance in today’s dynamic business landscape.

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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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