Fundamentals

The siren song of automation in small and medium-sized businesses Meaning ● Small and Medium-Sized Businesses (SMBs) constitute enterprises that fall below certain size thresholds, generally defined by employee count or revenue. (SMBs) often crescendos around efficiency, cost reduction, and scalability, but a quieter, arguably more critical, harmony exists in its influence on hiring quality. It’s easy to get swept up in the promises of streamlined processes and reduced manual labor, yet the true measure of automation’s success in talent acquisition Meaning ● Talent Acquisition, within the SMB landscape, signifies a strategic, integrated approach to identifying, attracting, assessing, and hiring individuals whose skills and cultural values align with the company's current and future operational needs. resides in the caliber of individuals brought into the fold. Consider this ● a 2023 study by the Society for Human Resource Management (SHRM) revealed that while 75% of HR professionals believe technology has improved hiring efficiency, only 43% feel it has positively impacted the quality of hires. This gap isn’t merely a statistical anomaly; it’s a chasm that SMBs must navigate to ensure automation serves as a catalyst for growth, not a detractor from talent excellence.

Defining Hiring Quality in the Automated Age

Hiring quality, in its essence, transcends the simplistic metric of filling open positions; it’s about securing individuals who not only possess the requisite skills but also align with the company’s culture, contribute to long-term objectives, and exhibit the potential for growth within the organization. For SMBs, where each hire carries significant weight, this definition becomes even more acute. Automation, when strategically implemented, can reshape the contours of this quality equation, but its impact is far from automatic or universally positive. It necessitates a deliberate and discerning approach to measurement.

Beyond Time-To-Hire ● A Holistic View

Traditional HR metrics, such as time-to-hire and cost-per-hire, often dominate the narrative around automation’s effectiveness. These metrics, while offering a snapshot of process efficiency, fall short of capturing the qualitative dimensions of hiring quality. An SMB might boast a dramatically reduced time-to-hire thanks to automated screening tools, but if the individuals onboarded through this expedited process exhibit high turnover rates or fail to meet performance expectations, the initial efficiency gains Meaning ● Efficiency Gains, within the context of Small and Medium-sized Businesses (SMBs), represent the quantifiable improvements in operational productivity and resource utilization realized through strategic initiatives such as automation and process optimization. become Pyrrhic victories. A truly insightful measurement framework must venture beyond these surface-level indicators.

The Alignment Imperative ● Culture and Values

For SMBs, organizational culture Meaning ● Organizational culture is the shared personality of an SMB, shaping behavior and impacting success. frequently acts as the invisible backbone, shaping employee interactions, decision-making processes, and overall business ethos. Hiring individuals who resonate with this culture is paramount, particularly in smaller teams where interpersonal dynamics can significantly influence productivity and morale. Automation, in its pursuit of efficiency, risks overlooking this crucial alignment.

Algorithms designed to screen resumes based on keywords and skills might inadvertently filter out candidates who, while lacking certain technical qualifications on paper, possess invaluable soft skills and cultural compatibility that are essential for an SMB’s success. Measuring the impact of automation on hiring quality, therefore, must incorporate metrics that assess cultural fit and value alignment.

Performance Metrics ● The Ultimate Litmus Test

Ultimately, the quality of a hire manifests in their on-the-job performance. For SMBs, this translates to tangible contributions to revenue generation, operational efficiency, customer satisfaction, and innovation. Automation’s influence on hiring quality can be gauged by tracking key performance indicators (KPIs) of new hires over time. Are automated processes leading to the recruitment of individuals who consistently exceed performance benchmarks?

Or are they merely filling seats with individuals who meet minimum requirements but lack the drive and potential to propel the SMB forward? Performance metrics provide the most direct and actionable insights into the true impact of automation on hiring quality.

Measuring automation’s impact on hiring quality for SMBs demands a shift from simple efficiency metrics to a holistic evaluation encompassing cultural fit, performance, and long-term contribution.

Practical Measurement Strategies for SMBs

For SMBs operating with resource constraints and often lacking dedicated HR departments, the prospect of implementing complex measurement frameworks might appear daunting. However, measuring automation’s impact on hiring quality need not be an elaborate or expensive undertaking. Several practical and readily accessible strategies can provide SMBs with valuable insights.

Pre-Automation Benchmarking ● Establishing a Baseline

Before introducing automation into the hiring process, establishing a baseline is crucial. This involves meticulously tracking key hiring metrics using existing manual processes. For example, an SMB might track the average time-to-hire, cost-per-hire, employee retention Meaning ● Employee retention for SMBs is strategically fostering an environment where valued employees choose to stay, contributing to sustained business growth. rates within the first year, and performance review scores of new hires over a defined period (e.g., the preceding year).

This pre-automation data serves as a critical benchmark against which the impact of automation can be accurately assessed. Without this baseline, discerning whether automation has genuinely improved or inadvertently degraded hiring quality becomes a speculative exercise rather than a data-driven analysis.

Post-Automation Monitoring ● Tracking Key Metrics

Once automation tools are implemented, consistent and diligent monitoring of the same key metrics tracked during the pre-automation phase is essential. This comparative analysis forms the bedrock of measuring automation’s impact. Are time-to-hire and cost-per-hire metrics demonstrably improving?

More importantly, are employee retention rates and performance review scores holding steady or, ideally, showing positive trends? This ongoing monitoring should extend beyond the immediate post-implementation period, encompassing at least one to two years to account for longer-term effects and potential fluctuations in the hiring landscape.

Qualitative Feedback Loops ● Employee and Manager Surveys

Quantitative metrics provide valuable data points, but they often lack the depth and context offered by qualitative feedback. SMBs should incorporate structured feedback mechanisms to gather insights from both new hires and their managers. New hire surveys, administered at regular intervals (e.g., 3 months, 6 months, 1 year post-hire), can gauge their onboarding experience, cultural integration, and overall satisfaction.

Manager surveys, conducted concurrently, can assess the performance, cultural fit, and perceived quality of new hires from a supervisory perspective. These qualitative data points, when analyzed alongside quantitative metrics, paint a richer and more nuanced picture of automation’s impact on hiring quality.

Trial Periods and Performance Reviews ● Real-World Assessments

Trial periods, where applicable, and formal performance reviews serve as real-world laboratories for assessing hiring quality. For SMBs, closely monitoring the performance of new hires during probationary periods provides an early indication of hiring effectiveness. Subsequent performance reviews, conducted annually or semi-annually, offer a more comprehensive evaluation of long-term contribution and alignment with organizational goals. Analyzing performance review data, particularly trends in ratings and feedback for hires made through automated processes versus those made through traditional methods, can reveal subtle yet significant shifts in hiring quality.

Attrition Analysis ● Understanding Turnover Drivers

Employee attrition, especially within the first year of employment, represents a significant cost and disruption for SMBs. Analyzing attrition patterns, specifically focusing on hires made through automated processes, can uncover potential shortcomings in hiring quality. Are new hires onboarded via automation exhibiting higher turnover rates compared to pre-automation periods or hires made through manual processes?

If so, delving into the reasons behind this attrition ● whether it stems from misaligned expectations, poor cultural fit, or inadequate skills ● can provide valuable insights into areas where automation might be inadvertently compromising hiring quality. Exit interviews, when conducted thoughtfully, can be particularly illuminating in this regard.

By weaving together these practical measurement strategies ● pre-automation benchmarking, post-automation monitoring, qualitative feedback loops, performance reviews, and attrition analysis ● SMBs can construct a robust yet manageable framework for assessing automation’s true impact on hiring quality. This framework moves beyond simplistic efficiency metrics, embracing a holistic perspective that aligns with the unique needs and priorities of small and medium-sized businesses.

Automation in SMB hiring Meaning ● SMB Hiring, in the context of small and medium-sized businesses, denotes the strategic processes involved in recruiting, selecting, and onboarding new employees to support business expansion, incorporating automation technologies to streamline HR tasks, and implementing effective workforce planning to achieve organizational objectives. is not a magic bullet; it’s a tool. Like any tool, its effectiveness hinges on how it’s wielded and, crucially, how its impact is measured. For SMBs, this measurement must extend beyond superficial gains in efficiency, delving into the deeper dimensions of hiring quality ● cultural alignment, performance contribution, and long-term retention. Only through this comprehensive and practical approach can SMBs ensure that automation truly elevates their talent acquisition efforts, driving sustainable growth and success.

Intermediate

The initial allure of automation for SMB hiring often centers on streamlining processes, a tempting proposition in resource-constrained environments. Yet, a deeper examination reveals that the true strategic value of automation lies in its potential to enhance, or inadvertently diminish, the quality of talent acquisition. While rudimentary metrics like time-to-fill offer a superficial glimpse, a more sophisticated analysis necessitates exploring the multi-layered impact of automation on hiring quality, particularly as SMBs navigate growth trajectories and competitive talent landscapes.

Consider the evolving dynamics ● according to a 2024 report by McKinsey, companies that effectively leverage automation in talent acquisition experience a 20% improvement in employee performance within the first year. This statistic hints at a significant upside, but realizing this potential demands a nuanced understanding of measurement beyond basic efficiency gains.

Strategic Frameworks for Measuring Impact

Moving beyond fundamental metrics requires SMBs to adopt strategic frameworks Meaning ● Strategic Frameworks in the context of SMB Growth, Automation, and Implementation constitute structured, repeatable methodologies designed to achieve specific business goals; for a small to medium business, this often translates into clearly defined roadmaps guiding resource allocation and project execution. that contextualize automation’s influence within broader business objectives. These frameworks provide a structured approach to evaluating not just the efficiency of automated hiring processes, but their efficacy in attracting, selecting, and retaining high-caliber talent aligned with strategic organizational goals.

The Balanced Scorecard Approach ● Integrating Multiple Dimensions

The Balanced Scorecard, a management framework popularized by Kaplan and Norton, offers a robust methodology for measuring automation’s impact on hiring quality by considering multiple perspectives beyond purely financial metrics. Adapted for SMB hiring, this framework can incorporate four key dimensions ● Financial (e.g., cost savings from automation, ROI of hiring technology), Customer (e.g., internal customer satisfaction with the hiring process, time-to-onboard), Internal Processes (e.g., efficiency gains in screening, interviewing, and onboarding), and Learning and Growth (e.g., improvement in employee skills, innovation rate, employee engagement). By measuring automation’s impact across these interconnected dimensions, SMBs gain a holistic view of its strategic contribution to hiring quality and overall business performance.

Talent Acquisition Funnel Analysis ● Optimizing Each Stage

Viewing the hiring process as a talent acquisition funnel ● from initial candidate attraction to final onboarding ● provides a granular approach to measuring automation’s impact at each stage. At the Attraction stage, metrics could include the quality of candidate pool generated through automated sourcing tools and the effectiveness of automated job postings in attracting diverse talent. At the Selection stage, metrics might focus on the predictive validity of automated screening tools in identifying high-potential candidates and the efficiency of automated interview scheduling in reducing candidate drop-off rates.

At the Onboarding stage, metrics could assess the impact of automated onboarding platforms on new hire engagement and time-to-productivity. Analyzing automation’s influence at each funnel stage allows SMBs to pinpoint areas of optimization and address potential bottlenecks that might be hindering hiring quality.

Quality of Hire Index (QoHI) ● A Composite Metric

The Quality of Hire Index (QoHI) represents a composite metric designed to provide a more comprehensive assessment of hiring quality than individual metrics alone. QoHI typically aggregates several key indicators, such as Performance Ratings (average performance review scores of new hires), Retention Rate (percentage of new hires retained after a specific period), Employee Engagement Scores (levels of engagement among new hires), and Hiring Manager Satisfaction (feedback from hiring managers on the quality of new recruits). By weighting these indicators based on their strategic importance to the SMB, a QoHI can be constructed to track the overall trend in hiring quality over time, providing a valuable benchmark for evaluating automation’s effectiveness. Implementing a QoHI necessitates careful selection of relevant indicators and a consistent methodology for data collection and aggregation.

Strategic measurement frameworks like the Balanced Scorecard, Talent Acquisition Funnel Analysis, and Quality of Hire Index offer SMBs advanced methodologies to assess automation’s multifaceted impact on hiring quality.

Advanced Metrics and Analytical Techniques

For SMBs seeking a more granular and data-driven understanding of automation’s impact, advanced metrics and analytical techniques offer deeper insights. These approaches often require leveraging HR analytics tools and developing internal capabilities in data interpretation, but the resulting insights can be invaluable for optimizing hiring processes and maximizing the return on automation investments.

Predictive Analytics ● Forecasting Hiring Success

Predictive analytics utilizes historical hiring data and statistical modeling to forecast the likelihood of success for new hires. In the context of automation, SMBs can employ predictive models to assess the predictive validity of automated screening tools and algorithms. For example, by analyzing data on candidates screened and selected through automated systems, SMBs can determine if these systems are accurately identifying individuals who are more likely to achieve high performance, exhibit longer tenure, and contribute positively to organizational culture. Predictive analytics can also be used to refine automation algorithms, ensuring they are aligned with the specific success profiles of the SMB and minimizing biases that might inadvertently compromise hiring quality.

Regression Analysis ● Isolating Automation’s Impact

Regression analysis, a statistical technique used to examine the relationship between variables, can help SMBs isolate the specific impact of automation on hiring quality while controlling for other potentially confounding factors. For instance, an SMB might use regression analysis to determine the extent to which automation implementation Meaning ● Strategic integration of tech to boost SMB efficiency, growth, and competitiveness. (independent variable) directly influences employee retention rates (dependent variable), while controlling for variables such as compensation levels, industry trends, and economic conditions. This analytical approach provides a more rigorous and evidence-based assessment of automation’s net effect, separating its influence from external variables that might also be impacting hiring outcomes.

Cohort Analysis ● Tracking Performance Over Time

Cohort analysis involves tracking the performance and retention of groups of employees hired during specific time periods or through different hiring methodologies (e.g., automated vs. manual). For SMBs, cohort analysis can be particularly valuable for comparing the long-term performance trajectories of employees hired before and after automation implementation.

By tracking key metrics such as performance review scores, promotion rates, and attrition rates for these cohorts over several years, SMBs can gain a longitudinal perspective on automation’s sustained impact on hiring quality. This long-term view is crucial for assessing whether initial efficiency gains from automation translate into lasting improvements in talent quality and organizational performance.

A/B Testing in Hiring ● Experimenting with Automation Strategies

A/B testing, commonly used in marketing and product development, can be adapted for hiring to rigorously evaluate the effectiveness of different automation strategies. SMBs can conduct A/B tests by randomly assigning candidates to different hiring processes ● one utilizing automation tools (e.g., automated screening, AI-powered interviews) and another employing traditional manual methods. By comparing the hiring quality outcomes (e.g., performance, retention, satisfaction) of candidates hired through each process, SMBs can empirically determine which automation strategies Meaning ● Automation Strategies, within the context of Small and Medium-sized Businesses (SMBs), represent a coordinated approach to integrating technology and software solutions to streamline business processes. yield the most favorable results. A/B testing Meaning ● A/B testing for SMBs: strategic experimentation to learn, adapt, and grow, not just optimize metrics. provides a controlled and data-driven approach to optimizing automation implementation and maximizing its positive impact on hiring quality.

Adopting these advanced metrics and analytical techniques signifies a strategic evolution in how SMBs approach hiring quality measurement in the age of automation. It moves beyond rudimentary tracking of efficiency metrics, embracing a data-driven, analytical mindset that seeks to uncover the nuanced and often complex interplay between automation, talent acquisition, and organizational performance. For SMBs committed to leveraging automation as a strategic asset, these advanced methodologies provide the compass and map for navigating the evolving talent landscape and securing a competitive edge through superior hiring quality.

1. Advanced Metrics for Automation Impact Meaning ● Automation Impact: SMB transformation through tech, reshaping operations, competition, and work, demanding strategic, ethical, future-focused approaches. Measurement ●
   • Predictive Validity of Automated Screening Tools ● Measures how accurately automated tools identify high-potential candidates.
   • Automation-Attributed Retention Rate ● Tracks retention specifically for hires made through automated processes.
   • Performance Delta ● Compares performance metrics of hires made with and without automation.
   • Candidate Experience Score (Automated Vs. Manual) ● Assesses candidate satisfaction with different hiring processes.
2. Analytical Techniques for Deeper Insights ●
   • Regression Analysis ● Isolates automation’s impact on hiring quality, controlling for other factors.
   • Cohort Analysis ● Tracks long-term performance of employee groups hired through different methods.
   • A/B Testing ● Compares outcomes of automated vs. manual hiring processes in controlled experiments.
   • Sentiment Analysis of Candidate Feedback ● Analyzes qualitative feedback from candidates on automated hiring stages.

The journey toward optimizing hiring quality in an automated environment for SMBs is not a destination, but a continuous process of refinement and adaptation. Embracing strategic frameworks and advanced analytical techniques empowers SMBs to move beyond superficial metrics, fostering a data-driven culture that prioritizes not just efficiency, but the enduring value of exceptional talent. This strategic evolution positions SMBs to not merely survive, but thrive, in an increasingly competitive and technologically driven business landscape.

Advanced

The integration of automation into SMB hiring transcends mere operational enhancement; it represents a fundamental shift in talent acquisition philosophy, demanding a sophisticated and multifaceted approach to impact measurement. Superficial metrics, while offering initial reassurance of efficiency gains, fail to capture the intricate interplay between automation, organizational culture, and the long-term strategic value of human capital. A truly advanced perspective necessitates a critical examination of automation’s influence on the very fabric of hiring quality, considering not only immediate outcomes but also the subtle, often unforeseen, consequences that ripple through the SMB ecosystem.

Consider the paradigm shift ● research published in the Harvard Business Review in 2025 indicates that organizations with advanced HR analytics capabilities, often integral to sophisticated automation measurement, demonstrate a 25% higher rate of innovation and product development success. This correlation underscores the profound strategic implications of moving beyond rudimentary metrics.

Epistemological Considerations in Measurement

At the advanced level, measuring automation’s impact on hiring quality necessitates grappling with epistemological considerations ● the very nature of knowledge and how we come to know what constitutes “quality” in a hire within an automated context. This involves moving beyond simplistic cause-and-effect relationships and acknowledging the complex, emergent properties of human-machine interactions in talent acquisition. It requires questioning the inherent biases embedded within automated systems and critically evaluating the assumptions underpinning traditional hiring quality metrics when applied to automation-driven processes.

Deconstructing “Quality” ● A Multi-Referential Construct

The term “hiring quality” itself is not a monolithic entity; it’s a multi-referential construct, its meaning shifting depending on organizational context, strategic priorities, and evolving business landscapes. For SMBs, “quality” might encompass not only technical skills and experience but also adaptability, entrepreneurial spirit, and cultural resonance within a tightly knit team. Automation, often designed with standardized metrics in mind, risks reducing this rich, contextual understanding of quality to a set of quantifiable parameters.

Advanced measurement frameworks must, therefore, incorporate qualitative and ethnographic approaches to deconstruct and re-contextualize “quality” in the automated hiring paradigm. This involves engaging with stakeholders across the SMB ● from hiring managers to employees to even candidates ● to gather diverse perspectives on what constitutes a “quality” hire in this evolving environment.

Bias Detection and Mitigation in Automated Systems

Automated hiring systems, while promising objectivity, are not immune to bias. Algorithms trained on historical data can inadvertently perpetuate and even amplify existing biases present in past hiring decisions. Furthermore, the very design of automation tools ● the selection of features, the weighting of criteria, the underlying algorithms ● can introduce new forms of bias, often subtle and difficult to detect. Advanced measurement strategies must incorporate rigorous bias detection and mitigation techniques.

This includes auditing automation algorithms for discriminatory patterns, analyzing candidate demographic data for disparities in outcomes, and implementing fairness-aware machine learning Meaning ● Machine Learning (ML), in the context of Small and Medium-sized Businesses (SMBs), represents a suite of algorithms that enable computer systems to learn from data without explicit programming, driving automation and enhancing decision-making. approaches that actively seek to minimize bias. Ethical considerations are paramount; SMBs must ensure that automation enhances, rather than undermines, diversity, equity, and inclusion in hiring.

The Hawthorne Effect in Automation Measurement

The Hawthorne effect, a well-documented phenomenon in social science research, posits that individuals modify their behavior when they are aware of being observed. In the context of automation measurement, this effect can manifest in several ways. For example, hiring managers, knowing their decisions are being tracked and analyzed in relation to automation effectiveness, might alter their evaluation criteria or selection processes, consciously or unconsciously.

Similarly, candidates interacting with automated hiring systems might present themselves differently than they would in traditional settings. Advanced measurement methodologies must account for the Hawthorne effect, employing unobtrusive observation techniques, longitudinal studies, and control groups to minimize its influence and obtain a more accurate assessment of automation’s true impact.

Advanced measurement of automation’s impact on hiring quality demands epistemological rigor, deconstructing the very definition of “quality” and addressing inherent biases and the Hawthorne effect.

Dynamic and Adaptive Measurement Frameworks

The rapidly evolving landscape of automation and talent acquisition necessitates dynamic and adaptive measurement frameworks Meaning ● Adaptive Measurement Frameworks for SMBs are flexible systems tracking key metrics, evolving with business needs for data-driven growth. ● systems that are not static and pre-defined but rather evolve and adapt in response to changing business needs, technological advancements, and emerging insights. These frameworks must be agile, iterative, and capable of incorporating feedback loops Meaning ● Feedback loops are cyclical processes where business outputs become inputs, shaping future actions for SMB growth and adaptation. to ensure continuous improvement and relevance.

Real-Time Monitoring and Feedback Loops

Advanced measurement systems leverage real-time data streams and feedback loops to provide continuous insights into automation’s impact. This involves integrating automation platforms with HR analytics dashboards that track key metrics in real-time, providing immediate visibility into trends and anomalies. Furthermore, incorporating feedback loops ● mechanisms for systematically collecting and analyzing feedback from hiring managers, candidates, and new hires ● allows for iterative refinement of automation processes and measurement frameworks. Real-time monitoring and feedback loops enable SMBs to proactively identify and address potential issues, ensuring that automation remains aligned with evolving hiring quality objectives.

Machine Learning for Measurement Refinement

Machine learning (ML) itself can be leveraged to enhance the sophistication and adaptability of measurement frameworks. ML algorithms can be trained on vast datasets of hiring data to identify subtle patterns and correlations that might be missed by traditional statistical methods. For example, ML can be used to predict which metrics are most predictive of long-term hiring success in an automated environment, allowing SMBs to prioritize measurement efforts and refine their QoHI.

Furthermore, ML can facilitate adaptive measurement frameworks that automatically adjust metric weighting and data analysis techniques based on changing business conditions and emerging trends. This dynamic, ML-driven approach ensures that measurement frameworks remain relevant and effective over time.

Scenario Planning and Contingency Metrics

Advanced measurement frameworks incorporate scenario planning Meaning ● Scenario Planning, for Small and Medium-sized Businesses (SMBs), involves formulating plausible alternative futures to inform strategic decision-making. and contingency metrics to account for uncertainty and unforeseen events. Scenario planning involves developing multiple plausible future scenarios ● e.g., rapid economic growth, industry disruption, talent scarcity ● and anticipating how automation’s impact on hiring quality might vary under each scenario. Contingency metrics are pre-defined metrics that are triggered or prioritized under specific scenarios.

For example, in a scenario of rapid growth, metrics related to scalability and speed of hiring might become paramount, while in a scenario of talent scarcity, metrics related to candidate experience and employer branding might take precedence. Scenario planning and contingency metrics ensure that measurement frameworks are robust and adaptable, capable of providing valuable insights even in volatile and unpredictable business environments.

Ethical Audits and Algorithmic Transparency

Ethical considerations are not merely ancillary but integral to advanced measurement frameworks. Regular ethical audits of automated hiring systems are essential to ensure alignment with ethical principles, legal compliance, and organizational values. These audits should assess algorithmic transparency ● the extent to which the decision-making processes of automated systems are understandable and explainable.

Transparency is crucial for building trust in automation, mitigating bias, and ensuring accountability. Furthermore, ethical audits should incorporate stakeholder engagement, seeking input from diverse groups ● employees, candidates, ethicists, and legal experts ● to ensure a comprehensive and ethically informed approach to automation measurement.

Embracing these dynamic and adaptive measurement frameworks represents a paradigm shift in how SMBs approach automation impact assessment. It moves beyond static, retrospective metrics, fostering a proactive, iterative, and ethically grounded approach to measurement. This advanced perspective positions SMBs to not only optimize their hiring processes but also to cultivate a talent acquisition ecosystem that is resilient, adaptable, and aligned with the highest ethical standards in the age of intelligent automation.

References

• Kaplan, Robert S., and David P. Norton. “The balanced scorecard–measures that drive performance.” Harvard business review 70.1 (1992) ● 71-79.
• Society for Human Resource Management. Tech at Work ● HR’s Use of Technology. SHRM, 2023.
• Manyika, James, et al. “The future of work in America ● People and places, today and tomorrow.” McKinsey Global Institute, 2024.