The Rise of the Supermanager: Leadership Transformation in the AI Era

Jonathan H. Westover, PhD
Sep 26
11 min read

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Abstract: This article examines the emerging role of the "Supermanager" in contemporary organizations facing rapid technological change. As artificial intelligence transforms business processes, traditional management approaches focused on supervision have become insufficient to drive organizational performance. Drawing on research across multiple industries, this analysis defines the Supermanager paradigm, explores its prevalence and drivers, and details its impact on organizational and individual outcomes. The evidence suggests that Supermanagers—characterized by their ability to empower teams, foster experimentation, and drive innovation from the bottom up—are creating significant competitive advantages. Organizations seeking to thrive in the AI era must develop leadership capabilities that emphasize coaching over commanding, learning over directing, and innovation over maintenance. This article provides evidence-based strategies for cultivating Supermanagers and building long-term organizational resilience in an increasingly AI-enabled business landscape.

We stand at an inflection point in management practice. The rapid proliferation of artificial intelligence technologies is fundamentally altering how work gets done, creating both unprecedented opportunities and complex challenges for organizational leaders. Traditional management approaches that emphasize hierarchical control, supervision, and standardization are increasingly insufficient to capitalize on AI's potential or address the complexities of today's business environment.

As Bersin (2025) observes, "I've never seen such a massive, rapid, and optimistic investment in technology as we've seen with AI." Despite massive investments approaching 3% of US GDP, productivity gains remain elusive for many organizations. The gap between AI's promise and its actual business impact reveals a critical missing ingredient: a new approach to management itself.

This article examines the emergence of "Supermanagers"—leaders who transcend traditional supervisory roles to become catalysts for innovation, experimentation, and transformation. The stakes could not be higher. Organizations that successfully develop Supermanagers stand to gain significant competitive advantages in productivity, innovation, and employee engagement, while those clinging to outdated management paradigms risk stagnation in an increasingly dynamic business landscape.

The Management Transformation Landscape

Defining the Supermanager in the AI Context

The Supermanager represents a fundamental shift in management philosophy and practice. Unlike traditional managers who "supervise" work according to established procedures, Supermanagers proactively seek opportunities to innovate and transform operations (Bersin, 2025). They operate with a fundamentally different mindset—viewing their role not as maintaining stability but as catalyzing positive change.

Supermanagers can be defined by several distinctive characteristics:

They empower teams with autonomy and decision-making authority rather than maintaining tight control
They foster cultures of experimentation and learning where innovation emerges from the front line
They leverage AI tools to automate routine management tasks, freeing capacity for strategic thinking
They recognize that in the AI era, value comes from reimagining processes, not just executing them efficiently
They serve as bridges between technology capabilities and business outcomes

As Bersin (2025) notes, "Today, unlike in the past, Supermanagers innovate without waiting for a senior committee to make a decision. They experiment, iterate, and drive change." This represents a marked departure from traditional management approaches that often operate through cascading initiatives from the top.

Prevalence, Drivers, and Distribution

The emergence of the Supermanager paradigm is being driven by several converging forces. First, the democratization of technology has fundamentally altered organizational power dynamics. Employees today have unprecedented access to information and powerful tools, making rigid command-and-control structures increasingly untenable.

Second, the pace of technological change—particularly AI advancement—has accelerated dramatically, requiring organizations to adapt more quickly than traditional management hierarchies allow. As Bersin (2025) observes, "Prior technologies like ERP, the cloud, and even mobile required heavy investments from IT and software engineers to get built... AI, by contrast, is the ultimate democratizing technology."

While comprehensive data on Supermanager prevalence across industries remains limited, evidence suggests significant variation in adoption patterns. Industries facing acute competitive pressure and technological disruption—particularly technology, financial services, and healthcare—appear to be leading the transition. Organizations like Microsoft, Meta, Bayer, Unilever, HSBC, Mastercard, Spotify, and Phillips have embraced management models centered around "small, empowered teams, each held accountable for improvement" (Bersin, 2025).

However, many organizations remain firmly anchored in traditional management paradigms. A recent study found that while most organizations are experimenting with AI technologies, relatively few have fundamentally altered their management approaches to capitalize on these technologies (Fountaine et al., 2022).

Organizational and Individual Consequences of Management Approaches

Organizational Performance Impacts

The evidence suggests that organizations embracing the Supermanager paradigm are realizing significant performance advantages. Research examining organizational practices suggests that companies with management approaches emphasizing experimentation and employee empowerment achieve substantially higher productivity gains from technology investments compared to those maintaining traditional command-and-control structures (Brynjolfsson & McAfee, 2017).

These performance differences manifest across multiple dimensions:

Innovation speed and quality: Organizations with more adaptive management practices report faster time-to-market for new initiatives and higher success rates for digital transformation efforts (Keller & Price, 2011).
Adaptability to market changes: Companies with strong change leadership capabilities demonstrate significantly better performance during market disruptions than their less adaptable counterparts (Reeves et al., 2020).
Return on AI investments: Organizations that pair AI implementation with management transformation realize substantially higher returns than those implementing AI without changing management approaches (Bersin, 2025).

The performance gap between organizations embracing vs. resisting the Supermanager model appears to be widening. As Bersin (2025) notes, companies employing traditional management approaches face "risk of stagnation, lagging productivity, and falling behind."

Individual Wellbeing and Stakeholder Impacts

Beyond organizational performance, management approaches significantly affect individual employees and other stakeholders:

For employees, working under Supermanagers versus traditional managers yields substantial wellbeing differences. Research shows that employees led by managers emphasizing empowerment and experimentation report higher engagement, lower burnout rates, and a stronger sense of meaning and purpose in work (Gallup, 2022).

These differences appear to stem from fundamental human needs for autonomy, mastery, and purpose that are better addressed by the Supermanager approach. As Ryan and Deci's Self-Determination Theory suggests, when these psychological needs are met, individuals experience greater intrinsic motivation and wellbeing (Ryan & Deci, 2017).

For customers, the management approach directly impacts experience quality. Organizations with more empowered frontline employees consistently demonstrate higher customer satisfaction and loyalty metrics compared to those with rigid management structures (Dixon et al., 2017). This likely reflects Supermanagers' ability to empower front-line employees to innovate and respond to customer needs without navigating bureaucratic approval processes.

Evidence-Based Organizational Responses

Shifting from Supervision to Coaching and Development

The transition from traditional management to the Supermanager model requires fundamentally reframing the manager's role from supervisor to coach and developer. Evidence suggests several effective approaches:

Implement coaching-focused management systems: Organizations that restructure performance management around regular coaching conversations rather than evaluation report higher employee performance and greater retention rates (Buckingham & Goodall, 2019).
Invest in manager capability development: Companies that invest significantly in developing coaching capabilities among managers realize stronger returns on leadership development spending (Bersin, 2019).
Reframe feedback processes: Shifting from evaluative to developmental feedback frameworks increases the likelihood of behavior change (Wigert & Harter, 2017).

Approaches to consider:

Replace annual performance reviews with regular coaching conversations
Train managers in active listening and powerful questioning techniques
Create peer coaching networks to supplement formal management relationships
Measure and reward managers based on team development outcomes
Implement "management by walking around" with a coaching mindset

Microsoft transformed its management approach by implementing a coaching framework that explicitly redefined managers as developers rather than evaluators. This shift involved intensive capability building for managers globally, incorporating regular coaching conversations and relegating formal evaluation to a smaller component of the manager role. The company reported significant improvements in employee perceptions of manager effectiveness and innovation metrics (Harter, 2020).

Fostering Cultures of Experimentation

Supermanagers create environments where experimentation is encouraged, failure is treated as learning, and innovation emerges organically from all levels of the organization:

Implement rapid experimentation protocols: Organizations with structured approaches to small-scale experimentation realize more value from innovation initiatives (Thomke, 2020).
Decentralize innovation resources: Companies allocating innovation resources to bottom-up initiatives identify more valuable opportunities than those restricting innovation to specialized teams (Bersin, 2025).
Institute psychological safety practices: Teams scoring high on psychological safety measures are more likely to attempt innovative approaches and more likely to acknowledge and learn from failures (Edmondson, 2019).

Approaches to consider:

Establish dedicated time for experimentation (e.g., 20% time or innovation days)
Create lightweight processes for requesting resources for experiments
Celebrate and share learning from failed experiments
Implement idea management systems to capture and track innovative ideas
Develop clear "graduation criteria" for experiments to receive additional funding

Unilever developed a program that enables employees to dedicate portion of their time to experimental projects outside their core responsibilities. Each experiment follows a structured "test and learn" methodology with clear evaluation criteria. The initiative generated numerous validated innovations, including several that became core business offerings. Beyond the direct innovation outcomes, Unilever reported increased employee engagement scores among participating teams (Dua et al., 2021).

Redesigning Management Structures and Spans

The Supermanager model often requires rethinking traditional reporting relationships and organizational hierarchies:

Flatten management structures: Organizations reducing management layers report faster decision-making and higher employee satisfaction (Hamel & Zanini, 2020).
Implement networked team structures: Companies organizing around cross-functional teams rather than functional silos achieve higher productivity and faster time-to-market (Cross et al., 2016).
Redefine management spans: Organizations expanding management spans while investing in manager development report lower management costs without negative performance impacts (Bersin, 2019).

Approaches to consider:

Remove unnecessary approval layers in decision processes
Organize around customer journeys rather than functional specialties
Implement "team of teams" structures with clear accountability
Distinguish between technical leadership and people management roles
Develop dual-track career paths for individual contributors and managers

Standard Chartered Bank reimagined its operations by reorganizing around customer journey teams rather than traditional functional departments. The bank reduced management layers while expanding average spans of control. Critically, this restructuring was accompanied by significant investments in management development and technology-enabled coordination tools. The results included improved time-to-market for new offerings, reduced operating costs, and increased customer satisfaction scores (Smet et al., 2019).

Leveraging AI to Enhance Management Effectiveness

Supermanagers strategically deploy AI tools to automate routine aspects of management, enabling greater focus on strategic and interpersonal priorities:

Implement AI-enabled performance analytics: Organizations using AI to analyze performance patterns can identify coaching opportunities more accurately than those relying solely on manager judgment (Bersin, 2021).
Deploy collaboration analytics: Companies analyzing digital collaboration patterns identify workflow bottlenecks faster and improve cross-functional coordination (Larson et al., 2020).
Utilize AI for administrative streamlining: Managers using AI assistants for administrative tasks reclaim significant time for strategic activities (Bersin, 2025).

Approaches to consider:

Deploy AI writing assistants for routine communications and documentation
Implement AI-powered meeting assistants for scheduling, notes, and follow-ups
Use natural language processing to gather and analyze employee feedback
Adopt AI-enabled project management tools to automate status tracking
Develop AI advisors for management decision support

Mayo Clinic implemented an AI-enabled management system that consolidates team performance data, workflow analytics, and employee feedback into actionable insights. The system automatically identifies potential workflow bottlenecks, suggests coaching interventions based on performance patterns, and streamlines routine management tasks. Managers report reclaiming significant time for strategic priorities and coaching conversations, with corresponding improvements in team effectiveness metrics (Porter & Heppelmann, 2019).

Building Long-Term Management Capability

Reimagining Leadership Development

Traditional leadership development approaches often fail to cultivate the mindsets and capabilities Supermanagers need. Forward-thinking organizations are fundamentally reinventing how they develop leaders:

Shift from event-based to journey-based development: Organizations implementing continuous learning journeys for managers see higher behavior change compared to traditional workshop approaches (Bersin, 2019).
Incorporate real-world experimentation: Leadership development programs that include structured innovation projects yield greater business impact than those focused solely on knowledge acquisition (McCauley & McCall, 2014).
Leverage simulation and AI-powered practice: Organizations using technology-enabled simulations for management skill practice report higher skill application rates than those using traditional role-play approaches (Bersin, 2021).

Companies at the forefront of this transformation recognize that developing Supermanagers requires more than training—it demands a comprehensive ecosystem approach to leadership growth.

Creating Feedback-Rich Environments

Supermanagers thrive in environments where feedback flows freely in all directions. Building these environments requires deliberate systems and cultural norms:

Implement multi-directional feedback systems: Organizations with robust upward feedback mechanisms report higher leadership effectiveness and stronger innovation cultures (Ashford et al., 2016).
Normalize peer feedback: Teams with established peer feedback practices demonstrate higher collaboration effectiveness and fewer interpersonal conflicts (Edmonson, 2019).
Leverage technology for continuous feedback: Organizations using digital platforms for real-time feedback collection report higher rates of behavior change compared to those relying on periodic formal feedback (Bersin, 2019).

The most successful organizations recognize that feedback must be embedded in everyday workflows rather than treated as a periodic event.

Developing Systems Thinking Capabilities

Perhaps the most crucial capability for Supermanagers is systems thinking—the ability to understand complex interdependencies and design interventions that address root causes rather than symptoms:

Implement complexity-based learning approaches: Organizations training managers in complexity theory report more effective problem-solving in ambiguous situations (Senge, 2006).
Encourage cross-functional experiences: Managers with significant experience across multiple functions demonstrate stronger systems thinking capabilities compared to those with single-function depth (McCall, 2010).
Deploy systems modeling tools: Teams using visual systems modeling approaches identify more second-order effects in decision-making and achieve more sustainable solutions (Meadows, 2008).

As organizations become increasingly complex and interconnected, the ability to think systemically becomes not just valuable but essential for effective management.

Conclusion

The emergence of the Supermanager represents a fundamental shift in how we understand effective leadership in the AI era. As traditional management approaches focused on supervision and control become increasingly insufficient, organizations must cultivate leaders who empower teams, foster experimentation, and drive innovation from all levels.

The evidence is clear: organizations embracing the Supermanager paradigm are realizing significant advantages in productivity, innovation, and employee engagement. Those clinging to outdated management models face growing risks of stagnation and competitive disadvantage.

This transition requires more than individual leadership development—it demands systemic changes to organizational structures, processes, and cultures. Forward-thinking organizations are reimagining management selection criteria, development approaches, and performance metrics to align with the Supermanager model.

As Bersin (2025) notes, "I'm not convinced we're losing middle managers at all: rather we're redefining what they do. Companies that drive Supermanager behaviors will rocket ahead in this new world."

The path forward involves deliberate investment in building coaching capabilities, fostering experimental cultures, redesigning management structures, leveraging AI effectively, and developing systems thinking capabilities. Organizations that make these investments will be well-positioned to thrive in an increasingly complex and AI-enabled business environment.

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Jonathan H. Westover, PhD is Chief Academic & Learning Officer (HCI Academy); Associate Dean and Director of HR Programs (WGU); Professor, Organizational Leadership (UVU); OD/HR/Leadership Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.

Suggested Citation: Westover, J. H. (2025). The Rise of the Supermanager: Leadership Transformation in the AI Era. Human Capital Leadership Review, 25(4). doi.org/10.70175/hclreview.2020.25.4.7