What’s the point of a manager, really?
Ask an executive, and they may say managers are essential for ensuring accountability and team performance. Ask an employee, and they’ll probably tell you that becoming a manager is their main path to advancement. Both perspectives hold truth, and both underscore a pressing need to reconsider how management roles operate.
Many organizations have a lot of managers in their ranks, perhaps due to a long-standing belief that investing in manager positions will improve business performance. However, promoting employees to management as a retention strategy has overpopulated leadership ranks while diminishing the quality of leadership itself. Only 22% of managers globally are engaged at work, according to Gallup.
As the rise of AI creates an expectation and opportunity for organizations to oversee more people with fewer leaders, the “addiction” to managers is hitting a breaking point. While some organizations will default to the status quo, those that use AI to amplify human judgment can move beyond traditional management models and begin to capture return on investment (ROI).
Why we’re addicted to managers (and what it’s costing us)
This is not to say that management roles have no purpose. Certainly, they exist for practical reasons. Managers are responsible for frontline supervision, from setting goals that align their teams with business strategy to coordinating across departments. Just as critically, they support the employee experience by offering coaching, hiring new staff, handling HR and compensation questions, and helping to build a positive team culture.
Yet being a manager has also become a social contract—an unspoken agreement that employees who stick around will eventually be promoted to a leadership role. Becoming a manager tends to be the fastest track to greater influence and higher pay: On average, managers earn 33% more than individual contributors who do not manage others, and that gap grows to over 50% at senior levels.
In theory, that premium should pay for leaders equipped to sustain high-performing teams. In reality, the prevalence of “accidental managers,” including 82% of U.K. managers without any formal training, can quickly stunt team development. Individual contributors promoted to management without sufficient support often rely on gut instincts and surface-level metrics to lead, making it easy for them to miss early signs of employee burnout or team dysfunction.
The risk of poor management compounds when organizations can’t reliably measure manager effectiveness. Infrastructure to connect people data, HR data, and relevant business data is critical to identify which leaders are moving the needle, yet many organizations still operate without it.
Feeding a sprawling management layer that operates without the necessary data to drive performance has become the norm. AI presents an opportunity to reject this tendency and rebuild management models with a focus on insight, not instinct.
A new management model in the AI economy
Managers are often trapped in a frustrating game of telephone. They waste time chasing down and consolidating information to inform upper leadership, spending nearly 40% of their time on “firefighting” and administrative work, and only 13% of their time on developing their people. Meanwhile, access to strategic guidance from the senior or executive leadership is often confined to channels such as town halls, quarterly meetings, and reports.
In addition to being inefficient, this reporting loop creates a disconnect between executive priorities and frontline managers’ actions. AI can close this costly information gap, but only when used to democratize data and strategic insights across the organization.
To truly improve strategic alignment, AI must be integrated into core management processes, not just used as a personal productivity tool. Consider a manager who, rather than asking a general large language model (LLM) for advice, can instead query an AI system to understand how a decision aligns with company strategy. They can also ask for advice on how team-level signals, such as engagement and capacity, should inform their next steps.
A unified AI system built on trusted workforce data empowers both managers and C-suite leaders by supporting:
- Data-informed decisions: With access to AI-powered insights grounded in their organization’s workforce data, managers can rapidly evolve as people developers. By harnessing real-time signals about team capacity and performance, they can make decisions about resourcing or prioritization that map to both coaching needs and business goals. Particularly for newer managers or those stepping into leadership without formal training, contextual AI offers the situational guidance a generic LLM cannot.
- Better people practices: Key aspects of manager development, such as learning how to boost team engagement, can also occur in the flow of work. For example, to pinpoint areas of friction a manager might ask an AI system to assess trends in team Employee Net Promoter Score—a measure of engagement—in the context of goal achievement. Likewise, they can track how engagement shifts after a change in workload or coaching approach. Dynamic feedback empowers managers to adjust their tactics and improve their decision-making over time.
- Strategy-execution alignment: For executives, AI can offer real-time visibility into how manager-level decisions align with organizational priorities. While executives previously relied on delayed secondhand reports, they can now leverage unified people and business data to assess which managers are driving key outcomes and building thriving teams, compared with those that may need additional support to succeed in their roles.
- Manager effectiveness insights: As upper leadership uses connected workforce data to evaluate manager effectiveness, they can begin to make more informed talent and promotion decisions. Along with deploying targeted development support for specific leaders, they can identify employees who are primed to step into management roles based on proven impact and cultivate the next generation of effective managers.
Too often, companies invest in generalist LLMs and assume value will emerge naturally through individual use. But unlocking ROI and reshaping management for the better begins by treating AI as a system that can support leaner structures by enhancing decision-making at every level.
Rewriting the logic of management with AI
Organizations are clinging to legacy management structures because they’re familiar—not because they always work. AI can help break the addiction to an outdated approach that routinely promotes employees into management roles without assessing readiness, then expects managers to lead without effective support or timely data.
Instead, a management model built for the speed of today’s business can leverage contextual AI that drives alignment across the organization. Insights based on connected workforce data empower managers to directly apply company strategy in day-to-day choices, while illuminating frontline execution for upper leadership.
Yet, AI is only one facet of this shift—an opportunity for a deeper rethinking of how we develop and promote leaders. Organizations that first define effective leadership and management potential, and complement it with technology, will create the conditions for AI to deliver on its promise and elevate the potential of both current and future management roles.
