Author name: Khushi Sharma

The Future of Work: Will Your Next Manager Be an AI? Imagine this: you walk into work Monday morning, expecting your manager’s voice in your inbox. Instead, you receive a notification from AI-Manager Pro laying out tasks, deadlines, and performance metrics, without any human in sight. It sounds futuristic. But the reality? More plausible than many realize. Across industries, AI in management is already stepping toward managerial responsibilities with sometimes assisting, sometimes deciding. And for business leaders interested in where future of work in corporate culture is heading, the question isn’t if, but how soon and how wisely. Redefining Management: What Being a Manager Means When AI Steps In The manager’s traditional role has always blended strategic decision-making, people leadership, emotional intelligence, and oversight. But now AI managers are reconfiguring parts of that mix. Today, “manager” doesn’t necessarily mean someone who sits in meetings; it could mean an algorithm optimizing workflows, assigning tasks, or even evaluating performance.  Recent research confirms this shift. A 2025 McKinsey survey shows 92% of companies plan to increase AI spending over the next three years, yet only about 1% consider themselves AI-mature. Simultaneously, employees are using generative AI far more than top executives expect. For example, while only 4% of executives believe their employees use generative AI for more than 30% of their daily tasks, about 13% of employees report they do.  In this environment, humans must focus on future work skills like empathy, vision, innovation, adaptability, while leaving repetitive AI decision-making to algorithms. This balance between human leadership and automation defines the next phase of digital workplace transformation.  The Promise and the Problems of AI-Managers AI in management brings efficiency, speed, consistency and sometimes a little cold precision. The potential upsides are compelling:  Productivity gains: AI can handle repetitive tasks, monitor performance continuously, and flag issues instantly. BCG reports that over 80% of corporate affairs tasks are automatable or supportable by AI, freeing up 26-36% of time in roles heavy in routine, analytics, and content work.   Fairness in evaluations: When employees believe human bias may be at play, they often find algorithmic evaluation more trustworthy. Research from the University of New Hampshire shows that when bias is expected from a supervisor, people trust objective/computer-based evaluations instead.  But the flip side is real:  Transparency issues: How does the algorithm decide? What data does it use? Employees can feel uneasy if decisions seem opaque.  Bias baked in: If training data is skewed, AI may replicate or amplify existing unfairness, even if it’s “objective” on the surface.  Human needs ignored: Empathy, understanding, adaptability, all these soft skills are difficult for AI. When humans report to machines, satisfaction and employee experience sometimes drop.   In sum, while AI workplace transformation promises a sharper, more consistent performance, organizations need guardrails for ethical design, clarity, human oversight to avoid unintended consequences.  What Employees Actually Think: Excited, Apprehensive, or Somewhere In Between When people talk about “AI bosses,” responses tend to split between cautious optimism and mistrust. Recent surveys reveal interesting contrasts:  Roughly 75% of workers are comfortable collaborating with AI agents for support tasks, but only about 30% are okay with AI actually acting as their manager or making major decisions. Employees are more ready for AI than many leaders believe. For instance, in McKinsey’s “State of AI” studies, employees self-report much higher usage of generative AI workplace automation tools than their leadership expects. These attitudes reflect a mix: people see value in AI helping them with scheduling, metrics, logistics but resist when tasks involve judgment, fairness, or personal context. AI may enforce rules reliably, but humans still want recognition, feedback, and emotional nuance, things algorithms struggle with.  Interestingly, hybrid models (AI plus human leader) surface as the most accepted. When AI assists with task delegation or performance tracking, and a human leader translates the data into meaningful feedback, employee satisfaction tends to be higher. Teams feel both empowered and seen.  The Skills Human Leaders Must Double Down On If AI takes over predictable, rule-based management duties, what does that leave for human leaders? Plenty.   Leading in an AI-augmented workplace demands doubling down on distinctly human strengths:  Empathy & psychological safety – Knowing when people need encouragement or flexibility matters. AI lacks the situational judgement to understand personal contexts.  Vision, creativity, and innovation – Charting courses into an uncertain future, imagining what isn’t there yet. AI can support but cannot originate a purpose.  Ethical judgment & fairness – Ensuring algorithms are fair, transparent, and aligned with the organization’s values. Leaders need to interpret outputs, check biases, and maintain trust.  Data literacy & oversight – Leaders should understand enough about AI’s workings to ask critical questions: what data is used, how metrics are weighted, which models are involved.  Change management & communication – As AI takes up more space, resistance is inevitable. Leaders who successfully shepherd adoption, explain “why,” listen to concerns, and adjust roll-out will succeed.  Supportive data backs this. For example, research on managerial skills and AI in management indicates most skills like communication, recruitment, complex decision-making and innovation are augmented rather than replaced; only more administrative or simple tasks are likely to be fully automated.   Ethical, Operational, and Organizational Challenges AI-managed work isn’t simply about better algorithms. Several serious challenges loom:  Fairness, bias, and legal exposure: If AI decisions affect promotions, pay, or termination, companies must ensure fairness. Studies show that even AI managers are not free from stereotypes, how they’re perceived (or how they behave) can still reflect human biases.   Transparency and trust: Employees want to understand why decisions are made. Black-box decision-making breeds suspicion and disengagement.  Privacy concerns: Using personal data for performance evaluation, tracking, behaviour prediction can feel invasive. Studies show that privacy intrusion mediates whether employees feel the organization is attractive or not.   Well-being: AI’s efficiency can increase pressure. If every metric is tracked, deadlines become rigid, slack time vanishes and stress rises. Well-being depends on good design: buffer periods, reasonable thresholds and human intervention.  Workforce effects: Some roles face reduction or transformation. McKinsey data