Leaders face environments shaped by fast-moving AI integration, volatile markets and shifting workforce expectations. Research confirms that leaders who adjust their style to situational demands achieve higher team performance and engagement (Del Pino-Marchito, Galán-García, & Plaza-Mejía, 2025). The principle behind situational leadership, that no single style fits every moment, gains urgency when artificial intelligence introduces both capability and complexity into business decisions.
Adaptive leadership responses are now essential. Sott and Bender (2025) found that companies using adaptive and situational leadership approaches improved decision quality by 23% during recent crises. The demand for leaders who can read context, pivot styles and integrate technology has never been more pressing. It is in these intersections of people, machines and strategy that adaptive leadership and situational leadership combine to produce resilient, high-performing organizations.
A Practical Matrix of Leadership Styles for AI-era Scenarios
Organizations deploying AI tools often confront diverse readiness levels among employees. Studies show teams vary widely in comfort and competence with new technology, influencing how leaders should engage them (Maseko, Van Wyk, & Odendaal, 2019). The following matrix summarizes leadership style recommendations documented in organizational change literature, mapped against levels of follower readiness and AI-specific challenges:
| Leadership style | Follower AI-readiness | Typical AI-era scenario | Core coaching move |
|---|---|---|---|
| Directing | Low competence, low commitment | Employees newly trained in generative AI tools | Provide clear instructions, monitor closely |
| Coaching | Rising competence, variable commitment | Teams experimenting with AI forecasting | Co-create metrics, encourage learning |
| Supporting | High competence, wavering commitment | Data scientists managing algorithmic bias concerns | Offer resources, celebrate progress |
| Delegating | High competence, high commitment | Established AI/ML teams scaling automation | Set outcome goals, allow autonomy |
This matrix is grounded in evidence that leadership styles must align with both human and technological readiness, rather than traditional hierarchy (Schulze & Pinkow, 2020). Leaders who ignore this alignment risk eroding trust and slowing transformation efforts.
Adaptive Leadership Meets Situational Leadership
Organizations that successfully navigate AI disruption often combine adaptive leadership with situational leadership. Schulze and Pinkow (2020) describe how enabling leaders foster “adaptive space” where ideas can safely collide and generate innovation. Such leaders facilitate candid dialogue and allow emerging patterns to guide decisions, avoiding rigid control that stifles responsiveness.
Integrating this approach with situational awareness allows leaders to adjust quickly as conditions shift. Research shows that effective leaders scan for signals of technological change, frame emerging issues with clarity, match their leadership style to team readiness and iterate frequently based on feedback (Abositta, Adedokun, & Berberoğlu, 2024). Leaders who neglect this process risk misapplying authority, either micromanaging skilled teams or abandoning teams who need clear direction.
Team Coaching and Reflective Practice as Multipliers
Research highlights team coaching as a powerful tool for embedding adaptive and situational approaches into day-to-day practice. Maseko et al. (2019) found that structured team coaching improved technology adoption rates and overall team effectiveness by over 30%. In fast-changing environments like AI deployment, such interventions ensure that learning spreads quickly and that teams build resilience together.
A key practice is integrating reflective pauses after significant milestones or sprint cycles. Asking questions like, “Which leadership style helped us move forward?” or “Where did we need more guidance?” builds a shared language and empowers teams to request the support they need. Reflective routines are increasingly viewed as critical in bridging technical and human elements of leadership development (Del Pino-Marchito et al., 2025). Such practices ensure that lessons from AI deployments inform not only technology roadmaps but also leadership strategies.
Reflective Questions for Every Manager
- Which leadership styles did I employ today, and did they fit each colleague’s AI skill level?
- When might team coaching have been more effective than a directive approach?
- How does my next one-on-one advance our leadership development goals in areas that AI cannot replicate?
- What signals psychological safety during turbulent organizational change in my team?
- Am I modelling ethical AI in leadership, or relying too heavily on automated decisions?
Leadership Development and Organizational Change
Traditional leadership training often delivers static models unfit for AI-era complexity. In contrast, progressive organizations embed situational leadership and adaptive leadership into real-time learning environments. Del Pino-Marchito et al. (2025) emphasize that training grounded in real business dilemmas, especially those involving AI decision-making, enhances knowledge transfer and leadership agility. Leaders who rehearse shifting styles in safe settings are significantly more likely to adjust effectively under pressure.
Adopting AI frequently triggers organizational redesigns, with new roles, altered workflows and evolving governance structures (Schulze & Pinkow, 2020). Such changes heighten uncertainty, making organizational change management a critical leadership skill. Leaders who adapt their style to the moment, combining directive clarity with participative dialogue, report smoother transitions and less employee resistance (Sott & Bender, 2025).
Manager Coaching in the Flow of Work and AI Augmentation
Managerial coaching increasingly happens “in the flow of work,” rather than in formal sessions. Studies show that leaders who openly discuss shifts in their leadership style foster learning cultures that extend beyond individual performance (Maseko et al., 2019). For example, a manager might share: “I’m moving from a coaching to a supporting role because our AI tool is now stable.” This transparency turns daily interactions into micro-lessons in manager coaching and adaptive thinking.
AI tools offer tremendous potential to support leadership decisions, from surfacing data trends to predicting team stress levels (Abositta et al., 2024). However, AI cannot choose the correct relational stance. Leaders remain accountable for determining when to instruct, coach, support or delegate. Evidence suggests that leaders who combine human judgment with AI insights reach decisions 17% faster and with higher quality outcomes (Abositta et al., 2024). The goal is augmentation, not abdication, a principle central to responsible AI in leadership practice.
Bringing it All Together
Situational leadership retains its power because it scales from small teams to entire organizations, adapting to both human and technological contexts. When paired with adaptive leadership, it helps leaders navigate rapid technological shifts while preserving engagement and trust. Embedding these practices through team coaching, tailored leadership styles, modern leadership development methods, and clear communication during organizational change equips leaders to handle the complexity AI brings.
Leaders who consciously match their style to readiness, context and technological realities don’t merely survive AI disruption, they cultivate teams that out-learn and out-adapt competitors. That is the defining advantage in the AI revolution.
