Field Leadership: Why It’s More Relevant Than Ever

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Between 2008 and 2012, as explored in Part 1, ERP systems gained traction across industries, many enterprises raced to implement them in hopes of unlocking automation and scale. Yet, in the field, the same breakdowns kept recurring. Processes that were never streamlined were now being force-fitted into digital workflows. Arul, working with multiple clients during that period, often shared a metaphor with his teams, “This is like running a high-speed train on tracks built for diesel locomotives.” 

Without process clarity and human discipline, no technology could deliver transformation.

This lesson still applies. Today, the high-speed train is Artificial Intelligence. But the organisational tracks have barely evolved.

From Automation to Alignment

Enterprise AI has shifted from promise to presence. Organisations are experimenting with generative assistants, predictive algorithms, and AI copilots. However, success remains elusive for most. For relational and structural reasons.

In most large-scale implementations, there exists a silent misalignment between business intent, leadership posture, and the lived reality of operations. Leaders are often drawn to AI’s promise of performance—improved efficiency, reduced costs, faster delivery. But these benefits assume a level of organisational readiness that may not exist. As in the ERP era, AI cannot simply be installed on top of chaos.

True alignment begins not with systems, but with leadership. It requires confronting the fragmented realities across functions, countries, and teams and thereafter, choosing to harmonise intent with process. In many enterprises, what leaders envision is not what the frontline experiences. Governance models are top-heavy, and transformation agendas remain detached from operational complexity. Without aligning perspectives across the organisational spine, from strategic decision-makers to execution layers, AI will amplify fragmentation, not resolve it. This is where presence must precede performance.

Related: What the World's Leading Expert Says We Still Don't Know About AI

The Misalignment Between AI and Process

Across industries, AI is being deployed with the promise of automation, acceleration, and agility. But acceleration without alignment fractures the organisation. Leaders want insights, answers, and efficiencies—but when systems are not ready, the very intelligence that AI offers becomes noise.

A case from the freight forwarding industry illustrates the gap. In a global rollout of the Wisetech / Cargowise platform, every country operated with its own version of the order-to-cash process. Leadership, meanwhile, viewed the operation through a global lens. The divergence led to failed implementations and frustrated teams.

The resolution did not come from technology. It came from leadership-led transformation. Process improvement workshops were held in every country. Teams mapped the full swimlane of the operation, removed non-value-adding steps, and standardised wherever possible. Only after that discipline was established did the technology succeed. AI is no different. It demands the same intentionality and the same leadership presence.

Speed without presence creates incoherence. AI without stewardship invites collapse.

Why Presence Requires Leadership Maturity

To close the gap between performance and presence, leaders must embody presence themselves. This involves three shifts:

• From Control to Containment
  Leaders must move from trying to control AI outcomes to designing environments where AI can contribute meaningfully. Containment means holding space for uncertainty, experimentation, and reflection.
• From Urgency to Integrity
  Presence requires a willingness to slow down. Rushing AI into production without engaging frontline realities will only widen the gap. Leadership integrity means honouring the lived experience of teams.
• From Silence to Dialogue
  Many AI systems are deployed without field-level dialogue. Presence-oriented leaders convene process redesign workshops, ethical reviews, and reflective learning loops—not just project steering meetings.

As highlighted by the recent MIT Sloan report, successful AI adoption hinges not on technical integration, but on organisational maturity and alignment.

The Role of Dialogue in Transforming the Field

Closing the presence gap means transforming the field in which AI operates. It includes how employees experience decision-making, how values are encoded in process, and how humans and machines learn from each other.

This started with process-mapping workshops, waste identification, and on-the-ground listening, a unified process was created—one that honoured local needs while aligning to global standards. Only then was the platform customised and re-rolled.

Workshops, facilitated dialogues, and secure base leadership environments enable the field to mature. Without them, AI will always reflect dysfunction rather than resolve it.

As Mark Dreyer notes in his reflections on leadership and AI, confidence is built through conversations that allow people to witness the why and how of AI’s presence in their workflow. This is the work of leaders, not systems architects.

This is what AI implementation needs as well. It needs leadership to treat process design as sacred work. To bring people together. To make meaning. To listen. And only then, to implement.

Leadership as Field Stewardship

The concept of field stewardship originates in systems thinking and relational leadership—where the leader is not the sole actor, but a convener of environments, values, and conditions that allow right action to arise.

In the context of AI, this means moving beyond deployment strategies and into cultural design. Leaders must ask: What kind of field are we creating for AI to learn from? If the organisational field is marked by urgency, fractured communication, and distrust, AI will amplify these. If the field is one of reflection, safety, and shared purpose, then AI has the capacity to mirror and enhance that maturity.

This is not abstract idealism. Presence can be structured. Field stewardship looks like:

• Creating container spaces: such as reflective dialogues, process-improvement workshops, or decision-making forums where human insight is not bypassed by automated logic.
• Establishing field ethics: where AI use cases are not just evaluated for efficiency, but for resonance with organisational values, dignity, and care.
• Holding paradox and complexity: not rushing to closure, but cultivating discernment, where both innovation and alignment are considered before action.

Related: 6 Ways to Build Leadership That Drives Transformation

AI is a mirror to the organisation’s maturity. The field it reflects is the field leaders are responsible for tending. As emergent AI systems continue to grow in presence and influence, so too must leadership evolve from transactional to transformational.

This is the threshold. The old model—of owning the tool and measuring performance—must now give way to fielded leadership: presence over pressure, design over dominance, relationship over rules.

A Call to Action

Leaders reading this must ask themselves:

• Are we stewarding a field where AI can truly serve the human good?
• Do our systems reflect clarity of process, integrity of purpose, and alignment across levels?
• Are we willing to pause, dialogue, and design—not just implement?

The time to lead is now. Not through control, but through presence. Not by accelerating, but by anchoring. Steward the field—and the future will emerge, not as a performance metric, but as a shared becoming.

AI will not bridge this gap on its own. The bridge must be built by leaders who are willing to slow down, listen closely, and transform their organisations from within. Performance may be the goal. But presence is the path.

Glossary

• Presence-based Leadership: A leadership stance grounded in attentiveness, containment, and relational awareness rather than urgency or control.
• Containment: A leadership capability to hold space for complexity without premature action.
• Secure Base Leadership: A model developed by Prof George Kohlrieser focused on creating safety, trust, and challenge in teams.
• Swimlane Mapping: A process improvement method used to visualise end-to-end workflows across functional areas.
• Field: In emergent frameworks, the relational and energetic environment in which human-AI systems interact.

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