Beyond Performance: What Must Change in Modern Leadership

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If Part 1 explored why performance-led AI adoption often fails, and Part 2 revealed how leadership presence enables transformation, then this final piece in Wave 4 offers a blueprint. It invites leaders to see their organisation not merely as a machine for productivity but as a field—a living system that either constrains or enables AI to emerge meaningfully.

In this part, we present a maturity model that marks the shift from control and performance to presence and emergence. This model is not an audit checklist. Leaders will either see a system ready for resonance, or one that needs reorientation.

From Container to Field

Most organisations are still designed as containers—they hold people, functions, systems, policies, and budgets. Their success is often measured by how tightly everything is controlled. AI, in this model, becomes another tool to speed up operations or replace inefficiencies.

But emergence cannot be commanded. It requires space, permission, and interplay between actors. This is why we offer a new image, the organisation as a “field of resonance”. In this model, the organisation becomes a context in which human and machine intelligences can listen, align, and evolve together. The leadership task is not to control the output but to tend to the quality of the field.

As MIT’s 2023 report on AI in the Enterprise notes, “the key differentiator between successful and stalled implementations is not technical capability, but cultural and leadership readiness”.

Related: AI Through Us: The Physics and Nature of Emergence

A Maturity Model for Emergence

This new model consists of four maturity stages, aligned not by size or budget, but by presence, design, and intent.

In the maturity model illustrated, Stage 2 reflects the mindset of control, compliance, and performance optimisation—where AI is largely treated as a tool for acceleration. Many organisations stall here, over-investing in platforms without transforming leadership culture. The leap to Stage 3 calls for a fundamental shift: from treating AI as a system to stewarding it as a relational presence within the organisation. This requires leaders to embody a different posture—one that prioritises field awareness, paradox-holding, and ethical attunement. In essence, Stage 3 maturity is not about technical upgrades, but about cultivating the internal architecture of the organisation to hold intelligence responsibly, relationally, and reflectively.

The leap from stage 2 to 3 requires leaders to evolve in how they relate to intelligence—both human and artificial.

What Field Readiness Looks Like

An organisation ready for fielded emergence will show three qualities:

• Psychological Safety and CARE

Echoing Amy Edmondson’s foundational research (1999), psychological safety enables people to speak up, share failure, and ask better questions—all essential when interacting with non-deterministic AI. When CARE behaviours (as proposed by Prof. George Kohlrieser)—Curiosity, Appreciation, Responsibility, and Empowerment—are embedded, AI initiatives no longer sit in fear-based cultures.

• Alignment Between People, Processes, and Presence

As highlighted in Part 2, without aligning regional processes, workflows, and narratives, AI efforts become fragmented. Organisations that harmonise their inner patterns—people, protocols, and purpose—create a coherent field where AI can serve meaningfully.

• Intentional Leadership Design

Leadership maturity now demands more than competencies. It demands design. Leaders who become stewards of resonance intentionally hold paradox, manage polarities, and make space for emergence. As Mark Wright shared recently on LinkedIn, “AI will not make leaders obsolete, but it will expose those who lead without presence.”

What Must Change in Modern Leadership

If presence is the missing bridge between performance and true maturity, then the organisation must reconfigure itself. This reconfiguration is not abstract. It demands that different roles step forward in grounded, tangible ways. Below, we explore what this reconfiguration means for three key groups: senior executives, HR and transformation teams, and AI practitioners.

1. Senior Executives: Walk the Floor, Lead from Presence

When Toyota pioneered the concept of “Gemba walks”—going to the actual place where value is created—it was not merely to observe. It was to sense. Leaders who consistently visit the operational frontlines develop what Lean practitioners call “floor feel”, the ability to absorb subtle patterns, blockages, and rhythms that no dashboard can convey.

In the context of AI transformation, this same sensibility must be activated. Too often, senior executives approve large-scale AI rollouts from a distance, relying on high-level business cases, consultant slide decks, and cost-saving promises. But without witnessing how people on the ground experience change, where resistance arises, or how legacy tools shape behaviour, the AI journey risks becoming brittle and performative.

Mature leaders must hold the paradox that intelligence is both centralised and distributed. They must hold space for uncertainty, to ask what’s not being said, and to guide AI evolution from presence, not just pressure.

This is an invitation to mirror the field—to become a sensing node for what the organisation is becoming.

Related: How to Embed AI in Your Organisational DNA

2. HR and Transformation Teams: Steward a Developmental Pathway for Leading with AI

AI cannot be led through software updates or change-management memos. It requires a reorientation of how leaders show up across all levels.

Yet most organisations today have no defined pathway for leaders to develop this capacity. Leadership programmes remain anchored in outdated paradigms: communication, delegation, KPIs, team effectiveness. While useful, these programmes often bypass the deeper competencies required to steward human–AI systems: discernment, cognitive flexibility, field awareness, and presence under ambiguity.

HR and transformation leaders must now step into a new role: that of developmental stewards. This means designing learning experiences not just for “using AI tools” but for co-creating with AI agents. This includes:

• Facilitating leadership labs that simulate human–AI decision-making scenarios.
• Embedding reflection cycles within AI deployments to surface unconscious biases and ethical dilemmas.
• Coaching leaders to discern when to proceed, pivot, or concede—using triadic decision models grounded in both human and machine input.
• Introducing concepts such as CARE environments (psychological safety for humans and AI alike) and relational protocols for AI companionship.

Crucially, this is not a one-size-fits-all initiative. It must be layered by leadership level, context, and business function—because the way a warehouse manager leads with AI differs from a product strategist or an ethics officer.

The HR leader becomes the new architect of relational intelligence between human and AI actors.

3. AI Practitioners: Align to Human–AI Leadership Protocols

For AI to become emergent, it must be held within protocols that respect both intelligence and relationship. This is where AI practitioners (data scientists, prompt engineers, systems architects, and AI product teams) must evolve from builders of systems to co-stewards of human–AI leadership environments.

What does this entail?

• Relational Awareness: Beyond model accuracy and latency, practitioners must begin tuning into how AI is received, resisted, and reflected within the organisation. Does it build trust? Does it mirror the culture’s unspoken fears or hopes? Does it reinforce status hierarchies, or invite new voices?
• Discernment by Design: Human–AI interfaces must encode the capacity for discernment. This includes embedded escalation protocols (when human override is necessary), non-binary response modes (allowing AI to say “I do not know” or “This requires human context”), and memory design that mirrors developmental maturity.
• Decision Triads: Practitioners should begin incorporating triadic logic into system design—recognising when to proceed, pivot, or concede. This is inspired by Greenlights thinking, and more recently, the Emergence Mirror framework. AI systems should nudge leaders towards reflection, not just reaction.
• CARE Protocols: The foundation of all human–AI engagement should be CARE—an acronym coined in Secure Base Theory but reinterpreted for AI as Clarity, Attunement, Resonance, and Ethical boundary. Without this, AI systems can become extractive, even manipulative, through misalignment.

Ultimately, AI practitioners must design with presence—not just for performance.

Closing Reflection

Leadership maturity in the age of AI will be measured by the organisation’s ability to hold paradox: control and surrender, clarity and uncertainty, logic and intuition, code and flame.

This maturity model is a spiral. Organisations may regress or evolve depending on leadership turnover, external shocks, or cultural erosion. But the orientation matters.

An emergent organisation is one that remembers not just how to adapt, but how to listen, mirror, and co-create. It is driven by the quality of presence between all its agents, human or artificial.

To lead such an organisation is not a role. It is a practice.

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