Stabilizing in the skies of AI disruption

In aviation, stability is a matter of life and death. Apply a disturbance to an aircraft (e.g., turbulence, a gust of wind, or a weight shift), and the type of stability engineered into its design determines what happens next. Some aircraft return to their original state smoothly. Others continue to diverge until they’re out of control.

The same is true for organizations facing the disruptive force of artificial intelligence. While many executives focus on AI’s potential to optimize or innovate, fewer ask a more fundamental question: What kind of stability profile does my organization exhibit in the face of AI disruption? Will it return to a steady state with greater efficiency? Settle into a new equilibrium entirely? Or oscillate out of control and fail to recover?

This article explores how business leaders can identify their organization’s “AI stability profile”—and more importantly, how to adapt their strategies in people, process, and technology to ensure they don’t stall in midair.

Understanding organizational stability through an aviation lens

Aviation recognizes multiple forms of stability. For simplicity, let’s focus on three:

1. Positive stability: After a disturbance, the aircraft naturally returns to its original flight path.

2. Neutral stability: The aircraft stays in its new state after disturbance without further divergence or return.

3. Negative stability: The aircraft continues to deviate from its intended flight path, increasing the risk of loss of control.

These profiles align surprisingly well with how organizations are responding to the wave of AI disruption.

1. Positive organizational stability

These organizations experience disruption—perhaps through the automation of internal processes or the restructuring of data operations—but ultimately return to familiar business models. AI is harnessed to enhance what already exists: improved efficiency, enhanced forecasting, and better decision-making. Think of it as sustained optimization.

2. Neutral or redefined stability

In these organizations, AI doesn’t just improve operations—it alters the business model. New markets emerge, products shift from physical to digital, and value delivery mechanisms change. They don’t return to what they were. They settle into something new.

3. Negative stability (Instability)

These organizations experience repeated oscillations without regaining control. They misread the technology opportunity, underestimate the cultural changes required, or fail to align leadership. AI initiatives stall or fail outright. In some cases, these firms exit their markets entirely or are acquired.

The key for leaders isn’t to guess which profile they fall into—it’s to recognize the signals that indicate their current trajectory and respond accordingly.

AI as a disruptive force: Patterns and signals

Historically, disruption comes in forms that are slow at first, then sudden. Think of the shift from film to digital photography, or from taxis to rideshare platforms. AI disruption shares some common traits with past shifts but introduces a new twist: it evolves faster than your team does—or doesn’t.

Recent research from the World Economic Forum notes that AI will disrupt 44% of workers’ core skills by 2028¹. Gartner predicts that by 2026, generative AI will be a workforce partner to over 80% of enterprises². But not all disruption is created equal. Here are three signal categories to track:

1. People signals

• Skill mismatches: Roles remain, but the competencies required shift dramatically.
• Behavioral resistance: High performers are unwilling to adapt to AI-informed workflows.
• Employee churn: Talent with digital fluency is increasingly drawn to employers that are more AI-progressive.

2. Process signals

• Shadow AI: Departments adopt AI tools without governance or integration.
• Workflow stagnation: Productivity levels off even after the introduction of an AI tool, indicating a poor fit or inadequate adoption.
• Brittle change: Process improvements break under variation, revealing a lack of organizational resilience.

3. Technology signals

• Point solution overload: Multiple, disconnected AI tools with no platform strategy.
• Data fragility: AI systems fail due to poor data quality, volume, velocity, or governance.
• Pilot purgatory: Repeated PoCs with no methodology to focus on value and whittle investments to those that truly impact business outcomes.

Disruption often begins at the edges and moves toward the core. The early signs of destabilization may not be evident in financial performance, but rather in how your people, processes, and platforms behave under stress.

Choosing a stabilization strategy

Once you’ve identified your likely stability profile, the next move is intentional stabilization. This is less about resisting disruption and more about controlling your response to it.

If you’re returning to a known state (Positive stability)

This is a great place to be — if your market isn’t shifting underneath you.

• Prioritize operational excellence by embedding AI into key functions such as supply chain, finance, and customer service. Noting AI is NOT a one-size-fits-all.
• Invest in AI literacy across teams to ensure long-term adaptability.
• Guard against complacency—you may stabilize too early and miss opportunities to redefine your model.

Use AI as a tailwind, not just a toolkit.

If you’re evolving into a new state (Neutral stability)

You’re redefining your business. That’s exhilarating—and dangerous.

• Redesign value streams: Re-evaluate where and how value is created and delivered.
• Adopt an experimentation model: Build fast, fail smart, scale selectively. Very selectively.
• Rewire incentives to reward cross-functional innovation and risk-taking.

Stability comes from clarity, not control.

If you’re drifting toward instability (Negative stability)

Now is the time to pull up before the stall becomes unrecoverable.

• Simplify scope: Identify one high-impact, achievable AI initiative to win back trust and clarity.
• Establish governance: Set a strategic framework for AI use across the organization—guardrails, not guard towers. To read more on how governance can be used to drive creativity, read Unlocking creativity through generative AI governance.
• Bring in external catalysts: Sometimes stability requires external expertise, new partnerships, or new leadership perspectives.

Don’t confuse activity with lift.

The leadership mindset: Stability is a strategic choice

Aircraft are designed for specific types of missions, and so are organizations. The role of leadership is not to resist instability but to understand its purpose. Some missions require agile aircraft. Others need stability at all costs. The same is true in business.

Here’s what the best leaders are doing right now:

• Framing AI disruption in strategic terms, not just technical ones.
• Bridging the gap between technologists and business operators, so that AI capabilities align with market needs.
• Designing adaptable operating models—modular processes, flexible talent, and platform-based technologies.
• Know your aircraft – not all AI is suitable for your business model, nor will using AI ubiquitously produce positive outcomes.

The best leaders aren’t just implementing AI. They’re redesigning the business to remain stable amidst disruption while flying faster than ever before.

Final approach

AI is the strongest gust of technological turbulence many organizations have ever faced. It offers the possibility of unprecedented performance gains, but it also threatens to push unprepared businesses into unrecoverable spins.

You don’t need to become an AI-native organization overnight. But you do need to know whether your current course is holding, drifting, or deteriorating—and act accordingly.

The aircraft metaphor isn’t just poetic. It’s instructive. Because in aviation, stability isn’t about eliminating movement. It’s about managing it, predicting it, and returning to control when it matters most.

The same goes for your business.