This Advisor helps business leaders navigate the emerging challenge of AI-driven transformation thresholds—critical points where expertise shifts from scarcity to abundance. Drawing lessons from genomics and other rapidly evolving domains, it introduces a framework for recognizing when AI is poised to disrupt core capabilities and outlines strategic responses across four categories: commoditized capabilities, augmentation opportunities, transformation candidates, and resilient differentiators.

Benjamin Herndon and Ben Szuhaj trace resilience failures not just to structural flaws but to the limits of human cognition in the face of complexity. Through lessons drawn from NASA’s Challenger and Columbia disasters, they show that true resilience depends on seeing weak signals, surfacing dissent, and learning continuously — capabilities that human-machine teaming and thoughtful AI integration can unlock. Human and machine, working together, create foresight and learning beyond any individual capability.

Benjamin Herndon and Ben Szuhaj trace resilience failures not just to structural flaws but to the limits of human cognition in the face of complexity. Through lessons drawn from NASA’s Challenger and Columbia disasters, they show that true resilience depends on seeing weak signals, surfacing dissent, and learning continuously — capabilities that human-machine teaming and thoughtful AI integration can unlock. Human and machine, working together, create foresight and learning beyond any individual capability.

Randal Whitlatch delivers a practical framework for managing today’s increasingly volatile supply chains. He demonstrates how embedding prestructured, legally sound, operationally consistent protocols transforms supply chain risk from a source of fragility into a pillar of organizational resilience. By aligning legal rigor with repeatable processes, companies can mitigate disputes, preserve commercial integrity, and sustain customer trust — critical foundations for maintaining business continuity and securing lasting advantage in uncertain times.

Through candid storytelling and sharp analysis, François Pumir shows how fear-based planning, driven by old narratives and past failures, inadvertently breeds fragility instead of true readiness. He invites us to replace exhaustive frameworks with a new leadership practice rooted in clarity and direct perception, offering practical steps to cultivate a culture that responds fluidly to emergent realities. It’s a transformative reminder that true resilience starts within by shifting mindset from fear to clarity.

Bill Fox maps out six “territories of resilience,” from shifting societal expectations and regulatory complexity to technological and climate upheaval, and proposes four pillars that organizations can use to anchor resilient design. Fox’s article is rich with examples: logistics companies thriving through ecosystem collaboration, manufacturers succeeding by fusing user-centric design with systemic thinking, and firms reviving their foundational “why” to attract and mobilize new generations of talent. His message is clear: those who design resilience into their strategy are designing advantage in a world of constant upheaval.

This issue of Amplify brings together leading thinkers and practitioners whose work redefines how we conceive, cultivate, and measure resilience for the challenges ahead. Their contributions illuminate how foresight, adaptability, and governance intersect to create organizations that are not only robust but regenerative. Together, they remind us that resilience is not a destination but a continuous act of design, one that equips organizations to shape, rather than merely survive, the future.

ADL’s Nicholas Johnson, Siddharth Pai, Olivier Pilot, Joshua Sanz, and Eystein Thanisch explore how autonomous AI agents are reshaping customer interaction, data ownership, and advertising strategies. Their findings highlight that designing for resilience is essential for retailers to stay competitive as AI agents transform the market. Original experiments carried out by the authors reveal how these AI-driven disruptions challenge traditional retail media players, underscoring the urgent need for resilience by design in the face of rapidly evolving AI ecosystems.

ADL’s Nicholas Johnson, Siddharth Pai, Olivier Pilot, Joshua Sanz, and Eystein Thanisch explore how autonomous AI agents are reshaping customer interaction, data ownership, and advertising strategies. Their findings highlight that designing for resilience is essential for retailers to stay competitive as AI agents transform the market. Original experiments carried out by the authors reveal how these AI-driven disruptions challenge traditional retail media players, underscoring the urgent need for resilience by design in the face of rapidly evolving AI ecosystems.

Cutter Expert Curt Hall explores how neuro-symbolic AI — a fusion of data-driven neural networks and rule-based symbolic reasoning — can address key limitations in today’s agentic AI systems. By combining pattern recognition and adaptability with logic, structure, and explainability, neuro-symbolic architectures enable autonomous agents to operate with greater reliability, transparency, and compliance. He highlights practical use cases where this hybrid approach mitigates risks such as hallucinations, planning errors, and opaque decision-making, laying the groundwork for trustworthy, real-world autonomy.

In this Advisor, Noah Barsky explores three essential lenses — profit precision, ratio relevance, and cash clarity — that help leaders make smarter financial decisions. Building on prior insights, it shows how financial fluency equips executives to align strategy with reality, drive efficiency, and sustain enterprise health in an age of accelerating change.

In this Advisor, ADL’s Michael Papadopoulos, Olivier Pilot, and Eystein Thanisch explore how AI’s greatest challenges in 2025 aren’t about intelligence, but context. From viral drive-through debacles to high-stakes corporate missteps, the problem isn’t capability — it’s integration. As top models plateau on benchmarks, their real-world performance often falters when they’re not tightly aligned with human workflows, norms, and systems.

When people believe in what they are doing and feel a part of it, they create better customer service. When employees feel psychologically safe to innovate, they push their thinking further and happily voice their ideas. They are excited about going to work, don’t often miss work, feel like they are a part of the solution, and don’t quit. And if people aren’t leaving, then companies aren’t spending billions to replace them.

Getting It Right

So who’s currently getting this right? And what does that look like?

This Advisor explores agentic AI — the next phase beyond generative AI — where systems not only generate outputs but also autonomously plan and execute tasks across enterprise environments. By combining LLMs with orchestration, tool integration, and feedback mechanisms, agentic AI enables end-to-end workflow automation with minimal human oversight. While the potential is substantial, technical, organizational, and governance challenges still limit deployment to early-stage pilots in most enterprises.

In this Advisor, Bill Fox traces a personal journey of purpose-driven leadership sparked by a desire to fix what’s broken in organizational change. Inspired by Steve Jobs’s belief in “connecting the dots,” he shares three pivotal steps: becoming a catalyst for change, taking small actions aligned with a larger mission, and participating in the emerging future by sharing insights. Each step illustrates how clarity and impact emerge when purpose guides action.

This Advisor explores how agentic AI — autonomous systems capable of reasoning and acting across multi-step workflows — is rapidly reshaping enterprise operations. Drawing on field research and market signals, it distinguishes between firms embedding agents into core processes and those stalled in pilot purgatory due to fragmented data and governance. Early winners aren’t just using better models; they’re better prepared — with mature data pipelines, governed toolchains, and institutionalized knowledge.

While often overshadowed by buzzwords and tech trends, financial literacy enables leaders to spot early distress signals, assess business health, and make grounded decisions. This Advisor presents two enduring insights: first, while earnings drive headlines, it’s the balance sheet that determines survivability — highlighting the importance of stewardship over growth at any cost. Second, that sales growth rate serves as a company’s “speed limit,” providing critical context for interpreting operational changes and ensuring strategic alignment. In times of transformation, disciplined financial insight remains a leader’s most dependable tool.

This Advisor distills insights from interviews with 48 healthcare and pharmaceutical leaders on the challenges and best practices for cross-sector collaboration using data and technology platforms. Key barriers include trust, governance complexity, misaligned incentives, and limited scalability. Success hinges on shared agendas, clear accountability, streamlined contracts, and strong leadership — all essential for embedding technology in a way that delivers lasting value to stakeholders.

Part II of this Amplify series on disciplining AI explores how to rigorously evaluate and govern systems amid rising concerns over safety, performance, and accountability. As GenAI plateaus in both capability and ROI, this issue argues for a shift from benchmark-based assessments to context-specific evaluations emphasizing oversight, adaptability, and explainability. It offers philosophical and technical frameworks for aligning AI outputs with human-defined goals — underscoring that effective AI integration demands continuous learning, human judgment, and strategic foresight.

ADL’s Dan North examines the engineering discipline of AI evaluations, arguing that this is the locus of an LLM’s translation from task-agnostic capabilities measured by benchmarks to tech that is setting specific and ready-to-deliver success. North emphasizes the ultimately human nature of this discipline. An organization must define the kind of outputs it is looking for through an inclusive process involving customers and stakeholders, alongside engineers.

Paul Clermont reminds us of a crucial but under-recognized trait of true AI: learning and improvement in response to feedback independent of explicit human design. The implementation of human requirements is thus both highly feasible and dialogic. Clermont stresses the high level of responsibility borne by humans when interacting with AI. Critical thinking about inputs and outputs, and awareness of both objectives and social context, remain firmly human (and sometimes regulatory) responsibilities — no matter how widely terms like “AI accountability” have gained currency.

Joe Allen joins the critique of benchmarks as the key means of measuring AI systems, with a focus on agents. Given the open world in which they operate, agents have autonomy over both planning and execution, and Allen argues they have already outgrown even the criteria used to evaluate LLMs. As self-organizing systems, agents should be assessed on their ability to follow their own plans consistently while adapting to unforeseen eventualities — an evaluation that cannot be fully scripted in advance. Drawing on direct experience, Allen details a suite of techniques that can be used to track and improve agent performance in terms of internal coherence, adherence to a context model, and more.

Chirag Kundalia and V. Kavida propose using survival analysis to understand when, and under what circumstances, an AI system might need maintenance or replacement. This approach involves modeling the intrinsic and extrinsic factors that could render a system no longer fit for purpose. It also requires organizations to define the standard of output the system must deliver, monitor that performance, and scan the horizon for relevant externalities (e.g., superior technology). Although survival analysis cannot anticipate every eventuality, modeling the future of an AI system enhances budgeting, compliance reporting, and strategic planning.

ADL’s Michael Papadopoulos, Olivier Pilot, and Eystein Thanish argue that with model performance plateauing, the critical differentiator for AI in 2025 is how the technology is integrated into the specifics of an organization — and the context in which it will operate. Benchmarking, which is concerned with models out of context, is of diminishing applicability. After reviewing some entertaining but troubling examples of theoretically capable AI systems disconnecting disastrously from data, business rules, and basic plausibility, they propose an evaluation framework more attuned to contemporary challenges to prevent similar outcomes.

This Advisor explores how leader character can strengthen board governance and support ethical, effective decision-making in the face of growing ESG demands. It argues that traits such as courage, integrity, humility, and judgment are essential for directors to fulfill their fiduciary duties while navigating complex stakeholder expectations. By embracing character-driven leadership, boards can move beyond compliance to shape resilient, purpose-led organizations capable of long-term value creation.