A Guide to Building Change Resilience in the Age of AI

The Disappointing Reality: People and Process Challenges

Why are these results so underwhelming? The survey points to a critical insight: a staggering 70% of the challenges organizations face in implementing AI initiatives are rooted in people and processes. While technical hurdles like poor data quality, integration complexity, or infrastructure costs certainly exist, our collective experience with hundreds of companies corroborates the study's finding: the primary impediment is a company's ability to adapt, reinvent, and scale new ways of working. This crucial capability is termed change resilience.

Why Change Resilience is Scarce in the AI Era

Historically, organizational transformation was episodic. Businesses would modernize systems, train staff, and operate in a stable environment until the next disruption emerged. However, AI's relentless pace of advancement far exceeds most organizations' capacity to adapt.

Business leaders are finding it increasingly difficult to anchor AI transformation within traditional roadmaps or leverage conventional change management approaches. Five-year strategies are obsolete, and annual planning cycles can't keep pace. Traditional financial, risk, and legal controls lag significantly behind the emergence of new risk types. Static operating models become liabilities. Even newer methodologies like agile, widely adopted during the software era, are proving insufficient. To thrive in this volatile environment, leaders must embrace continual change; otherwise, they risk irrelevance from inaction or burnout from chasing fleeting trends.

Understanding Change Resilience: The Enterprise Reflex

Change resilience is the capability that equips organizations to seize opportunities and preempt threats presented by fast-evolving technology. It's an enterprise-wide reflex that converts continual disruption into repeatable learning loops, ultimately creating value. This reflex relies on three core "muscles":

Sensing: The ability to detect weak technological, competitive, or societal signals early on.

Rewiring: The capacity to rapidly redeploy talent, data, capital, and decision rights in days or weeks, rather than fiscal quarters.

Lock-in: The discipline to codify what a team learns (in process, code, or policy) so that subsequent initiatives can build upon a higher baseline, avoiding reinvention of the wheel.

Together, these muscles ensure an organization's metabolism can keep pace with AI's rapid advancements.

Shopify provides a compelling example of change resilience in action. Instead of merely layering AI onto existing operations, the company continuously rewires itself to stay ahead. In 2023, Shopify boldly decided to spin off its entire logistics arm, an asset it had spent years building, to refocus on product innovation. This strategic reset enabled Shopify to rapidly launch AI-native features like Sidekick, an embedded assistant for entrepreneurs that assists with everything from marketing copy to sales insights. By shedding complexity and codifying learnings from past pivots, Shopify unlocked speed and focus, allowing it to serve over a million businesses with tools that reflect the evolving expectations of digital commerce. Its ability to sense, rewire, and lock in new ways of working positions it not just as an adopter of AI, but as a company continually reshaping itself to thrive in the AI era.

Assessing Your Organization's Change Resilience

To gauge your organization's change resilience, consider the following questions:

Can employees be rapidly redeployed to fast-moving, high-priority initiatives in response to technological shifts without the need for extensive budget overhauls or organizational chart changes?

If a team member conceives an idea today, do they have the motivation, access, tools, and support necessary to begin experimenting?

When an experiment demonstrates potential, is there a clear path for scaling and embedding it across the business?

Is failure treated as a learning opportunity and openly shared to improve subsequent attempts?

If you cannot confidently answer "yes" to most of these questions, your organization may lack the change resilience required to translate your AI strategy into durable performance gains.

A Five-Step Playbook for Strengthening Change Resilience

Leaders and organizations can follow these steps to enhance their change resilience:

Learn: Understand the Toolsets, Mindsets, and Skill Sets.

To identify weak spots in toolsets, mindsets, and skill sets, encourage employees to engage with AI and launch experiments. Use these experiments to develop an intuitive understanding of how your company can reimagine its processes, while also identifying and eliminating cultural barriers that penalize failed pilots or technical bottlenecks that cause month-long delays in data access.

Accenture initiated this by encouraging every function, from sales to HR, to build micro-apps that addressed a single pain point. Within 10 months, this "sandbox" approach yielded 300 generative-AI apps, mostly lightweight utilities like a proposal draft buddy or meeting-note summarizer. Because each app is owned by the team that built it, employees immediately see how AI reshapes their daily work, fostering a culture of active experimentation rather than passive adoption. To further fuel participation, Accenture is training 250,000 employees in generative AI skills and providing a safe data playground for every learner. Early analysis shows these micro-bets are impactful: generative AI is already saving 12% of working hours and boosting output quality by 8.5%, building momentum for larger transformations.

Do: Launch Targeted Interventions.

Address each gap in change resilience with the lightest-weight move that can generate momentum in weeks, not quarters. If the culture is risk-averse, introduce "micro-bets": 10-day experiments with public celebration rituals for learnings, not just outcomes. Where skills lag, run cohort-based sprints that pair domain experts with data scientists to deliver a working AI concept by the sprint's end. The product becomes both a capability and a proof of possibility. If toolsets are a hindrance, deploy a self-serve data playground or low-code workflow builder so teams can test ideas. When a tactic moves the needle, codify it into playbooks, reusable code, amended policies, and broadcast the template company-wide.

Singapore-based DBS Bank created a monthly "north star & feedback" ritual that flags cultural, skill, and tooling frictions, then assigns cross-functional "mini-squads" to tackle the biggest challenge. One early scan revealed manual hand-offs that were slowing loan approvals. Within weeks, a new AI credit-assessment workflow was live, which now processes around 380,000 lending applications annually and cuts manual work by 85%. Similar micro-interventions have seeded over 800 production AI models across 350 use cases, generating an estimated US $563 million in economic value in 2024 alone. Each successful fix is codified into a bank-wide playbook through DBS’s digital academy, ensuring every cycle of experimentation leaves the organization measurably more change-resilient.

Imagine: Challenge Your Team to Start Fresh.

Don’t simply modernize the old operating model; invent a new one. The functions of the future will fundamentally differ from today's work functions. AI-enabled organizations will feature entirely new roles, workflows, and value propositions.

Moderna exemplified this creativity by merging its technology and human resources departments into a single function. This move aimed to redefine work responsibilities by distinguishing between tasks best suited to humans and those ripe for automation. This strategic shift, influenced by Moderna’s partnership with OpenAI, led to the creation of over 3,000 customized AI agents for various business functions, including clinical trials and HR operations, fundamentally modernizing workplace dynamics and the roles of HR and technology.

Act: Embrace Ongoing Cycles of Measurement, Learning, and Re-investment.

Don’t get stuck in the "trough of disillusionment." Every wave of technology brings both inflated expectations and real strategic potential, making it crucial to move early, learn fast, and persevere.

P&G has approached change resilience as an ongoing capability, building momentum across toolsets, skillsets, and mindsets with measurable outcomes. Its custom Generative AI platform, ChatPG, now has over 30,000 employees onboard and supports more than 35 production use cases. In marketing, it has reduced concept-testing cycles from months to days, dramatically cutting costs. In supply-chain operations, pilots combining AI with plant-floor sensors have already enabled fully autonomous shifts at Gillette facilities, part of a broader plan that the CFO estimates could unlock $2 billion in productivity gains. On the skills front, the company rolled out AI reskilling, where an initial cohort of 200 employees earned over 4,400 badges and nearly 90 certifications, applying their learning to dozens of digital initiatives. These metrics allow P&G to directly link upskilling to business impact and optimize its learning investments accordingly.

Culturally, P&G reinforces a growth mindset through its "School of P&G," which blends formal training (10%), mentorship (20%), and on-the-job experience (70%). It further personalizes learning using AI that recommends content and pathways based on individual goals and behavior, an approach that has boosted engagement scores in internal surveys. By integrating bold bets, fast learning loops, and targeted reinvestment, P&G is converting AI experimentation into enterprise-wide performance gains.

Care: Put Human Wellbeing at the Center of Change.

Rapid change can exhaust even the most capable workforce. Without deliberate attention to wellbeing, enthusiasm for new technology quickly morphs into fatigue and resistance. The "care" muscle therefore focuses on creating psychological safety, monitoring sentiment in real-time, and providing people with the resources (time, coaching, and flexibility) they need to stay healthy and engaged while learning new ways of working. When leaders treat wellbeing data with the same rigor as financial metrics, they not only protect their people but also accelerate the adoption of the very innovations that drive competitive advantage.

Cisco demonstrates how addressing employees’ physical, mental, and social needs can accelerate rather than detract from digital reinvention. In 2024, 84% of its workforce logged a combined 2.3 million team check-ins, providing leaders with a real-time understanding of sentiment and workload. Simultaneously, Cisco embedded an AI-powered "WellNest" bot to deliver personalized resources for physical, mental, financial, and social wellbeing. These holistic supports have maintained high engagement while the company scales AI pilots across the business, proving that caring for people is a prerequisite to sustained, resilient transformation.