There’s near universal consensus that AI will fundamentally change how business is done, yet most organizations have not yet seen a substantive impact from their AI efforts. A BCG Global Survey of 1,000 CXOs in more than 20 sectors reports that just 26% of organizations have achieved value from AI, realizing an average cost savings of 45% and 60% higher revenue growth compared to peer firms.

Why such disappointing results? The survey found that among the many challenges organizations face in implementing AI initiatives, 70% are related to people and processes. While it’s true that organizations face additional technical barriers such as poor data quality, integration complexity, or infrastructure costs, our collective experience working across hundreds of companies comports with the study’s finding: The primary obstacle is the ability of companies to adapt, reinvent, and scale new ways of working.  We call this change resilience.

Why Change Resilience Is Scarce

In the past, organizational transformation was episodic. You modernized your systems, trained your people, and operated in a stable environment until the next wave of disruption hit. But now AI is advancing at a pace that far exceeds most organizations’ ability to adapt, and the change is unrelenting.

Business leaders find it harder to anchor AI transformation in a traditional roadmap or use conventional means to drive change-management initiatives. Five-year strategies no longer hold. Annual planning cycles can’t keep up. Traditional financial, risk, and legal controls lag further and further behind the onset of new risk types. Static operating models become liabilities. Even the newer ways of working, like agile methodologies, broadly adopted during the rise of the software era, aren’t sufficient. To compete in this volatile environment, leaders need to embrace continual change, otherwise they face irrelevance from inaction or burnout from chasing shiny objects.

Change resilience is the capability that will equip organizations to seize the opportunities and preempt the threats presented by fast-evolving technology. It’s an enterprise‑wide reflex that converts continual disruption into repeatable learning loops that create value. It uses three muscles:

• Sensing, or the ability to pick up weak technological, competitive, or societal signals early
• Rewiring, or the capacity to redeploy talent, data, capital, and decision rights in days or weeks, not fiscal quarters
• Lock‑in, or the discipline to codify what a team learns (in process, code, or policy) so the next initiative starts from a higher baseline instead of reinventing the wheel

Together, these muscles can keep an organization’s metabolism in step with AI’s rapid advances.

Shopify offers a compelling example of change resilience in action. Rather than layering AI on top of existing operations, the company continuously rewires itself to stay ahead of what’s next. In 2023, Shopify made the bold decision to spin off its entire logistics arm, one it had spent years building, to refocus on product innovation. This reset enabled Shopify to rapidly launch AI-native features like Sidekick, an embedded assistant for entrepreneurs that helps 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 more than 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 continuously reshaping itself to thrive in the AI era.

To understand how change resilient your organization is, ask yourself:

• Can employees be redeployed to fast-moving, high-priority initiatives to respond to changes in technological capabilities without the need to overhaul budgets or org charts?
• If a team member had an idea today, do they have the motivation, access, tools, and support to start experimenting?
• When an experiment shows 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 the next attempt?

If you can’t answer “yes” to most of these questions, then your organization does not yet have the  change resilience required to turn your AI strategy into durable performance gains.