The Growth Project

Strong buy-in from senior executives is critical at the beginning of any transformative initiative. Wallis understood that leaders had to experience AI directly for themselves. In doing so, she was putting into practice what Everett Rogers’ diffusion theory had long shown: new ideas spread faster when they are trialable—safe to experiment with in low-risk conditions—and observable—when peers can see results firsthand. Together, these conditions turn abstract technology into something tangible enough to believe in.

That is exactly what unfolded in the boardroom. Once a few respected voices found the tool useful—asking follow-ups, reading fluent outputs aloud—trialability was satisfied: executives could experiment in a low-stakes, hands-on way. And because these experiments happened in public, observability took hold: colleagues could watch, compare reactions, and see the system working in real time. What could have been a solitary experiment quickly became a shared moment of discovery. Peer-to-peer reinforcement allowed skepticism to fall away and curiosity to spread, because the technology no longer seemed risky or abstract.

But conviction alone is not enough. To matter, it had to be translated into infrastructure that would let Manulife absorb and scale what leaders had seen. That is where absorptive capacity comes in.

A single demo, however persuasive at the individual level, fades unless an organization as a whole can metabolize what it saw into repeatable capability. That is the job of absorptive capacity—a firm’s ability to recognize the value of new information, assimilate it, and apply it to commercial ends—the infrastructure that makes later adaptation possible. Research on absorptive capacity, first developed by professors Wesley Cohen and Daniel Levinthal in the 1990s, highlights two foundations of that infrastructure:

Knowledge is cumulative and path-dependent—it builds fastest on what people already know, meaning prior knowledge is like scaffolding for future learning.

Breadth of knowledge expands absorptive reach—organizations with a wide base of prior knowledge can take in and apply new external ideas more effectively.

Absorptive capacity is about learning—building the knowledge base and routines to embed new tools. Adaptive capacity (discussed in Insight Three) is about changing—reconfiguring those routines when the frontier shifts and old paths no longer fit. Manulife needed both, but it started by deliberately building the absorptive infrastructure needed to allow the organization to learn. In doing so, Wallis’s team treated culture and skills as equal pillars to technology and designed a multi-tier learning stack:

AI 101 for anyone with an interest

advanced prompt-engineering and data-science for power users, and

tailored executive modules delivered with university partners.

They then wove AI into leadership rituals. At Manulife’s Global Leadership Conference, for example, executives showcased employee-built solutions to their peers, creating a common language of use cases and governance. The goal wasn’t just awareness; it was to give every layer of the company—front line to boardroom—enough context to recognize where AI was relevant and embed it in daily work.

In Cohen and Levinthal’s terms, Manulife was steadily increasing its stock of prior knowledge, so each new wave of technology could be absorbed and recombined faster. Wallis’s actions directly aligned with the two conditions they described: training and rituals made learning cumulative by building on what employees already knew, and broad participation across the workforce expanded the base of knowledge available to draw on. In an industry often criticized as “too comfortable,” this gave Manulife a distinctive edge: the ability to build on new tools and embed them into its routines in ways that accumulated advantage over time.

But infrastructure alone is not enough. Once that foundation was in place, the challenge became keeping momentum when the frontier shifted and old paths no longer fit. That required a different capability: adaptive capacity—the engine that keeps the flywheel turning.

When then-CEO Roy Gori warned that the industry had grown “too comfortable,” Wallis knew this complacency was dangerous in a domain where new AI models and applications were appearing at a breakneck pace, driven by massive new capital flows. Absorptive capacity had already given Manulife the infrastructure to learn and embed AI tools across the enterprise. The next challenge was agility: ensuring the company’s response to advancing technology was equally swift and dynamic. Adoption couldn’t be a one-off event; it had to become iterative. That insight set the stage for adaptive capacity—the engine that converts adoption into continuous reinvention.

Research underscores why this engine is critical. Prior adoption experience is the single strongest predictor of inventive capacity: organizations learn to invent by first copying. Yet when firms switch paths—moving to new models or methods —performance often dips before it recovers, as old mental models stop fitting the new approach. Adaptive capacity is therefore the discipline of riding out that trough and recovering faster, turning temporary disruption into cumulative learning. Manulife operationalized this discipline through a set of deliberate routines.

Adoption→ taking in new tools, practices, or patterns developed elsewhere, and embedding them into the organization’s routines.

Selection and Scale → filtering what works, embedding it into routines, and scaling proven solutions across the enterprise.

Invention→ creating original solutions internally, without relying on external patterns.

Manulife built this discipline deliberately. With a strong foundation of AI literacy embedded across the company, leadership worked to smooth adoption pathways by normalizing copying as a precursor to invention. Wallis instituted prompt-a-thons and leadership conferences where employee-built tools were showcased, creating a common language of value and risk. These rituals made it legitimate to borrow, refine, and scale what worked—ensuring adoption wasn’t confined to early enthusiasts but cascaded across the enterprise. In Cohen and Levinthal’s terms, this was about continuously increasing the firm’s stock of prior knowledge so that when a path switch came—whether a new model, platform, or application—the organization could absorb and apply it faster.

Secondly, Wallis deliberately designed for safe path-switching. A vendor-agnostic, cloud-ready stack allowed models to be swapped ‘even daily,’ making technology change a managed routine rather than a disruptive reset. Scaling decisions were tied to clear business outcomes—revenue lift, cost savings, risk reduction, or productivity—so that pivots created value rather than noise.

Finally, it embedded selection capacity—the discipline to prune weak ideas quickly and scale winners. Cross-functional forums and outcome-based funding kept the portfolio focused, so absorptive capacity compounded rather than leaked.

Together, these routines formed Manulife’s innovation flywheel: adoption experience generated invention; selection routines filtered the noise; flexible architecture enabled safe path-switching; and the loop restarted with each cycle stronger than the last.