How Eric Morrison Decodes the Specific Sequences That Lead Users to Adopt New Collaboration Tools

Why Most Collaboration Tools Fail to Stick

Companies spend millions building collaboration platforms, yet many never gain traction. The problem isn't usually the technology itself. It's a fundamental misunderstanding of how people actually decide to incorporate new tools into their daily workflows. Eric Morrison, a User Experience Research Lead at Google with over 14 years of experience studying product adoption at companies like TikTok and Disney, has made it his mission to crack this puzzle. With degrees in History from Yale and the Social Science of the Internet from Oxford, he brings an interdisciplinary lens to a challenge that has stumped countless product teams.

"I've always been fascinated by the underlying mechanics of how people adopt new tools," Morrison explains. "My goal isn't just to observe behavior, but to decode the specific sequences that lead to a successful product launch."

The Hidden Architecture of Adoption

Understanding why users embrace one collaboration tool while abandoning another requires looking beyond surface-level engagement metrics. Morrison's approach centers on identifying repeatable patterns rather than relying on lucky breaks or viral moments.

"Too often, product decisions are based on assumptions or happenstance," he notes. "Our job is to find the repeatable formula. If we understand the fundamental process of why a user finds value, we can engineer that success again and again."

This philosophy stems from his academic background. Historical analysis taught him to dissect complex events into their component parts, searching for cause and effect relationships that others might miss. He applies the same rigor to user behavior, breaking down adoption into discrete phases that can be studied, tested, and optimized.

"History and the social sciences give me a unique approach to UX research," Morrison says. "I believe that even the most complex outcomes—say, for example: building a novel innovation, or driving viral adoption of a product—can be broken down into foundational processes that can be replicated."

The Three Critical Stages of Tool Adoption

Research into collaboration tool adoption reveals three distinct phases that users move through before a new platform becomes part of their regular workflow.

Discovery and Initial Trust

The first hurdle is helping users understand what a tool does and why it matters. Yet, as a tool scales, the psychology of discovery shifts. While early adopters arrive with low expectations and high friction tolerance, subsequent cohorts are primed by word-of-mouth—introducing a complex web of cognitive biases before they ever click "Sign Up."

Among these many psychological shifts, two contrasting dynamics stand out:

• The Gateway Effect: A trusted referral pre-seeds goodwill, lowering initial defense mechanisms and leaving users highly motivated to find value.
• The Verification Tax: This priming simultaneously raises the stakes. These users do not arrive to discover value, but to verify praise.

Consequently, while word-of-mouth secures initial attention, it radically shrinks the margin for error. What an early adopter might have tolerated as a minor quirk, a primed user may perceive as a broken promise. To sustain momentum, the discovery experience must elegantly leverage this inherited trust while delivering an immediate, frictionless payoff.

The First Win

Discovery alone doesn't drive adoption. Users need to experience tangible value quickly. Morrison calls this the first win, that moment when someone accomplishes something meaningful with the new tool that they couldn't do as easily before.

The timing of this win matters enormously. If it takes too long to achieve, users abandon the tool before reaching the payoff. Product teams need to engineer this success moment intentionally, designing onboarding flows and initial experiences that guide users toward their first positive outcome.

Habit Formation

The final stage is where most collaboration tools stumble. Getting someone to try a new platform is very different from getting them to use it consistently. Habit formation requires the tool to become integrated into existing workflows rather than existing as a separate destination.

Morrison's research shows that successful adoption happens when tools reduce friction rather than adding steps. If using a collaboration platform requires people to change established routines dramatically, resistance builds. The most successful products find ways to layer into current behaviors, creating value without demanding wholesale process changes.

The Velocity Paradox

One of the biggest mistakes product teams make is moving too fast with unvetted concepts or moving too slowly while competitors capture the market. Morrison calls this the velocity paradox.

"There is a hidden cost to moving fast without direction," he explains. "But there is also a massive cost to moving too slowly. If you take six months to deliver an insight, the world has already moved on."

The solution is strategic calibration. Teams need to identify which decisions require deep research and which can move forward with lighter validation. Not every question demands a six-month study. Some hypotheses can be tested with rapid prototyping and quick feedback loops.

The key is knowing which type of decision you're making. Foundational choices about core functionality and user mental models benefit from rigorous investigation. Tactical decisions about interface details or feature prioritization can often move faster with iterative testing.

Trust as the Foundation

Particularly relevant for AI-powered collaboration tools, trust plays an outsized role in adoption. Users need confidence that systems will behave predictably and respect their agency.

"Users need to trust the systems they use," Eric Morrison emphasizes. "If they don't understand what AI is doing, it won't be effective. Clear communication and control are essential."

This becomes especially critical as more collaboration platforms incorporate automation and intelligent features. When a tool makes decisions on behalf of users, transparency about how and why those decisions happen becomes non-negotiable. Opacity breeds suspicion, which kills adoption faster than almost any other factor.

Practical Steps for Product Teams

Morrison points to several concrete actions that product teams can take to navigate the adoption journey—not by micro-optimizing their way to marginal gains, but by making bolder, highly educated bets.

• Develop Deep Baseline Intuition: Before taking a big swing, invest the time to truly understand the existing gravity of the user's current workflow. High-conviction product bets fail not from a lack of ambition, but from misjudging the baseline behavior they are trying to disrupt.
• Place Concentrated, High-Conviction Bets: Rather than diluting your focus by trying to perfect every feature path, identify the single most transformative value hypothesis—the "first win"—and commit to it. True traction comes from taking a calculated risk on one killer hook, not from hedging your bets across a dozen mediocre ones.
• Audit Your Hypotheses Sequentially: Do not look at adoption as a flat metric or a series of micro-optimizations. Track it as a sequence of behavioral transitions. This allows you to see exactly where your strategic assumptions meet real-world friction, letting you know when to double down on a bet or when to pivot.
• Remember the Human Risk Factor: Ultimately, adopting a new tool requires the user to take a leap of faith. Technology is only half the battle; the real challenge is building enough organizational trust and value alignment to convince humans to leave their comfort zones behind.