When Shopify CEO Tobi Lütke told employees that “everyone means everyone” in the company’s AI rollout, he wasn’t being provocative for the sake of it. He was setting a new standard for how technology adoption should work–bold, all-in, and deeply human.
In a company-wide memo, Lütke laid out a mandate that repositions AI from a department-specific, standalone tool to shared infrastructure. Every team, from product to HR, is expected to adapt.
It’s a move that mirrors a broader shift taking hold across high-performing organizations: AI strategies that succeed are ones built around people. They’re clear in purpose, intentional in execution, and grounded in trust. That perspective echoes Sandy Carter’s book AI First, Humans Always, which frames successful adoption as a balance between capability and care.
While many enterprises are still experimenting with pilots and cautiously weighing ROI, Shopify is already operationalizing. Their momentum offers a valuable signal to leaders navigating the early stages of transformation.
The Three Pillars of Shopify’s AI Strategy
1. Cross-Functional AI Implementation
Lütke’s AI strategy doesn’t silo innovation in technical teams. It reaches every corner of the company, from executives to front-line employees.
“Everyone means everyone.”
To support this, Shopify is equipping teams with role-specific tools. They’re facilitating shared learning across functions by positioning AI as something every employee must learn to navigate (not a side project owned by IT).
2. Embodying Human Intuition, Creativity, and Empathy
This memo drew mixed reactions. Some welcomed its clarity. Others bristled at its bluntness, interpreting the mandate as an outright threat to human roles. But there’s a more nuanced point beneath the dry urgency: AI should elevate and extend human potential, not replace it.
Lütke describes AI as a “multiplier” and “thought partner” that enables employees to take on challenges that were once out of reach. His strategy leans into creative leverage.
Empathy, intuition, and entrepreneurial thinking still matter. Combined with the right tools, they create new levels of impact.
3. Building Trust Through Training and Change Management
Shopify pairs urgency with structure. Performance expectations now include AI use, but Lütke doesn’t assume adoption will happen on its own. Teams get access to hands-on resources, peer learning, and real-time support.
Lütke reinforces this shift by modeling it himself and holding leaders accountable. That consistency builds momentum while making space for employees to learn and adapt.
Why Other Enterprises Are Lagging Behind
Most enterprises aren’t ignoring AI. According to McKinsey’s 2025 AI in the Workplace report, nearly all are investing, but only 1% believe they’ve reached maturity.
The biggest barrier, researchers found, isn’t employee readiness. It’s leadership inertia.
What’s slowing leaders down? A mix of mindset, structure, and scale.
Deep-Rooted Operational Lag
Legacy systems are the most visible drag on transformation. Many enterprise architectures weren’t built to evolve quickly. Outdated infrastructure, complex tech stacks, and deeply entrenched processes make it harder to pivot at the speed of AI.
Shopify had a head start. It was digital from day one.
Heritage systems are a headwind, but they aren’t immovable. Digital transformation doesn’t require a clean slate, just a clear mandate and consistent execution, even if the path forward is incremental.
Cultural and Talent Gaps
High-performing AI adoption depends on curiosity, technical fluency, and a willingness to experiment. These traits are cultivated through hiring, incentives, and modeling from leadership.
Building an AI-ready culture means consistently investing in both mindset and infrastructure for learning. Progress will be a lot slower in companies where risk is punished or siloed teams limit knowledge-sharing, realities which are, unfortunately, prevalent in large enterprises.
Leadership Hesitation
Lütke comes from a tech-forward background, so adopting AI tools likely felt natural. But tech fluency at the top isn’t a prerequisite for action. What is required is a bias for learning, a tolerance for calculated risk, and the ability to lead through ambiguity.
Many public companies struggle here. Shareholder expectations and earnings pressure make it harder to justify bold moves with uncertain near-term returns. Add in lingering skepticism from past digital transformation efforts, unclear ROI forecasts, and the reality of competing strategic initiatives, and it’s easy to see why momentum fades.
Complexity at Scale
Transforming how 300,000 people work is an entirely different challenge from shifting a team of 1,000 or even 10,000. The larger the org, the harder it is to execute with consistency and speed.
Global enterprises often have thousands of systems, vendors, and rules all in play. Making AI work in that kind of environment takes structured governance, clear ownership, and scalable systems for training and change.
For companies watching Shopify and others move decisively, the way forward won’t come from copying tools or tactics. It’ll come from rethinking how transformation happens and how fast.
The Role of Chief Data Officers in AI Transformation
In most organizations, the CDO was originally hired to bring order to the chaos: clean up inconsistent datasets, establish governance, and help extract more value from business intelligence tools.
But with the rise of generative AI, the role is evolving fast.
Today’s CDOs are shaping enterprise strategies. They still safeguard data, but now they’re helping organizations figure out where AI fits and how to make it work.
As more companies aim to operationalize AI, CDOs are the ones asking the hard questions. Is our data structured enough? Can our systems scale? Are we investing in solutions that solve real business problems?
They also influence how teams learn and adapt. Great CDOs design programs that teach tools and new ways of thinking. They help teams build fluency, develop judgment, and stay grounded in outcomes.
This shift goes beyond technical expertise. CDOs need credibility in the boardroom, the ability to align stakeholders, and the instinct to navigate complex change. That includes setting the guardrails (governance, ethics, and accountability) before momentum outpaces control.
At companies like Shopify, this mindset is already baked into leadership. Elsewhere, the CDO is the one lighting the path. They remove roadblocks. They build alignment. They strike the match.
AI transformation shouldn’t start with tech. It should start with leadership. And increasingly, that leadership starts with data.
Leading the AI Transformation
The gap between companies experimenting with AI and those operationalizing it at scale is widening. Shopify moved fast with clear direction and full alignment. Others are still stuck in pilot mode, debating next steps. That hesitation comes at a cost. In a moment where AI maturity could define long-term market leadership, is indecision a risk you can afford?
Access to better tools won’t set you apart. The ability to connect technology to purpose, strategy, culture, and execution will. That kind of transformation doesn’t happen by accident. It takes leadership that’s willing to push past comfort and rethink how work happens to build something better.
CDOs are built for this moment. With the right mandate and executive support, they translate ambition into action, vision into execution, and AI buzzwords into actual business value.
The window for slow, cautious exploration is closing. This is the time to build momentum, sharpen your strategy, and design for impact and scale.
As Tobi Lütke puts it, “Stagnation is slow-motion failure.”
Book a data strategy session with Factor to assess your organization’s data readiness and identify your next best moves for AI transformation.
