For years, the debate in too many companies around workplace flexibility has focused on policy statements: The number of days a week CEOs want their people in the office. But companies like Airbnb, Atlassian, and Zillow that have figured out flexibility moved past policies and invested in transformation. They built change management capabilities that are now giving them an advantage in AI adoption.
The connection isn’t obvious at first. But organizations that successfully transformed their workplaces over the past five years already have the playbook for AI transformation. They learned to communicate transparently about change, focus on outcomes instead of activities, and build cross-functional orchestration—particularly across HR and IT. Those same capabilities will determine who wins at AI.
Why flexible work and AI transformation are really the same challenge
Recent research shows that people-centric organizations are seven times more likely to be mature in their adoption of AI, driving consistent return on their AI investments. That’s not a coincidence. Both flexible work and AI adoption require the same fundamental shift: trusting people to deliver results while giving them new tools to work differently.
Brandon Sammut, chief people and AI transformation officer at Zapier, framed it this way in a recent discussion: AI adoption raises the floor by improving productivity in existing roles. AI transformation raises the ceiling by fundamentally rethinking how work gets done. “We’re insisting that we scope work to produce meaningful, measurable benefits in three areas—efficiency, quality, and employee experience,” he says. “It’s not transformational if we just save time or money; we also need to boost quality and make work better for the people involved.”
That directly mirrors what successful flexible work programs have achieved. Companies like Dropbox didn’t just reduce office costs. They reinvested savings from efficiency gains into team gatherings and tools that improved the quality of employee interactions, helping engagement, retention and outcomes. They’re now applying similar methodologies to AI transformation.
But AI transformation faces a challenge that flexible work did not: employee fear. While 78% of workers would prefer to work one or more days a week from home, 71% of Americans are concerned that AI will be “putting too many people out of work permanently.”
When companies announced work-from-home policies, people worried about isolation or career advancement. But when you announce AI initiatives, people worry about their jobs disappearing. That makes the change management work exponentially harder—and exponentially more important to get right.
The questions that create transparent AI communication
Most AI announcements fail for the same reason return-to-office mandates fail: Leaders announce the change without explaining the rationale, making outcomes clear, or addressing people’s real concerns.
The good news? Your flexible work communication playbook already has the structure. From my own experience leading transformation efforts at firms like Google and Slack, four questions must be answered clearly and repeatedly:
What’s changing and why? Don’t lead with “AI is here” or “we’re implementing Copilot.” Start with the problems you’re solving. “Our account managers spend four to six hours creating quarterly customer presentations. We’re going to reduce that to 30 minutes so they can spend more time with customers.” This addresses job security fears: You’re eliminating toil, not eliminating jobs.
What’s staying the same? People also need to hear what’s not changing—especially now. Start with an explicit focus on outcomes and results. When employees see AI productivity gains, their first question is often “am I making myself obsolete?” Answer that fear directly by pointing to opportunities, such as business growth, if possible.
What do I need from you? Be clear about expectations. Tell people they need to experiment. Remind them to have patience when things break, and be willing to iterate. Frame this as skill-building, not threat assessment.
How will we measure success? This is where most companies stumble. They announce efficiency targets without considering quality or experience. People perform to the metric (generate more content, answer tickets quickly) without considering whether the work actually matters.
Build measurement systems that matter
Just as companies with successful flexible work programs measure outcomes rather than monitor activity, those making real headway with AI tend to do the same. This shift requires disciplined attention to three questions:
Are we creating conditions for success? Track training hours, but also experimentation time. Recent research shows that firms that are in the upper 5% of AI maturity are six times more likely to invest time in and have structured programs for AI learning.
What’s actually changing? Track which tasks are being automated, how much time is being saved, and what improvements are expected. But be specific: “We reduced expense processing time from 20 minutes to two minutes per report, eliminating 15 hours per week of work.”
Did this improve business results? Look at outcomes like customer growth, high-performing employee retention rates, and revenue per employee. Measure the metrics that actually matter to your business, not AI adoption rates.
Here’s the uncomfortable truth that flexible work taught us: Productivity usually dips before it improves. Early remote work felt chaotic. Meetings multiplied. Communication got harder. The same applies to AI. People spend time learning tools, documenting processes, and debugging failures before they see gains.
What AI transformation leaders really need
Just like companies added “head of remote” or “chief future of work officer” titles to their boardrooms several years ago, roles like “head of AI transformation” are appearing everywhere today. Some 33% of boards have appointed a chief AI officer, and another 43% say they think they should do so.
Establishing the right leader is important, but what really matters is creating a truly cross-functional approach.
The best AI transformation leaders combine three capabilities: deep understanding of how your organization actually works, product management thinking, and change management expertise. They need enough technical fluency to understand what AI can and can’t do, but their primary job is orchestrating people and processes, not building algorithms.
Look for leaders who’ve driven successful transformations before—the person who led your shift to agile, the operations leader who eliminated workflow bottlenecks, or yes, the person who worked across functions to drive the change to flexible work.
“The LLMs you choose or the specific AI tools you choose—those aren’t unimportant decisions,” Sammut explains. “But the difference maker is going to be the same as it was with other transformations: leadership, talent, and culture.”