Over the past three decades, I’ve lived through five major technology waves: the rise of client–server computing, the commercial internet, the birth of the cloud and SaaS, the exploration of VR/AR, and now the era of AI and machine learning. Each wave created winners who embraced change—and laggards who hesitated. From my time at Microsoft helping to envision Bing as a decision engine, to watching Xero seize the SaaS opportunity while QuickBooks stumbled, one lesson has been consistent: leaders who lean into platform shifts gain compounding advantage, while those who “wait and watch” rarely catch up. AI represents the most transformative wave yet, touching every stage of the product lifecycle—from prototyping and research to testing, documentation, code reviews, and incident handling. This isn’t just about tools. It’s about reimagining the role of product managers and the culture of product development itself. This is your embrace and extend moment: embrace AI as the new foundation, and extend your team’s capabilities, workflows, and impact around it. The risk isn’t getting it wrong. The risk is sitting it out.
A firsthand look at how GenAI is transforming the product lifecycle — and why now is the time to rethink your team, your tools, and your playbook.
I’ve been fortunate enough to spend my career building products through multiple waves of technological advancements. Each one felt seismic at the time. Each one created winners and laggards. And each one rewarded the leaders who leaned in early, while punishing those who hesitated.
The first wave I lived through was the era before the commercial internet, when Windows, Office, and client–server applications dominated. Businesses that adopted client–server early — such as SAP and Oracle — rewrote how enterprises managed their operations. Those who clung to green screens and mainframes found themselves displaced.
The second wave was the rise of the commercial internet. Amazon and eBay leaned in, building commerce and communities directly into the fabric of the web. Traditional retailers viewed the internet as a secondary channel, something added to existing operations. Many of those brands are no longer in existence today. The winners were those who recognized that the internet wasn’t just a distribution model — it was a new business model.
The third wave was the commercial cloud. AWS launched S3 and EC2, while Microsoft bet big on Azure. As a result, software no longer needed to be installed, patched, and maintained on physical machines. SaaS became the default way to deliver innovation. I saw this up close at Xero, which has always embraced SaaS accounting. By providing a fully cloud-native experience, Xero offered small businesses a product that was always on, always current, and deeply integrated into the emerging ecosystem of online business tools. QuickBooks, by contrast, was slow to adapt, as it was tethered to its legacy desktop product. That lag gave Xero the space to capture early market share, build a passionate community, and set the pace for innovation in the category. QuickBooks eventually responded with QuickBooks Online, but it took years to catch up. It’s a classic case of what happens when incumbents hesitate in a platform shift.
The fourth wave brought VR and AR. While not every experiment lived up to the hype, companies like Meta and Apple invested early to explore the future of interaction. That wave hasn’t fully broken yet, but the seeds it has planted — in spatial computing, immersive UX, and human-machine interaction — are already shaping adjacent spaces, such as gaming, design, and industrial training.
And now we are in the fifth wave: artificial intelligence and machine learning. Unlike the earlier eras, AI is not transforming a single layer of the stack. It is horizontal and vertical, platform and application, all at once.
What I’ve learned across these waves is simple: leaders who embrace the shift and extend their capabilities around it unlock compounding advantage. Those who wait and watch rarely catch up.
This lesson was clear to me as early as 2008, when I was a senior product leader at Microsoft working on Bing. Our ambition was not just to help people find links — it was to help them make decisions. We wanted Bing to be a “decision engine.” Imagine searching for flights and instantly seeing personalized recommendations based on price, convenience, and your personal preferences. Or planning a night out and getting curated suggestions, not just blue links.
We had the right vision. The problem was that the technology wasn’t ready. Natural language models were brittle. The data infrastructure couldn’t contextualize fast enough. Interfaces weren’t intuitive. We could see the future, but we couldn’t yet build it. Today, we can. The technology has caught up, and in many ways, surpassed what we imagined.
AI is already reshaping the product lifecycle from end to end. Where it once took weeks to turn an idea into a prototype, tools like Figma AI or Galileo now enable this process in hours. Instead of waiting for design resourcing, product managers can create a clickable UI, test assumptions with customers, and iterate instantly.
Customer research, once the most time-consuming part of the job, is being compressed by platforms like Gong AI and Viable, which parse thousands of customer interactions to surface themes and insights.
Documentation — always the bottleneck — is now continuous, with Mintlify or Notion AI generating onboarding flows, API documentation, and change logs.
QA is becoming proactive rather than reactive, with CodiumAI generating tests as code is written. Code reviews are accelerated by Copilot and CodeRabbit, allowing issues to be caught early. Even incident handling is evolving, with tools like Rootly summarizing outages and drafting postmortems in minutes.
Taken together, these shifts represent more than productivity gains. They create a new cultural expectation: that velocity is the baseline. In high-performing teams, meetings now start with working demos, not whiteboards. Product marketers spin up landing pages before the PRD is written. PMs generate prototypes and test copy themselves. The culture of “talk first, build later” is collapsing into “show me what it looks like now.”
For product managers, this is an identity shift. The rote parts of the job — manual synthesis, endless writeups, and heavy documentation — are being offloaded to AI. What remains is the strategic core: vision, judgment, prioritization, and storytelling. AI doesn’t replace the PM. It sharpens them.
But this only happens if leadership leans in. With every prior wave — client–server, internet, cloud — the most significant barrier wasn’t technology. It was a mindset. Companies that treated SaaS as a toy lost to those who rebuilt their business models around it. At Xero, embracing SaaS early enabled global expansion and set a new standard for how accounting software should operate. At Intuit, hesitation cost them years of innovation runway. The same pattern is now unfolding with AI.
That’s why I call this your embrace and extend moment. Back at Microsoft, the phrase was coined by Bill Gates and meant adopting emerging technology and pushing it further to create an advantage. Today, it means embracing AI as a foundational shift and extending your team’s capabilities, workflows, and customer impact around it.
This isn’t an optional upgrade. It’s the fifth wave. And unlike VR or AR, this one isn’t speculative. It’s here. It’s real. And it’s rewriting how products are built, marketed, and delivered.
As Product and Business leaders in this moment, the risk isn’t getting it wrong.
The risk is sitting it out.
