When you think about the future of work, it’s impossible to ignore the seismic shift that AI is bringing to every industry, and perhaps nowhere more deeply than in software engineering.
However, becoming truly AI-forward is more than sprinkling AI into your product roadmap or buying a license for the latest tool. It starts with a cultural mindset shift and it has to come from the top.
In this episode of SaaS That App: Building B2B Web Applications, Karynn Ikeda, Engineering Manager at Babylist, sat down with Aaron Marchbanks and Justin Edwards to share what she’s learned helping teams adopt AI thoughtfully and what companies get wrong when they don’t.
Mindset Before Models
The first big takeaway from Karynn’s story is this: an AI-forward company is not just a company that plugs in AI. It’s a company that empowers its people to experiment, learn, and grow alongside it.
At Babylist, this mindset shift came straight from the top. Karynn shared how her SVP of Engineering challenged the entire engineering organization to become the top 1% of AI users when it comes to productivity, creativity, and impact. That’s not a KPI you can measure overnight, but it sends a powerful signal: using AI is not cheating; it’s the expectation.
Karynn also pointed to Zapier as an early example. When OpenAI’s first models hit the headlines, Zapier’s leadership called a “code red” and rallied the entire company to figure out how they could use these tools. They didn’t limit AI to their product roadmap. Instead, they dogfooded it across the organization, creating space for every team to discover how AI could improve their workflows.
As Karynn put it, “You can’t get to that level of maturity if you treat AI like a pet project for just your product org.”
How to Overcome Fear and Resistance
Of course, new tools only matter if your people are willing to use them. And that’s where things get human and sometimes uncomfortable. One of the biggest obstacles Karynn sees is that many engineers are at the peak of their careers. They know how to ship code. They’re great at what they do. Suddenly, they’re asked to go back to square one, adopt a beginner’s mindset, and learn a tool that will fundamentally change how they work.
It’s not just technical barriers. It’s psychological. Some worry about job security. Others feel overwhelmed by the firehose of tools. Some are genuinely concerned about the ethical and environmental impact of large models. For leaders, the key is understanding the source of that skepticism.
Meeting people where they are and showing them the “carrot” is far more effective than fear. The best incentive? Freeing up tedious work so engineers can focus on the parts they love.
Experimentation Over Perfection
One trap that organizations fall into is overthinking their AI tool stack. It’s tempting to run a long procurement process to find the perfect fit. However, in AI, today’s perfect tool may be outdated in two months.
Karynn’s approach? Embrace the mess. Let teams experiment, test different tools, and share what works. The companies that move fastest are those that open the floodgates for employees to try AI tools that meet basic security and data privacy requirements.
Is it chaotic? Absolutely. But companies that experiment broadly build collective wisdom faster than those who try to get it right on the first try. The key is encouraging a culture of sharing and learning. Spotlight your power users. Pair them with teammates who are struggling. Hold demos, hackathons, or casual “show and tells” so everyone learns together.
What Happens to Engineers?
That’s the million-dollar question.
Karynn doesn’t buy the hype that software engineers will disappear. What she does see is a shift: “AI is transforming not just products, but people and processes. The role of a software engineer will look dramatically different in five years.”
She described how some engineers at Babylist are already managing fleets of AI agents that write small chunks of code. Their job has shifted from writing code line by line to reviewing, testing, and debugging what AI produces.
Some engineers thrive in this world, especially those who see code as a tool to deliver great user experiences. Others may resist, preferring to stay hands-on. That’s okay, too.
Final Thoughts
Karynn’s insights remind us that building an AI-forward company isn’t about the shiniest tools; it’s about your people. It’s about giving them the freedom to experiment. It’s about leadership that sets the tone, and it’s about helping your teams feel excited, not threatened, by what AI can do next.
In the next five years, AI will reshape how we build, ship, and scale software. The companies that thrive won’t be the ones who treat AI like a quick fix. They’ll be the ones who put their people first and create a culture where learning, curiosity, and experimentation lead the way.
Karynn’s Background
Karynn Ikeda is an AI strategist and engineering leader at Babylist, where she drives AI adoption initiatives for the trusted platform serving millions of growing families. With over a decade of experience spanning software engineering, strategic operations, and digital storytelling, she has been instrumental in shaping how organizations embrace technological transformation. As an expert in human-centered technology implementation, Karynn focuses on amplifying the human experience through AI adoption while addressing practical challenges faced by engineering teams.
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