Every product team is shipping AI. But for many companies, very few users are adopting it. The people who try the feature love it, and adoption stalls anyway.
Charanya Kannan thinks that’s because teams obsess over building AI and barely think about adoption. As VP and GM of Navan Anywhere, she puts Navan’s AI travel and expense tools inside Slack, Teams, and Gemini, where people already work, instead of asking them to open yet another app. She joined Navan when it was still called TripActions and doing under $50M in revenue. Now it’s well on its way to $1B!
Her core belief: if customers have to work harder to use your AI, it isn’t actually better.
In this episode, Charanya shares:
How Navan Anywhere was built distribution-first, bringing booking and expenses into the tools people already use every day
Why Navan’s margins went up in the AI era, while the rest of SaaS braces for compression
And why she believes PMs who mostly manage process and Jira tickets will fade away, while those who can actually drive user benefit and business outcomes will matter more than ever
1. Building is easy. Getting users to adopt is the real problem.
These days, you can create an AI prototype in about a weekend. But getting anyone to actually use it? That’s where most teams get stuck.
Charanya’s fix is to skip the “please come try our shiny new thing” step entirely. Navan Anywhere lives inside Slack, Teams, and Gemini, where people already spend their day. They don’t have to download a new app or form a new habit because, as she says:
“If they have to put in extra effort to do something because AI is superior, then AI is not really superior.”
Product takeaway: Plan for adoption as you plan the feature. The easiest product to adopt is the one that shows up where your users already are.
2. Not everything needs to be a chatbot
Six months ago, Charanya told her designers to start brushing up on evals, because chat was going to eat everything. Then users actually tried picking a hotel from five paragraphs of text. It turned out that people booking hotels wanted to see photos of the hotel.
So, Navan mixes and matches. Chat handles the “here’s what I want” part and the UI handles the “let’s compare” part, while payments stay firmly on the reliable deterministic rails.
“The real judgment or discernment is: Where do you bring in AI? Where does it make sense to bring in your agentic systems versus where do you fall back to your deterministic systems?”
Product takeaway: “AI everywhere” isn’t a strategy. Use AI where it removes friction, use UI where people need to see things, and be skeptical about AI related to anything that moves money.
3. The two kinds of PMs & why nobody’s irreplaceable because they write great Jira tickets
Charanya has mentored dozens of PMs, and she sees two types: Those who spend their days running standups and shuttling messages between sales and engineering, and those who ask why the team is building something at all, and whether it’ll still matter in five years.
Only one of those jobs survives the AI era.
Navan runs a travel business doing over $3B in bookings with fewer than five PMs, and engineers write a lot of their own tickets.
“Everybody knows there’s a problem, but how do you frame that problem? Give the problem eyes and ears and a figure that people can go after and solve it… If you’re just thinking about processes 80% of your time, that’s not what we want.”
Product takeaway: Process should be about 20% of the job. If it’s 80%, AI is coming for that part first.
4. How Navan’s margins went up in the AI era
While most of SaaS is sweating over inference bills, Navan’s margins are growing. The trick is letting AI do what it’s good at: it now resolves about half of support conversations, up from a third back in the pre-LLM days.
The truly messy stuff still goes to human travel counselors.
“When you’re stuck in a plane for five hours on the tarmac… no AI can come and solve the problem for you.”
Product takeaway: Let AI handle the repetitive stuff, save your humans for the hard stuff, and treat your model choices like the budget decisions they are.
Chapters
00:00 Introduction
04:17 Why AI features don't always get adopted
06:30 Building Navan Anywhere distribution-first
10:13 When to use AI vs. deterministic systems
12:01 Conversational cognitive load and why good UI still matters
15:57 Evals, distilled models, and the AI reliability pyramid
20:14 Product owners vs. product managers in the AI era
26:10 How Navan is growing margins while SaaS faces AI compression
31:40 Conclusion
Links
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