For years, product teams wasted time on work that shouldn't exist. Manually building in-app campaigns and hoping they stick. Explaining the same feature to twelve people. Enforcing guidelines from docs and Slack threads.
In 2025, something broke. AI didn't just make this work easier—it made it optional. The workflows that used to eat your week became automated, eliminated, or so fast they stopped feeling like work. But not every team moved on. Some teams reclaimed 20+ hours per month. Others are still doing things the old way, not realizing there's a new one.
Here are the ten workflows that became unnecessary, and what it looks like when teams finally let them go.
What Changed in 2025
The shift wasn't subtle, but it wasn't universal either. Some product adoption platforms started offering AI assistants and automation features—though with varying levels of maturity. The promise: instead of giving you better dashboards to analyze problems, AI could start solving problems for you. Instead of making manual work easier, it could eliminate chunks of it entirely.
The question became: which routines could you actually retire, and which were you still stuck with. Some teams moved on immediately. Others are still working through them. Here are the 10 routines worth checking off your list.
#1: Building from scratch, then shipping into the void
You know the cycle. New feature launches next Tuesday. Better build a tour. Start with a template. Segment the right users. Write the copy. Hit publish and pray someone notices. 10 hours of work. 8% adoption. Rinse and repeat next month.
The worst part? You have no idea why it really works or fails. Was it the targeting? The messaging? The timing? You're guessing based on what worked last time—or what you think worked.
How teams moved on: AI trained on 10+ years of product adoption data that actually knows what works. Copilot strategizes with you and builds your entire campaign in one go—it knows what messaging resonates for trial users vs. power users. It suggests proven flows that drive engagement. It analyzes performance and tells you what to improve.
Instead of shooting into the void and hoping, you're building with intelligence from thousands of successful launches.
The difference? Feature launches that take hours instead of days—and actually reach the users who need them because you built them right the first time. Teams that made the switch don't waste 10 hours building campaigns that disappear. They launch with confidence backed by benchmark data. Others are still crossing their fingers and repeating failed patterns.
👇 See a Demo of Copilot
#2: Styling like it's 2015
Two hours. That's how long it took to manually copy hex codes, type out font names, and write custom CSS just to match your brand guidelines. Then someone opens it in Safari and half of it breaks.
How teams moved on: AI that extracts themes directly from your product UI. Point at your interface, let AI capture the styles automatically, sync it with templates, and never write another color code by hand. When you do need custom CSS, AI writes it for you.
The difference? Product teams stopped being part-time designers and went back to building product. Some teams automated styling entirely. Others are still copying hex codes.
👇 See a Demo of styling in Chameleon
#3: Playing detective in your own account
"Who built this tour?" "Which experiences use the new theme?" "What's our segmentation logic for trial users?" Every question meant clicking through dozens of tabs, checking revision history, Slacking teammates, maintaining spreadsheets of what's where, hoping someone remembered to document it.
You were wasting hours investigating your own work.
How teams moved on: Conversational AI that knows your entire account. Ask Copilot in plain English: "Show me all tours using the onboarding theme" or "Who’s a publisher on our account?" Instant answers about setup, audiences, patterns, and performance data.
The difference? The information you needed was always there. Now teams can actually access it in seconds and some eliminated detective work entirely. Others are still digging through tabs or chats.
👇 See a Demo of Copilot
#4: Begging engineering for audiences
"Can we target Enterprise users who haven't used Feature X in 30 days?" Your engineering team groans audibly. Three-day turnaround, minimum. You figure out the datasets, manually maintain segment lists, submit tickets, and wait. Every targeting decision became a negotiation.
How teams moved on: AI that builds audiences from natural language. Tell Copilot who you want to target in plain English, and it creates the segment. Even better, your synced lists from integrations (like 'Webinar Attendees' and 'Referral Users') automatically generate relevant segments based on actual user data. No engineering queue required.
The difference? Segmentation went from a bottleneck to a five-second task. Teams that moved on deliver better in-app engagement. Others are still waiting on Engineering tickets.
👇 See a Demo of Segmentation in Chameleon
#5: Manually auditing what should be automatic
You set standards. "Use consistent audience names." "Set unpublish dates on feature tours." "Don't spam trial users." And still spend 30 minutes every week cleaning up. Scanning tours for naming inconsistencies. Archiving old announcements. Playing human QA bot for your own platform.
How teams moved on: Weekly AI audits that scan your account automatically. Ranger checks naming patterns, finds stale tours, flags violations, and helps you fix them in one click. The 30 minutes of manual checking? Done in seconds.
The difference? Manual cleanup hours turned into automated checks. Teams using Ranger keep their account organized without auditing flows and audiences; they just review what needs fixing and approve. Others still have “Weekly Cleanup Time” blocked into their calendars.
👇 See a Demo of Ranger
#6: Survey responses as Friday homework
47 NPS responses. 63 opinions on the homepage redesign. You export, clean, paste into ChatGPT, fact-check the hallucinations, build a spreadsheet. Hours later, you know what they wanted. Past tense.
How teams moved on: AI that analyzes all your feedback and surfaces themes automatically. Copilot reads every response, groups similar feedback, identifies what actually matters, and tells you exactly what users care about. No spreadsheets. No manual categorization.
The difference? Day-to-day became building product instead of analyzing feedback. Some teams process it in minutes now. Others are still spending afternoons in spreadsheets.
👇 See a Demo of Copilot
#7: Guessing what drives adoption
You built twelve onboarding tours last quarter. Which ones drove adoption? Which ones users ignored? Which messaging resonated? You're guessing. You cobble together metrics from three different tools, export to spreadsheets, and present "directionally accurate" data.
How teams moved on: A leaderboard that ranks what gets completed. Conversion goal tracking from any CTA. G2 integration showing external validation. Copilot analyzing performance and telling you exactly what to fix. All in one place, all connected.
The difference? Teams stopped guessing and started knowing. Some teams make data-driven decisions in minutes. Others are still cobbling together metrics from three tools.
👇 See a Demo of tracking performance in Chameleon
#8: Governance that lives in Google Docs
"Why are users getting five tours on their first day?" "What domains are we live on?" "Can tooltips appear on mobile?" No one knows. The answers exist somewhere in a Google Doc no one reads, Slack threads no one saved, or tribal knowledge in someone's head.
Pure chaos disguised as process.
How teams moved on: A governance page with actual controls. Deep visibility into who can do what, rate limiting to prevent user bombardment, standards that Ranger helps enforce automatically. Documentation became living, enforced system configuration instead of static files no one reads.
The difference? Governance stopped being a document and became scaled confidence. Teams that use scaled controls have enforceable standards. Others are still searching through Google Docs.
👇 See a Demo of Governance in Chameleon
#9: Explaining features like it's Groundhog Day
New feature launches. Sales needs you to explain it. Marketing wants a walkthrough. Support needs to understand it. A customer asks how it works. You've explained this exact feature twelve times this week to twelve different people.
You're a PM, not a human tape recorder.
How teams moved on: Record your screen once while explaining the feature to AI. It creates an Interactive Demo, generates all the materials (copy, graphics, campaign assets), and makes it launchable in-app or out. One explanation, infinite uses across sales, marketing, support, and your actual product.
The difference? You explain it once. AI handles the rest. Teams that moved on from this are creating entire launch campaigns in minutes. Others are still repeat-recording product walkthroughs and internal enablement videos.
👇 See a Demo of Chameleon's Interactive Demos
#10: Demos that live in a silo
You built a beautiful interactive demo. Sales uses it. Marketing shares it. But you have no idea which messaging actually resonates—you're just guessing. And even if you knew, demos live on landing pages, completely disconnected from the users in your product who actually need to see them.
How teams moved on: A/B testing for Demo variations. Launch winning demos directly to users inside your product. Analytics on what messaging drives engagement. And soon, conversion tracking showing which demos lead to actual feature adoption.
The difference? Demos stopped being marketing collateral and became part of the product experience. Teams that integrate their Demos with the product experience test messaging and launch in-product. Others are still guessing what turns viewers into power users.
👇 See a Demo of A/B Testing Interactive Demos
What actually changed
These ten workflows became purely optional. Hours of manual work turned to minutes of AI-powered automation. Guesswork became data-driven decisions. That's what teams reclaimed when they stopped doing work that became unnecessary.
In 2025, some teams moved on from all of them. Others moved on from half. And plenty of teams are still doing things the old way, not realizing there's a new one.
Which workflows are you still doing the old way?
Run a quick check: see which of these ten workflows you're still doing manually, and how much time you could reclaim by moving on.
You'll see exactly where you stand, and what's possible if you decide to let the old way go.
👉 Check which workflows you're ready to retire.
🤝 Already moved on? See what else is possible with Chameleon.
Book a demo to discover AI-first user onboarding
So you can get back to orchestrating product adoption
