How AI Transforms the Onboarding Process

Forget 90-Day Plans. I Used AI to Build a Strategic POV for my new role in 1 Day Last week I took on marketing leadership for both Wix and Wixel ( hello: dream job alert 🧚♀️ ) but in addition to the dream job I inherited a whole lot of complexity. Different teams, different products, with different levels of maturity. Complex organizational processes and reporting lines. 300+ product teams to work with, 400 something marketers to collaborate with. 😅 When you step into a role like that there is no time to “ramp up.” And honestly, there shouldn't be. So I skipped the traditional onboarding playbook and used AI to accelerate what actually matters: ➡️ Understanding the business ➡️ Spotting misalignment ➡️ Stress-testing direction ➡️ Building trust with a clear, informed POV Here’s how I did it: 🧠 1. I Used AI to Pressure-Test My Thinking, Not Just Write Docs AI wasn’t just a productivity tool : it was a strategic stress tester. I’d prompt: → “What’s missing from this product vision?” → “What would a skeptical PM, exec, or user push back on?” → “What assumptions are baked into this plan that we’re not surfacing?” We used this approach to refine product direction, sharpen SEO reporting, and align internally on what matters before locking in messaging. 📐 2. I Synthesized the Org with Prompts: Not Just Syncs I fed GPT a messy mix of Notion pages, docs, strategy decks, updates, and transcripts of about 20 meetings. Then I asked: → “Summarize this across product, marketing, and ops.” → “Where are the gaps or conflicting assumptions?” → “What questions should I ask in my first team meetings?” It gave me a full-field view in hours and helped me meet teams where they actually were. 🤝 3. I Co-Created with My Team in a faster and smarter way When we needed to clarify positioning, rework reporting, or align on narrative, I didn’t show up with answers. I showed up with prompts: → “Here’s the structure — what’s landing? What’s not?” → “Let’s run this messaging through a skeptical user’s lens.” → “If this report hit an exec’s inbox, what would feel fuzzy?” ✨ AI didn’t do my job for me. It helped me do the real job much much faster. Understanding nuance, spotting gaps, framing the right conversations. That’s the part no onboarding doc gives you (if you even get one). And the part most leaders spend months chasing. If you’re taking on a complex role, building a new team, or trying to move fast without cutting corners — this is how I’d do it again. #MarketingLeadership #AIThinkingPartner #Wix #Wixel #ExecutiveThinking #PromptEngineering #OnboardingWithAI #AIMarketing #Strategy

Signed up for 100+ SaaS products in the last 6 months. These are the 8 best examples of AI onboarding I’ve seen this year. Not hype, real AI used to onboard users in seconds. Took a few hours to put the onboarding flows on a Figma board, with some notes covering exactly how these companies use AI to get users to value faster. Here’s how they are using AI to cut time-to-value down to seconds 👇 1. Relay.app (Context > Content) Instead of asking 20 questions, Relay asks for your LinkedIn URL. The AI scans your profile and auto-configures your agents and workspace instantly. 2. Gamma (Execution > Guidance) Gamma doesn't teach you how to use the editor. It asks for a topic and generates a full slide deck for you in seconds. No more relying on "empty states." 3. Figma (Just-in-Time Education) Figma analyzes your behavior in the canvas. If you get stuck or pause too long, the AI suggests the specific plugin or feature you need right in that moment. 4. Zapier (Outcome > Templates) Templates have taken a back seat. Now, a Copilot ingests your desired outcome and builds the workflow for you. It uses your initial app selections to predict exactly which prompts you need first. 5. Notion (Conversational Setup) They replaced the static "welcome wizard" with an active AI chat. It uses natural language to configure your workspace behind the scenes. 6. Miro (Zero-Click Canvas) The first screen is a chatbot asking, "What are we working on?". It builds the board structure for you before you even learn the UI. 7. n8n (Teaching by Showing) The "Try an AI Workflow" option demonstrates a working example first, teaching you how to interact with the agent while giving you a feeling of immediate progress. 8. Instantly.ai (Embedded Support) While the main tour is traditional (tooltips), the real power is hidden inside. As you navigate, AI agents surface to handle complex setup tasks, proving you don't need to be "AI-Native" to be effective. Onboarding is evolving. → From: Teaching users how to use your interface. → To: Teaching AI what the user wants to do. Think I’m exaggerating? Watch your growth rate when competitors can activate users in seconds, while you do it in minutes. I compiled screenshots of all 8 flows into a Figma Board so you can see exactly how they work. I’m also covering how to do AI onboarding in a live workshop with Mickey Alon next week (Jan 28). Comment "AI Onboarding" below and I'll send you the link for both. 👇

Our AI agent onboarded a customer in 4 minutes. perfect score. zero friction. They churned 11 days later. I keep thinking about that.... Not because the onboarding was bad. It wasn't. By every metric we track, it was one of our best. That's the part that bothered me. We assumed every user shows up for the same reason. So we built one path, one flow, one definition of "ready." Then we made a decision that changed how we think about onboarding entirely: We gave our AI agent the authority to end it. Not when a checklist is complete. Not when activation criteria are met. When the agent decides the user has what they came for. That's a fundamentally different kind of autonomy. Most AI in onboarding is smart sequencing - the same flow, just faster. Our agent doesn't sequence. It listens. It figures out why this specific user showed up today. And it adapts.. not just the content, but the entire shape of the experience. A user who came to build gets building. No tour. No walkthrough. A user who came to explore gets room. No pressure. No forced setup. A user who came with no clear intent gets the gentlest version - and gets released the moment they have enough context. The agent doesn't measure success by completion rate. It measures it by intent resolution. And that reframes what onboarding is for. It's not a gate between signup and product. It's not a training module. It's not there to teach users the "right way" to use your software. It's there to listen to what they came for - and make the distance to it as short as possible. The problem with most onboarding isn't bad UX. It's a wrong assumption: that every user needs the same thing. They don't. One way to know if this applies to you: Open your onboarding. Count how many steps are about what YOU need the user to do, and how many are about what THEY came to do. If the first number is bigger - your onboarding isn't onboarding. It's an instruction manual for your product...

If your onboarding feels clunky, confusing, or last-minute… your client can feel it too. The work doesn’t begin after the payment. It begins the moment someone says “yes.” And this is where most people drop the ball. I’ve been there too. Until I started using AI to simplify, personalize, and hold space for my onboarding flow, without losing the human in the process. Here’s what that looks like: Step 1: Welcome, with intention: As soon as a client signs up, I feed their context to ChatGPT: “Write a warm welcome email to a new client who just signed up for [X service]. Acknowledge their goals, set the tone for our work together, and share what to expect this week.” It helps me start the relationship right, with presence, not a template. . . . Step 2: Kickoff kit, custom to them Instead of sending a generic Notion board or onboarding doc… I use AI to create a personalized one-pager: - Their name, goals, timeline - Pre-work checklist - Tools we’ll use - Access links - FAQs based on their niche It makes them feel seen. . . . Step 3: Pre-call prep that’s actually useful If I’ve collected form answers or voice notes, I prompt: “Summarize this client’s challenges and suggest 3 angles I should explore in our kickoff call.” I walk into the call aligned and calm. They feel it. . . . Step 4: Clarity recap - fast After the call, I feed my notes to ChatGPT: “Turn this into a call recap email with clear next steps and aligned expectations. Keep it real, not robotic.” It saves 30 minutes of staring at the screen and helps me build trust in the tiny details. . . . Step 5: Ongoing onboarding, quietly handled Need reminders? Nudges? Status updates? I’ll set up small AI workflows that keep things moving without nagging or micro-managing. Because onboarding isn’t a task. It’s the first chapter of your client experience. You don’t need AI to replace the way you work. But you can use it to hold the edges, so you show up more fully in the middle. That’s what onboarding should feel like. Intentional. Warm. Clear. And deeply human. If you want the actual AI stack I use to support this flow (without feeling cold or corporate), comment "ONBOARD" or DM me and I’ll send it over. Follow Vartika Mishra !

Onboarding isn’t broken — it’s just not human (yet) After reviewing 100s of onboarding flows, I saw the same pattern: → Pop-ups: no one reads → Click tours: everyone skips → FAQ bots: don’t move the needle So when I heard how ✨Jochem van der Veer achieved 2x activation at TheyDo - Journey Management with a self-guided AI avatar onboarding built with Pyne.ai — I was AMAZED. And a fun fact about it…  I had the chance to support pyne in their early days as a Growth Advisor. A year later, the results speak for themselves: - The team refined their GTM and built a powerful new onboarding solution - Companies like TheyDo are now using their AI avatar in real workflows - Most exciting? It helped DOUBLE the Activation rate — powered by thoughtful UX and smart execution 🚀 🎯HOW? They replaced scattered tutorials with a human-feeling “CEO Guide” inside the product. Here’s what stood out: →  𝐒𝐭𝐞𝐩 1: 𝐔𝐧𝐜𝐨𝐯𝐞𝐫 𝐲𝐨𝐮𝐫 𝐡𝐢𝐠𝐡𝐞𝐬𝐭-𝐜𝐨𝐧𝐯𝐞𝐫𝐭𝐢𝐧𝐠 𝐮𝐬𝐞 𝐜𝐚𝐬𝐞 Click tours didn’t work. Video tutorials didn’t scale.  Their turning point?  A cohort that saw the Opportunity Matrix converted 5x faster — but hardly anyone found it alone. → 𝐒𝐭𝐞𝐩 2: 𝐂𝐫𝐞𝐚𝐭𝐞 𝐟𝐨𝐮𝐧𝐝𝐞𝐫-𝐬𝐭𝐲𝐥𝐞 𝐀𝐈 𝐨𝐧𝐛𝐨𝐚𝐫𝐝𝐢𝐧𝐠 𝐟𝐨𝐫 𝐞𝐯𝐞𝐫𝐲 𝐮𝐬𝐞𝐫 They mapped real user journeys, distilled onboarding calls into micro-scripts, and embedded an AI avatar to guide users step by step. Think: a 5-min demo with CEO voice, tailored in-app. → 𝐒𝐭𝐞𝐩 3: 𝐂𝐨𝐦𝐛𝐢𝐧𝐞 𝐇𝐮𝐦𝐚𝐧 𝐢𝐧𝐬𝐢𝐠𝐡𝐭 + 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐩𝐫𝐨𝐦𝐩𝐭𝐬 It’s not about more tooltips. It’s about surfacing the “aha” moment buried deep in your product — and walking users there like a real human would. ✦ 𝐑𝐞𝐬𝐮𝐥𝐭𝐬? +67% 𝐭𝐨 𝐀𝐜𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧 𝐫𝐚𝐭𝐞 (!) The AI guide helped activate 2nd and 3rd teammates inside trial accounts — no more relying on one champion to spread the product love. 👉 Full case study with Onboarding AI playbook — now live on Growthmates newsletter: https://lnkd.in/euRNTNcP — 💬 Would you try an AI-powered onboarding avatar for your product? #onboarding #growth #AI

When Amerisure Insurance set out to modernize their IT operations, they saw an opportunity to rethink how the entire organization gets work done. They evaluated the options, including enterprise platforms that would have required a dedicated team to manage, and landed on Freshservice specifically because they wanted AI built into the system, not added on top of it. What happened next is the part I keep thinking about. Their IT analyst, Daniel McMaster, didn’t just fix IT. He built out 50+ custom workflows, brought legal, HR, underwriting, marketing, and facilities onto the same system, and deployed Freddy AI Insights to surface anomalies he never would have caught manually. One line says it all: "I used to spend an hour every morning looking at ticket trends. Now I spend three minutes with Freddy Insights — and I get better data." It’s the expansion pattern that stays with me. IT fixed its own house, and the rest of the business followed. Not because they were pushed, but because the results spoke for themselves. One platform, scaling across the enterprise without adding headcount or operational complexity. Result: 4,000+ hours saved in 2025. Employee onboarding resolution dropped from 118 hours to 4. Not AI as a moonshot. AI as the thing that gives you your morning back, and makes the whole organization run better while it does it. Read the full story: https://lnkd.in/e6Qnjnei #Freshworks #Amerisure #AI

𝗔𝗜 𝗱𝗶𝗱𝗻'𝘁 𝗳𝗶𝘅 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁'𝘀 𝗺𝗲𝘀𝘀 𝗶𝗻 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗼𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗱𝗶𝗱 When Carolina Dybeck Happe became Microsoft's COO, the complaints started rolling in almost immediately. Not angry emails. Quiet frustration from customers who genuinely wanted to work with Microsoft but found themselves trapped in what felt like administrative quicksand in onboarding of new customers. Here's what happened next. Carolina and her team did something many executives avoid: they went to the Gemba. They actually mapped the entire onboarding journey - every form, every handoff, every "please wait 3-5 business days." Here's what the team found out: 𝟮𝟯𝟬 steps to onboard a customer. What they discovered wasn't laziness or incompetence: • It was layer upon layer of good intentions that had grown into complexity. • Many functions were involved: Sales, Marketing, Product, Finance. • Nobody owned the whole process. Everyone owned a piece. So they did the work. They challenged every single step: 𝐼𝑠 𝑡ℎ𝑖𝑠 𝑎𝑑𝑑𝑖𝑛𝑔 𝑣𝑎𝑙𝑢𝑒 𝑓𝑜𝑟 𝑡ℎ𝑒 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟? 𝑊𝑜𝑢𝑙𝑑 𝑡ℎ𝑒 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟 𝑝𝑎𝑦 𝑓𝑜𝑟 𝑡ℎ𝑖𝑠? 𝗙𝗿𝗼𝗺 𝟮𝟯𝟬 𝘀𝘁𝗲𝗽𝘀 𝘁𝗼 𝗳𝗲𝘄𝗲𝗿 𝘁𝗵𝗮𝗻 𝟯𝟳. Only 37 steps added value for the customer, i.e. 16%. So the team revised the process to focus on these 37 value-adding steps. This is where AI comes in: Once they'd simplified the process, 75% of those remaining steps were automated using AI agents. Not the bloated original process. The streamlined one. This is why I bang on about process improvement before technology. You can't automate your way out of a poorly designed system. You'll just create expensive chaos faster. The sequence matters: 1. 𝗠𝗮𝗽 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 – What's actually happening? 2. 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝗮𝘀𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻𝘀 – Why does each step exist? 3. 𝗘𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗲 𝘄𝗮𝘀𝘁𝗲 – What adds no value? 4. 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗳𝗹𝗼𝘄 – How do we connect what remains? 5. 𝗧𝗵𝗲𝗻 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲 – Where can technology multiply the improvement? Speaking at the Wall Street Journal's Technology Council Summit, Carolina summed it up perfectly: 𝐼𝑛 𝑡ℎ𝑒 𝑒𝑛𝑑, 𝑤ℎ𝑎𝑡 𝑐𝑎𝑚𝑒 𝑜𝑢𝑡 𝑤𝑎𝑠 𝑎 𝑝ℎ𝑒𝑛𝑜𝑚𝑒𝑛𝑎𝑙𝑙𝑦 𝑏𝑒𝑡𝑡𝑒𝑟 𝑒𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒 𝑓𝑜𝑟 𝑡ℎ𝑒 𝑐𝑢𝑠𝑡𝑜𝑚𝑒𝑟𝑠 𝑎𝑠 𝑤𝑒𝑙𝑙 𝑎𝑠 𝑓𝑜𝑟 𝑜𝑢𝑟 𝑡𝑒𝑎𝑚𝑠. (Video link in the comments below). That's the point of continuous improvement. It's not about hitting arbitrary reduction targets or creating pretty charts. It's about making work actually work - for customers and for your people. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗶𝘀𝗻’𝘁 𝗔𝗜. 𝗜𝘁’𝘀 𝗸𝗻𝗼𝘄𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝗮𝗱𝗱𝘀 𝘃𝗮𝗹𝘂𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗶𝘁. If you want to simplify workflows before you automate them, follow me continuous improvement insights.

Yesterday, I had one of those moments where you realize something fundamental just changed. Not incrementally. Fundamentally. I have been thinking a lot about how to redesign customer-facing experiences using AI. Not just improving them, but completely rethinking them. I was reviewing the onboarding process for one of my clients. Like many companies, they provide getting-started guides on their website. They are well-designed. But they are not built for the specific business that is actually implementing the product. For example, a single-location bicycle shop onboarding their POS solution on an iPad needs different guidance than a multi-location apparel retailer running desktop registers and managing complex inventory. Yet they often receive the same documentation, the same training, the same email stream. So I experimented. I went back and forth with ChatGPT to explain what I was trying to do. Then I asked it to structure a detailed prompt that I could drop directly into a vibe-coding tool. The goal was simple: create a tailored "getting-started guide" generator. For example, the customer would select their store type, number of locations, inventory approach, payment processing setup, and register type. The app/workflow/agent would then generate a tailored getting-started guide that speaks their language and reflects their unique requirements. What happened next honestly floored me. Within a few hours, I had a working app. Clean. Functional. Fully generating configuration-specific onboarding guides using language they recognize. Was it perfect? No. Did it need product experts to validate the details? Absolutely. But that is not the point. The point is that the barrier to reimagining onboarding just collapsed. A non-technical person like me can now prototype adaptive onboarding systems in an afternoon. Not slideware. Not theory. Something you can click, test, and iterate on immediately. If this is possible within a few hours and with publicly available documentation, imagine what implementation and product teams can build when they design it intentionally. We are moving from static processes to adaptive post-sale experiences. The real question is: are you designing for that future yet? If you want to go deeper into this particular capability and how you can apply AI inside your post-sale teams, comment “CRAZY.” Tomorrow I’m releasing something deeper on AI in Post-Sale. I'll share what leaders should be thinking about next. #AI-in-Post-Sale #CustomerSuccess #Onboarding #CCO #AdaptiveCS

Every new role comes with a steep learning curve. New acronyms, new tools, new people, and a mountain of information to make sense of. This time, however, AI served as my quiet, invaluable onboarding companion from day one. Here’s how: 𝟭. 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝗟𝗠 𝗵𝗮𝘀 𝗯𝗲𝗰𝗼𝗺𝗲 𝗺𝘆 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗵𝘂𝗯, organizing everything I’m learning into topic-based notebooks I can search and summarize instantly. For example, it helped me quickly grasp intricate project acronyms and distill lengthy process documentation into concise summaries, saving hours of manual reading and recall in my first few weeks. 𝟮. 𝗔𝘀𝗸 𝗚𝗲𝗺𝗶𝗻𝗶 𝘁𝘂𝗿𝗻𝘀 𝗚𝗼𝗼𝗴𝗹𝗲 𝗪𝗼𝗿𝗸𝘀𝗽𝗮𝗰𝗲 𝗶𝗻𝘁𝗼 𝗮𝗻 𝗔𝗜-𝗻𝗮𝘁𝗶𝘃𝗲 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁, maintaining context where work happens. Whether I’m in Docs, Slides, or Sheets, having an AI assistant embedded in the flow of work has been game-changing. My favorite use has been leveraging Gemini to write and debug complex VLOOKUP formulas in large spreadsheets. This is something that previously took me days of manual data analysis work. 𝟯. 𝗚𝗲𝗺𝘀 𝗵𝗲𝗹𝗽 𝗰𝗿𝗲𝗮𝘁𝗲 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗰𝗮𝗽𝘀𝘂𝗹𝗲𝘀, 𝗲𝗻𝗮𝗯𝗹𝗶𝗻𝗴 𝗔𝗜-𝗽𝗼𝘄𝗲𝗿𝗲𝗱 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝘀𝗵𝗮𝗿𝗶𝗻𝗴 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲. To empower my team and stakeholders with self-service information, I built a Gem that answers frequently asked questions about our new product scope and timeline, ensuring consistent and immediate access for everyone. But here’s the biggest realization: 𝗘𝘃𝗲𝗻 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝘀𝗺𝗮𝗿𝘁𝗲𝘀𝘁 𝗔𝗜 𝗯𝘆 𝗺𝘆 𝘀𝗶𝗱𝗲, 𝗻𝗼𝘁𝗵𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗮𝗿𝗲𝘀 𝘁𝗼 𝘁𝗵𝗲 𝗵𝘂𝗺𝗮𝗻 𝗶𝗻𝗽𝘂𝘁 𝗮𝗻𝗱 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻. The colleagues who go out of their way to share, guide, and offer perspectives accelerate not just onboarding, but collective growth. They help understand the why behind the systems, offering the context, nuance, and lived experience that turn information into real organizational insight. AI accelerated how I learn. But people expanded what I learn, and more importantly, how deeply I understand it.

When you step into a new job, the real challenge isn’t the tasks—it’s the context. Who’s really making decisions? What are the unspoken priorities? What does that acronym actually mean? Without answers, most new hires spend months in hesitation. With AI, you don’t have to. You can feed in transcripts, Slack threads, or org charts and quickly get the crash course on your team’s language and dynamics. It gives you clarity where there’s usually confusion. AI won’t do the job for you—but it will help you accelerate the messy middle of onboarding so you can focus on the parts that matter most: building trust, contributing ideas, and making an impact faster.