How do I get started with AI?

Product

Turn feedback into roadmap decisions faster, and test ideas before you build.

Updated 4 days ago · 10 sources

Who already did it

  • Enterprise

    Ticketmaster

    A support team kept asking for ticket sales numbers, and every answer meant a manual data pull.

    Four more features prototyped in three months.

    Figma blog · Mar 2026

    How they did it

    Product manager Brian Muehlenkamp described the tool he wanted in Figma Make and got back a clickable prototype (a fake version you can click through). It shipped as an AI assistant inside internal dashboards. He built four more feature prototypes over the next three months.

    Tool used Figma

  • Mid-market

    Algolia

    Every pricing page test needed a developer, so most test ideas never got run.

    15% lift on the target metric, plus a six-figure pipeline effect.

    Amplitude blog · Jun 2026

    How they did it

    Website optimization manager Brittany Cole used Amplitude's website conversion agent to build and launch an A/B test (two versions shown to real users) on the pricing page. The agent wrote the variants and the custom code. No developer was involved.

    Tool used Amplitude

  • Enterprise

    LIFULL Co.

    Staff who could not write a query had to ask the data team about every odd number.

    Investigation time fell from 90 minutes to about 15.

    Amplitude blog · Jun 2026

    How they did it

    LIFULL connected Amplitude to Figma, Jira and Confluence so anyone could ask questions in plain language. People who could not write a query started checking specs and diagnosing issues on their own.

    Tool used Amplitude

  • Enterprise

    Duolingo

    Duolingo wanted to run more experiments than its engineers had time to build.

    Hundreds of tests run at once. The CEO called the gain "not humongous."

    Duolingo Q1 2026 earnings call transcript · May 2026

    How they did it

    Duolingo put AI tools into its engineering and product teams. On the Q1 2026 earnings call, CEO Luis von Ahn said the number of A/B tests the company can run had gone up.

  • Learn from thisSmall business

    METR

    Your engineers say AI speeds them up. Nobody has checked whether that is true.

    First round: 19% slower with AI. Repeat round: 4%, inside the error bars.

    So: Time the same task on your team, with and without the tool, before you buy.

    METR · Feb 2026

    How they did it

    METR, a non-profit research group, ran a controlled trial. Experienced developers worked on their own open-source projects. Some tasks allowed AI tools and some did not. METR then ran it again with new developers.

What to use, who to call

  • Dovetail

    Stores your interviews, support tickets and sales calls in one place, then summarises the themes it finds.

    free plan, then a custom quoteUnder a weekSeparate software to buyIT sign-off
    Is this your first move?

    What it fixes. Research synthesis (turning hours of interview notes into a short list of findings) eats a week every time you run a study.

    Good fit when. You have more customer conversations than anyone on your team has time to read.

    Skip it if. Your interviews are not recorded or transcribed. The tool has almost nothing to read and will find patterns in four notes.

    Start with. Upload three old interview recordings you already have. Check whether the themes it finds match what you remember hearing.

    Take 7 questions to the call

    Why call them. You are about to run a study a colleague already ran last year and nobody can find it.

    What they fix. Your customer knowledge lives in a hundred documents and three people's heads.

    Also used by. Atlassian, Shopify, Canva, Breville and Deloitte are named on Dovetail's own site

    1. Where are our customer interview recordings stored, in which country, and who inside your company can listen to them?
    2. When your AI gives me a theme, can I click straight through to the exact quote and timestamp it came from?
    3. What does it do with a project that has only four interviews? Does it still report a theme, and does it warn me?
    4. How do you stop one loud customer's opinion being written up as if many people said it?
    5. If we cancel, what do we get back, in what format, and how long do you keep copies of our recordings?
    6. Is our data used to train any model, yours or a third party's, and can we switch that off?
    7. Show me a customer who stopped using you and tell me what they said on the way out.

  • Maze

    Runs tests where real users try your design, plus interviews where an AI asks the follow-up questions.

    quote only; AI interviewer is enterpriseWeeksSeparate software to buyIT sign-off
    Is this your first move?

    What it fixes. You want to test a design this week, but recruiting people and writing up the results takes three.

    Good fit when. You test designs often and the write-up is the part that slows your team down.

    Skip it if. Sensitive or regulated subjects, where an AI asking follow-ups will miss what a trained researcher would hear.

    Start with. Test one screen you already shipped. Compare the AI summary against the raw session clips before you trust it.

    Take 6 questions to the call

    Why call them. Your designers are guessing because getting real users in front of a design takes too long.

    What they fix. You have design questions every week and a research team that can handle one study a month.

    1. When your AI asks a follow-up question, who wrote the rules behind it, and can I read them first?
    2. How do you stop the AI interviewer leading people toward the answer we were hoping for?
    3. Where are the session recordings and transcripts stored, and who at your company can watch them?
    4. With eight participants, what does your report say about confidence, and does it warn me at all?
    5. Which AI features sit behind the enterprise tier, and what does that tier cost for a team our size?
    6. Can we pull out the raw video and transcripts if we leave, and how long do you keep copies?

  • Productboard

    Pulls feature requests out of support, sales and email, groups them into themes, and links them to roadmap items.

    free plan, then from $19 per makerUnder a weekSeparate software to buyIT sign-off
    Is this your first move?

    What it fixes. Feedback triage (sorting incoming requests into themes) is a job nobody owns, so your loudest customer sets the roadmap.

    Good fit when. Requests arrive in five different places and nobody can name your top three themes.

    Skip it if. Fewer than a few hundred requests a quarter. A spreadsheet and one hour of reading beats it.

    Start with. Point it at one quarter of support tickets. Check whether its top theme matches your last roadmap decision.

    Take 6 questions to the call

    Why call them. Sales, support and your inbox all hold requests and nobody can name the top three themes.

    What they fix. The loudest account sets your roadmap because nobody counts what everyone else asked for.

    1. When your AI merges two requests into one theme, can I see why it decided they were the same?
    2. How many AI credits does a normal quarter of our feedback volume actually burn through?
    3. What happens to a request that is badly worded, very short, or written in another language?
    4. Can it tell one request from a large customer apart from the same request from fifty small ones?
    5. If we leave, do we export the notes, the themes, and the links between them, or only the raw notes?
    6. Which of your customers has our request volume, and can I speak to their product lead directly?

  • Figma Make

    Turns a written description into a clickable prototype in minutes.

    included from $16 per Figma seatUnder a weekAlready in software you own
    Is this your first move?

    What it fixes. You cannot get a designer for two weeks, so a good idea sits in a document nobody opens.

    Good fit when. You need to show an idea to engineers or customers before anyone commits build time.

    Skip it if. Anything headed for production. The generated code is for showing an idea, not for shipping to customers.

    Start with. Describe one screen from your next spec in plain English. Bring the result to your next review.

  • Amplitude

    Answers questions about your product data in plain English, and can build an A/B test.

    free to 2M events, then quotedWeeksSeparate software to buyIT sign-off
    Is this your first move?

    What it fixes. You wait days for an analyst to tell you whether last month's release moved anything at all.

    Good fit when. Your product already sends usage data and your team asks the same five questions every week.

    Skip it if. Your usage data is a mess. The agent answers confidently from bad numbers and nobody catches it.

    Start with. Ask it a question you already know the answer to. See whether it gets there the same way.

    Take 6 questions to the call

    Why call them. You ship every week but only measure every quarter, so you cannot tell which change helped.

    What they fix. Your team argues about what a release did because nobody can get the numbers quickly.

    1. When the agent answers a question, can I see the query it ran and the events it counted?
    2. What does it do when our tracking is wrong or missing? Does it say so, or answer anyway?
    3. Who reviews an experiment the agent sets up before it goes live to real customers?
    4. How many events does our current traffic generate, and what does that cost past the free tier?
    5. What data leaves our account when the agent answers a question, and where does it go?
    6. Show me a customer whose agent rollout stalled, and tell me what went wrong for them.

What everyone is asking

  • NewJul 2026

    Best AI for user research synthesis (turning interview notes into themes)?

    Dovetail for pulling themes out of interviews and tickets you already hold. Maze if you also need to run the sessions. Both need recordings before they do anything.

    The short answer

    The comparison is published by Maze, so read it as a vendor's view. Its March 2026 survey of about 500 practitioners is the more useful half.

    Also worth a look. Dovetail, Maze, UserTesting, Productboard Spark

  • NewJul 2026

    Does AI actually make my engineers faster?

    Measured, not much. METR's trial found experienced developers 19% slower with AI. A repeat round found 4%, inside the error bars. Surveys of executives say something else.

    The short answer

    Marty Cagan cites Atlassian's 2026 survey: 89% of executives say AI made work faster, and 6% can point to a return.

    Also worth a look. METR randomized trial, executive surveys

  • NewJul 2026

    Can AI-generated users replace real customer interviews?

    Synthetic users (AI-invented customers, not real people) agree with you too easily. Researchers use them to draft screeners and pilot a study, then test with real people.

    The short answer

    In a May 2026 survey of 150 researchers, 97% used AI somewhere in their work and 8% used AI-generated participants regularly.

    Also worth a look. AI-generated participants, recruited real users

  • NewJul 2026

    Best AI for sorting customer feedback into a roadmap?

    Productboard Spark if requests arrive as text from many places. Amplitude if you want feedback sitting beside usage data. Both need real volume to earn their price.

    The short answer

    Canny wrote that comparison and ranks itself first. It does list real weaknesses for each tool, including price floors and volume minimums.

    Also worth a look. Productboard Spark, Enterpret, Canny, Amplitude AI Feedback, Thematic

  • NewJul 2026

    Can a product manager build a prototype (a clickable fake) without a designer?

    For showing an idea, yes. Product managers at Ticketmaster, ServiceNow and Affirm built their own in Figma Make. What comes out is a demo, not shippable code.

    The short answer

    Both sources are vendor blogs, so treat the numbers as the vendor's. The people and companies named in them are checkable.

    Also worth a look. Figma Make, Amplitude AI Agents

Try this today

  • Rank your feedback by theme

    20 min

    A short list of what customers actually asked for, with a real quote behind each one.

    Copy the prompt

    You are a product researcher. Below is customer feedback my team collected from support tickets, sales calls and emails. Group it into themes. For each theme give four things: the problem in the customer's own words, the number of separate items that mention it, one exact quote, and whether it reads as a bug, a missing feature, or a confusing design. Rank themes by how many items mention them, not by how strongly they are worded. Mark any theme with only one item as 'single mention'. Add nothing that is not in the text. Put anything you cannot place under 'Unclear'. Feedback: [PASTE FEEDBACK]

    One check first. Check the sample size and read two raw quotes yourself. A summary is not the same as talking to a customer.

  • Pressure-test your spec

    15 min

    The open decisions and missing cases in your spec, written down before anyone starts writing code.

    Copy the prompt

    You are a senior engineer reviewing a product spec before your team commits to it. My spec is below. Do three things. First, list every decision the spec leaves open, hardest to work around first. Second, list the edge cases it does not cover — what happens when data is missing, the user is brand new, or the action fails. Third, write the five questions you would ask me in the review. Quote the line that prompted each point, or write 'not stated' if the spec is silent. Do not suggest new features and do not rewrite the spec. Spec: [PASTE YOUR SPEC]

    One check first. It finds gaps, not whether the thing is worth building. Your customer evidence still decides that.

  • Rehearse cutting a feature

    15 min

    You walk in knowing the three hardest objections and roughly how you will answer each.

    Copy the prompt

    You are playing my head of sales in a meeting. I will tell you which feature I am cutting this quarter and why. Argue against me with the reasons a sales leader really uses: revenue at risk, a named account, a promise already made. Give me your three strongest objections, hardest first, one at a time. After each answer I give, say whether it would satisfy you and what still bothers you. Do not be polite and do not agree early. Stay in role until I write 'stop'. The cut: [DESCRIBE THE FEATURE AND WHY YOU ARE CUTTING IT]

    One check first. The model is guessing at what your stakeholder cares about. Ask them first, then use this to rehearse.

Worth following

  • Lenny Rachitsky

    Writes a product, growth and career newsletter with more than 1.2 million subscribers.

    weekly or more · newsletter

    Why them

    Deeply researched advice aimed at product leaders and founders, not theory about the craft.

  • Teresa Torres

    Author and coach who runs Product Talk, a site about making better product decisions.

    a few times a month · blog and podcast

    Why them

    She teaches continuous discovery (weekly customer conversations instead of one big study a year) with worked examples.

  • Marty Cagan and SVPG

    Silicon Valley Product Group partners writing on product strategy, discovery and how teams are run.

    a few times a month · blog

    Why them

    Cagan is blunt about AI speed that does not produce better outcomes. His July 2026 piece says so.

  • Mind the Product

    A large product management community and publication, now owned by Pendo.

    near-daily · publication

    Why them

    Near-daily posts and podcasts from working product leaders, and it names who is speaking.

  • Paweł Huryn — The Product Compass

    Writes a product management and AI newsletter with more than 135,000 subscribers.

    several times a month · newsletter

    Why them

    Step-by-step playbooks and prompts you can run the same day, rather than opinion pieces.