You bought Pendo to cut support tickets. A year in, the guides are live, the checklists are built, and the queue looks roughly the same. Adoption dashboards show feature usage climbing, but Support is still fielding the same questions about permissions, exports, billing, and why a record did not sync. The usual conclusion is that the guides need better targeting or a fresh round of authoring. That is rarely the problem. Product tours and tooltips are built to teach a user a path through your UI. Most support tickets are not requests for a path. They are questions about a specific account's state, and no scripted guide can answer those. The gap is not tour quality; guidance and resolution are two different jobs.
Pendo does two things well, and it is worth being precise about them before criticizing anything. It is a genuinely strong product analytics platform: retroactive event capture, feature adoption tracking, path analysis, and segmentation that product teams use to decide what to build. And it is a mature no-code authoring environment, where a PM or CSM can build a guide, target it to a segment, and ship it without engineering.
Those are real capabilities and Worknet does not replicate either one. If your goal is to understand which features get used and to walk new users through a standard first-run flow, Pendo is a reasonable purchase and you should not rip it out. The problem is narrower than the product: it is the expectation that a tool built to guide will also resolve.
Because a guide is authored in advance and a support ticket is generated by a specific situation. A guide can only contain what someone wrote into it, targeted to a segment someone defined. It has no read on the account in front of it: what plan they are on, which integration is failing, whether their admin revoked a permission last Tuesday, what the last five tickets from that account were about.
Run the exercise on your own data. Pull last quarter's tickets and sort them into two buckets: questions with one correct answer that is identical for every user, and questions whose answer depends on the account. In most B2B SaaS products the second bucket is the larger one, and it is the bucket that scripted content structurally cannot touch. Better targeting does not help, because targeting selects which pre-written content to show, not what the answer is.
The pattern is consistent across CX teams we talk to. Tours reliably move first-run and feature-discovery questions. They do not move:
There is a second, quieter failure mode: content decay. Guides are pinned to UI selectors and to a product that keeps shipping. When the UI moves, the tour breaks or, worse, silently points at the wrong element. Teams end up with a maintenance queue nobody owns, and the guides that were meant to reduce work start generating it.
Guides arrive on the platform's schedule, not the user's. A walkthrough fires on first login, when the user has no context to attach it to and every incentive to dismiss it. Three weeks later, when they hit real friction on a real task, the guide is not there and the tooltip they need was dismissed once and never returns.
This is why in-app guidance metrics and support metrics often move in opposite directions. Guide completion rates can look healthy while contact rate per active account stays flat, because completion measures whether someone clicked through content, not whether they got unstuck. If you are evaluating in-app tooling on ticket deflection, measure contact rate per active account by ticket category, not raw ticket count and not guide engagement.
Three things a scripted flow cannot provide. First, the system has to answer the user's actual question rather than route them to a place where an answer might live. Second, it needs account context: plan, role, permissions, integration state, ticket history, so the answer is true for this user and not just true in general. Third, it has to work at the moment of friction, triggered by what the user is doing now, not by a segment rule written last quarter.
That is the design Worknet is built around. It is a proactive AI engine that intervenes in-product before a ticket exists, with the account context to give a specific answer, and it runs the same engine across Slack, Salesforce, Zendesk, and in-app rather than making the in-product experience a separate content silo. It goes live in days via API or MCP and is configured in plain English, so there is no library of flows to author or maintain. And because it is watching real user-level friction, it surfaces expansion and risk signals long before the QBR.
For most teams, run both. Worknet is not a no-code tour builder and it is not a product analytics suite, and it will not replace Pendo's flow authoring or its adoption dashboards. If those are why you bought Pendo, keep them.
What changes is the job you assign to each. Pendo owns onboarding flows, feature announcements, and the product analytics your PM team runs on. Worknet owns resolving in-product friction and deflecting support, in-app and everywhere else your users ask. The honest version of the decision: if your ticket queue is dominated by navigational and first-run questions, a DAP is the right tool and you may not need more. If it is dominated by account-specific questions, no amount of guide authoring will move it, and you are solving the wrong problem with a very good tool.
Before you buy anything else, categorize a quarter of tickets and calculate what share is account-specific. That number tells you your realistic ceiling for guide-based deflection, and it usually surprises people. If most of your queue sits above that ceiling, the fix is not more content.
If you want to see what resolution in-product looks like against your own ticket mix, book a Worknet demo and bring your ticket categories. We will be direct about which ones we move and which ones we don't.
Worknet is an AI adoption agent for the software your customers use and the software your employees use. It sits with every user inside the app, offering proactive support and onboarding. It answers with context, acts on the user's behalf within the permissions you set, and brings in the right person from your team when needed. For SaaS companies, what it learns becomes expansion signals for sales, risk alerts for success, and fixes for product. For IT, it rolls out across the apps you buy with no vendor changes and shows where employees get stuck. An AI-native alternative to Pendo, WalkMe and Appcues.
Yes, but in a narrow band. Pendo reliably deflects first-run, navigational, and feature-discovery questions, the ones a scripted walkthrough can answer identically for every user. It does not deflect account-specific tickets, which are the majority of the queue in most B2B SaaS products. Teams that measure deflection by ticket category usually find a real drop in onboarding questions and almost no movement in configuration, permissions, billing, and integration tickets.
Tours deflect questions with one correct answer that is the same for every user: where a feature lives, what a setting does, how to complete a standard first-time setup. They cannot deflect questions whose answer depends on the user's plan, role, permissions, data state, or integration status, because a tour has no access to that context and cannot branch on it.
No, not for what Pendo is best at. Pendo is a product analytics and no-code flow-authoring platform; Worknet is neither a tour builder nor an analytics suite and does not try to be. Worknet replaces the assumption that in-app guidance will deflect support. Many teams run both: Pendo for onboarding flows and product analytics, Worknet for resolving in-product friction across Slack, Salesforce, Zendesk, and in-app.
Digital adoption platform rollouts typically run weeks to months, because someone has to author every flow, map it to selectors in your UI, and keep it current as the product changes. Worknet deploys via API or MCP and is configured in plain English, so teams are usually live in days. The ongoing cost profile differs too: authored content needs maintenance, an AI engine reading your knowledge sources does not.
Segment your ticket volume by category before you start, then track each category separately. Look at contact rate per active account rather than raw ticket count, since raw volume moves with growth. Also watch time-to-resolution for the categories you expect to deflect, and the share of sessions where a user hit friction in-product and did not open a ticket afterward.
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