If you’ve spent any time in a business context over the last two years, you’ve heard the promises. AI will transform everything, automate the impossible, and change entire categories of work — depending on which headline you read last. For Customer Experience Program Owners, that noise creates a particular kind of frustration, because the gap between what AI claims to do and what it actually delivers inside a real program is rarely addressed with any specificity. This guide is an attempt to do exactly that. It covers six AI capabilities available in Qualtrics today — what each one does and how to put it to work. Less manifesto, more manual.
The capabilities covered here — Conversational Feedback, Insights Explorer, Qualtrics Assist for Customer Experience, Automated Text Analytics, Experience Agents, and AI Response Task — are not a vision of what your program could look like in three years. They are available now, inside the platform you already use. What they share is the same underlying logic: your program already generates significant data, and these tools exist to help you do more with it without proportionally growing your team or your workload.
If you’re relatively new to Qualtrics, this guide will help you understand what’s available and where to start. If you’re running a more mature program, it will help you identify where AI can close the gaps that manual workflows leave open. Start with the section most relevant to where your program is today rather than reading straight through — each section is designed to stand on its own.
Conversational feedback
Most surveys have at least one open-text question, and most open-text questions produce a familiar problem: short, vague responses that don’t tell you much. “It was fine.” “Good service.” “Could be better.” These answers aren’t useless, but they’re not actionable either — and the standard fix, adding more questions, tends to reduce completion rates rather than improve response quality. Conversational Feedback takes a different approach. Rather than asking more upfront, it waits. When a respondent submits a vague or incomplete open-text answer, Qualtrics AI detects the gap in real time and generates a follow-up question tailored to what the respondent actually wrote, all within the same survey session, without any re-contact.
How it works
You can configure Conversational Feedback inside the survey builder in six clicks. On any text-entry question, toggle on “Include follow-up question” under Conversational Feedback. From there, you can customize whether the company name appears in the follow-up, cap the number of follow-ups per question (one is recommended), and block specific topics from triggering a prompt. Once live, the AI monitors each open-text response as it’s submitted and generates a contextual follow-up only when the response lacks sufficient detail. Respondents who gave a complete, useful answer continue their survey without interruption.
Who benefits most
Customer Experience Program Owners running high-volume listening programs get the most immediate value — particularly those where short or one-word responses are a recurring problem. CX analysts tasked with root cause analysis also benefit significantly, since richer verbatims mean less time spent inferring what a score actually reflects. Any team where leadership regularly asks “but why did they rate us that way?” will find this useful.
Where teams use it
The most common application is post-transaction surveys, where brief responses often fail to identify the specific friction point that drove a low score. NPS detractor follow-up is another strong use case: when a customer submits a low score, the AI surfaces the reason before the survey session ends, giving the closed-loop team a specific issue to address rather than a score to explain. Relationship surveys and onboarding surveys — where open-ended questions historically underperform — are also well suited.
How to get more out of it
- Apply selectively — works best on high-signal open-text questions where richer responses would change what you do with the data
- Cap follow-ups at one per question to protect completion rates and avoid respondent fatigue
- Block sensitive topics (legal matters, HR disputes) using the topic exclusion configuration
- Pair with Automated Text Analytics so richer responses are analyzed at scale without adding manual coding work
- Review response patterns monthly — recurring themes in follow-up responses are often a signal to revise the original question design
What it delivers
Programs using Conversational Feedback see 40% of respondents expand their initial answer when prompted, with nearly four times more words per response on average, and no increase in survey dropout rates. The practical effect is a meaningful improvement in qualitative signal without changing your survey structure, extending your fielding period, or re-contacting respondents.
Insights explorer
The manual process of reviewing open-text feedback is one of the more time-consuming parts of running a Customer Experience program. Depending on response volume, a single weekly verbatim review can consume hours of analyst time reading, categorizing, and summarizing responses that could be spent on what those responses actually mean and what to do about them. Insights Explorer replaces that first-pass work. It’s an AI-powered text analytics tool built into Qualtrics that automatically surfaces key themes from open-ended responses, generates concise summaries, and produces actionable headlines.
How it works
Access Insights Explorer through the top-right menu in your Qualtrics account. Select “Generate an Insight,” choose the “Top themes to look out for” template, select your dataset and open-text question or questions, and apply any filters — by segment, date range, or available data field — to focus the analysis. The AI processes responses and returns synthesized themes, representative quotes, and a summary in minutes. Results can also be scheduled for automated weekly or monthly delivery via the Generate an Insight workflow task, which means stakeholders receive a consistent theme report without anyone manually triggering it.
Who benefits most
Customer Experience Program Owners who spend significant time each week on verbatim review will see the most immediate return. Insights Managers fielding ad hoc requests benefit from the speed with which it handles queries that would otherwise queue behind other work. Research teams can use it as a first-pass screening step before deeper qualitative analysis begins.
Where teams use it
The most common use is weekly NPS verbatim theme reports delivered to stakeholders on a scheduled basis. Detractor segment deep-dives isolating a specific customer cohort to identify shared pain points are another frequent application. Teams also use it for ad hoc topic queries, pulling what customers have said about a specific product area or process without building new reports or waiting for analyst availability.
How to get more out of it
- Start with your highest-volume open-text questions, where there’s enough signal for clear theme emergence
- Use segment filters to compare themes across customer types, regions, or journey stages, the “edit filters” button makes it quick to run new cuts of the same dataset
- Get better output by using it with TextiQ or Automated Text Analytics as opposed to open text alone
- Validate AI-generated themes against raw verbatims before presenting to executive stakeholders to build credibility and catch miscategorized responses
- Treat output as a first-pass filter, then use it to direct where deeper manual review is actually warranted
- Pair with Qualtrics Assist for Customer Experience to drill into specific themes via natural-language follow-up questions
What it delivers
Teams using Insights Explorer consistently report moving from days to minutes on the time between raw data and stakeholder-ready theme summaries. The more durable shift is structural: Customer Experience Program Owners who previously functioned as data processors begin operating as strategic advisors, because the first-pass work that consumed most of their reporting time is no longer manual.
Qualtrics assist for customer experience
Most Customer Experience dashboards are built to answer the questions you anticipated when you built them. That works well for structured metrics like scores, trends, and driver analysis but it creates a bottleneck when a leadership question arrives that the current dashboard layout doesn’t directly address. The usual path is a request to an analyst, a wait, and a response that may or may not arrive before the meeting it was needed for.
Qualtrics Assist for Customer Experience is an AI assistant embedded directly inside CX dashboards. It lets anyone (not just trained analysts) ask plain-language questions about open-text customer feedback and receive topic-level answers in seconds, without building new widgets, rebuilding dashboards, or routing requests through analyst queues.
How it works
Qualtrics Assist requires a response ticker widget with Text iQ topic models configured in the dashboard Text iQ tab — this is a prerequisite, and the feature won’t function correctly without it. One important scope note: Assist pulls exclusively from dashboard Text iQ. It does not pull from Discover topics or from embedded Discover dashboard pages. Dashboard Text iQ and Survey Platform Text iQ are treated as separate environments, so topic models need to exist within the dashboard ecosystem for Assist to interpret them.
Once that’s in place, enable Qualtrics Assist from the dashboard settings. A prompt icon appears in the lower-right corner of the enabled dashboard. Type a natural-language question — “What are the top three negative themes in our digital channel this quarter?” — and the AI returns topic-level answers drawn from the comments and data within your active dashboard filters and date range. Follow-up questions can be asked within the same session to drill into any theme; resetting the conversation starts a fresh analysis.
Who benefits most
Customer Experience Program Owners who regularly field ad hoc questions from leadership will find this reshapes how they prepare for those conversations. CX Directors preparing for executive briefings get real-time theme summaries from recent feedback without analyst involvement. Non-technical stakeholders like operations leaders and product managers can query VoC (voice of customer) data directly without needing someone to interpret the dashboard for them.
Where teams use it
Executive briefing prep is a primary use case: thirty minutes before a leadership meeting, a Program Owner can ask what customers have said about a specific process this month and arrive with a current, specific answer. Root cause identification for emerging topic spikes is another, instead of waiting for analyst bandwidth to investigate a score movement, a Program Owner can query the trend directly. Teams also use it to generate quick theme summaries for stakeholder updates and weekly reports without manual verbatim review.
How to get more out of it
- Use follow-up questions within the same session to drill into themes before resetting — the session retains context, making sequential questions more efficient
- Reset before switching to a new analysis topic to prevent context from one question bleeding into the next
- Share Assist outputs directly with stakeholders as spot insights between scheduled reviews — a practical way to demonstrate program value outside formal reporting cycles
- If your program relies on Discover topics or Survey Platform Text iQ, you don't have to rebuild from zero — Qualtrics provides pre-built topic libraries, recommended topics, and automatic topic suggestions to accelerate setup. Moving or recreating selected topics in dashboard Text iQ is what unlocks Qualtrics Assist, making those themes available for direct querying and more interactive analysis.
What it delivers
The core shift Qualtrics Assist produces is removing the analyst as the required intermediary for routine qualitative insight requests. Customer Experience Program Owners who previously spent hours preparing qualitative summaries for leadership can respond to the same requests in seconds, and non-technical stakeholders who never had direct access to customer feedback data can begin querying it themselves.
Automated text analytics
Survey data tells part of the story. What customers say in contact center calls, post on review platforms, and share on social channels tells the rest, but for most teams, those sources live in separate systems, analyzed separately, often not analyzed at all because the manual effort required isn’t practical at volume. The result is a Customer Experience program with a significant blind spot built into its design. Automated Text Analytics addresses that gap directly. It uses AI and machine learning to automatically identify, categorize, and analyze themes across open-text feedback from surveys, contact center transcripts, social posts, and online reviews, without manual topic coding and without requiring separate analysis workflows for each data source.
How it works
Once Automated Text Analytics is implemented, feedback from surveys, contact center interactions, social listening, and online reviews can be analyzed once it is in the platform. Specially tuned LLMs process all incoming unstructured data automatically, detecting topics and categorizing each piece of feedback without manual rule-writing. Sentiment analysis runs simultaneously, scoring each topic at the sentence level — from Very Positive to Very Negative — using a transformer-based multilingual model. Topics and sentiment scores are enriched directly into your dataset and surfaced in dashboards, available for filtering, trending, and driver analysis alongside structured metrics. As new data arrives, the model tags them continuously.
Who benefits most
Customer Experience Program Owners currently spending significant time manually coding open-text responses will see the most direct return. Current users of the TextiQ feature who want a deeper understanding of their unstructured data. CX Analysts who need to unify themes across omnichannel data sources rather than maintaining separate workflows for each will benefit from having a single analytics layer. Any program scaling to new data sources, like adding contact center or social data, will find manual analysis impractical at the volumes those channels generate.
Where teams use it
Omnichannel theme unification is the primary use case: understanding what customers are saying across surveys, contact center, and social simultaneously. Contact center intelligence is another — automatically tagging call transcripts and chat logs with topics and sentiment makes unstructured interaction data reportable in dashboards alongside survey metrics. Teams also use it for emerging issue detection, where continuous automated topic analysis surfaces new themes before they register in structured metrics like NPS or CSAT.
How to get more out of it
- Connect all available data sources before building dashboards — running automated text analytics across a unified omnichannel dataset delivers significantly more value than survey data alone
- Pair with Insights Explorer to move from theme detection to AI-generated summaries: Automated Text Analytics identifies what customers are talking about; Insights Explorer explains what the patterns mean
- Use topic trend analysis to monitor month-over-month theme movement — volume spikes often precede NPS or CSAT shifts by several weeks, giving the program a lead indicator rather than a lagging one
- Combine AI-detected topics with structured metrics in dashboards to move beyond score-only reporting and show leadership what is driving CX performance
What it delivers
The operational benefit is replacing weeks of manual text coding with continuous, automated analysis across every channel where customers are speaking. The strategic benefit is giving Customer Experience Program Owners a complete picture of customer sentiment, not just what surveys capture, but what every listening channel is producing, in a single, unified view.
Experience agents
Closing the loop is an integral part of excellent customer experience, but brands often fall short because following up often doesn't scale. Experience Agents change that. These agents sit at the intersection of listening and acting, autonomously moving from feedback to resolution the moment a signal is detected. When a customer shares feedback, Experience Agents determine the right response in real time: surfacing the right information to answer a question or guide a next step, take autonomous action to resolve an issue without human intervention, or initiate an escalation when a situation requires human oversight. The result is a close-the-loop program that operates at the speed and scale that modern experience management demands, converting feedback into outcomes rather than letting it sit in a queue.
How it works
Experience Agents are configured in Agent Studio, where you define agent behaviors, build knowledge bases, and connect to third-party tools. Once configured, agents can be embedded directly in surveys, or integrated into ticketing workflows. In a survey context, the agent operates during the survey session in-the-moment, detecting dissatisfaction as a respondent moves through the questionnaire and taking action before the session ends. In ticketing contexts, agents summarize incoming tickets, recommend actions, automate responses, and handle escalations.
Who benefits most
Customer-facing teams that handle high volumes of routine interactions benefit from the automation of those interactions, freeing capacity for higher-value work. Support and ticketing teams gain AI-generated summaries, recommended actions, and automated ticket actioning that reduces processing time and improves consistency. Organizations with complex, multi-channel customer journeys, where consistent, personalized engagement across touchpoints is both critical and difficult to maintain manually.
Where teams use it
The in-survey use case is one of the most distinctive: an agent embedded in a survey can detect that a respondent is expressing dissatisfaction mid-completion and take action, offering a resolution, routing to support, or triggering a follow-up workflow, before the respondent finishes. On the right data enrichments, agents provide real-time assistance and personalized support based on context from Qualtrics and other data sources. In ticketing systems, they handle the intake work — summarizing, categorizing, recommending next steps — so support teams spend their time on resolution rather than triage.
How to get more out of it
- Deploy across multiple touchpoints rather than a single channel, so you can meet the customer where they are instead of waiting for them to come to you.
- Review agent activity logs regularly to monitor agent actions and catch quality or compliance issues before they surface in customer feedback
- Iterate on agent behaviors using feedback and analytics over time — the agents improve, but only if the behaviors are being actively refined
- Work with your Qualtrics Account Team to configure agents for your specific workflows, particularly for complex multi-channel deployments
What it delivers
Experience Agents change the fundamental timing of how a Customer Experience program responds to customer signals. Instead of a cycle that runs survey → analysis → report → decision → action, the agent compresses that sequence into a single real-time event. The practical effect is a program that doesn’t just measure experience — it shapes it, at scale, without proportionally growing the team responsible for doing so.
AI response task
Experience Agents handle real-time, in-the-moment interactions — the intervention that happens while a customer is still in a survey, a chat session, or a support ticket. AI Response Task operates at a different point in that sequence. It’s a post-event capability: a workflow automation tool that runs after feedback has been collected to analyze responses, draft personalized outreach, and categorize feedback at scale. Where Experience Agents act during the experience, AI Response Task acts on what the experience produced.
It’s also worth noting that these two capabilities are complementary by design. AI Response Task can function as a tool that Experience Agents draw on — an agent can trigger a response task as part of a broader workflow, combining real-time detection with structured post-event follow-through. For teams building out their automation stack, understanding that relationship helps clarify where each capability fits.
How it works
Add an AI Response Task to any existing Qualtrics workflow and set it up to be triggered by a survey submission, a review event, a ticket, or a schedule. Write a natural-language prompt using available feedback data fields: for example, “Draft an empathetic response to this review in brand voice, under 75 words, acknowledging the customer’s specific concern.” The Qualtrics LLM executes the prompt and returns a structured output. That output can then be chained with downstream actions — sending an email, updating a ticket, logging to an embedded data field, or triggering another task. Because it’s native to the platform, no data needs to move outside Qualtrics to run it, and no external API credentials are required.
Who benefits most
Customer Experience Program Owners running closed-loop programs who need to scale detractor recovery or review response without growing headcount see the most immediate value. CX Operations teams managing online review response programs at high volumes benefit from the consistency and speed it enables. Insights teams looking to automate repetitive text tasks — categorization, summarization, tagging — that currently consume analyst time also find it a practical fit.
Where teams use it
Online reputation management is a primary post-event use case: auto-drafting brand-voice-aligned responses to Google, Yelp, or Trustpilot reviews after they’re submitted. NPS detractor recovery is another — triggering a personalized follow-up email when a low score arrives, drafted by AI and reviewed before sending. Teams also use it to classify incoming open-text responses into operational categories automatically, and to generate weekly AI-written feedback summaries routed to the appropriate stakeholders.
For teams managing online reputation at scale, the Online Reputation Management solution extends this capability further: when a ticket is created for an incoming review, Qualtrics can automatically generate a response tuned to your brand guidelines and standard operating procedures — ensuring consistency and speed in public-facing communications without requiring a team member to draft each reply from scratch.
How to get more out of it
- Test prompts in a sandbox or low-volume workflow before deploying to production — output quality varies with prompt specificity, and catching issues early is easier than correcting them at scale
- Include explicit brand voice guidance in every prompt: tone, word count limit, and formatting expectations
- Chain with Text iQ topic and sentiment enrichments to give the LLM richer context and improve output quality
- Build a human review step into high-stakes workflows — executive escalations, legally sensitive feedback — before AI-drafted content is sent
- Start with lower-risk use cases like internal summaries and categorization before extending to customer-facing response automation
What it delivers
AI Response Task closes the loop at scale without proportionally growing the team required to do it. Response consistency improves, draft time drops from minutes per response to seconds at scale, and the Customer Experience program begins functioning as an operational efficiency contributor — not just a source of insight, but an active participant in customer recovery and reputation management.
These capabilities compound
These features aren't theoretical. The time savings are real. The signal quality is measurably better. The closed-loop cycles are shorter. And the Customer Experience Program Owners running these programs are showing up to leadership conversations differently..
That shift doesn't happen all at once, and it doesn't require deploying everything in this guide simultaneously. It tends to happen incrementally, one friction point at a time, as teams identify where manual effort is costing them the most and replace it with something that compounds. The programs that are furthest along didn't start with a comprehensive AI strategy. They started with one capability that worked, built confidence, and expanded from there.
The question worth sitting with isn't whether AI belongs in your Customer Experience program. At this point, that's settled. The more useful question is where the gap between what your program currently produces and what it could produce is largest — and which of these capabilities closes it first.
