Executive Summary

Choosing an enterprise survey platform is no longer just about creating questionnaires. Modern organizations need a platform that can design studies with AI, recruit participants, distribute surveys across multiple channels, analyze results automatically, and scale securely across hundreds or thousands of employees.

For more than two decades, Qualtrics has been one of the most recognized names in enterprise experience management. It offers an extensive set of capabilities spanning customer experience, employee experience, market research, and strategy, and is deployed by many of the world’s largest organizations.

Over the past several years, however, buyer expectations have shifted significantly. Artificial intelligence has fundamentally changed how researchers and business teams create, field, and analyze surveys. Organizations increasingly expect AI to generate survey drafts, improve question wording, summarize thousands of responses, identify themes, and accelerate the entire research process.

At the same time, many enterprises are looking for platforms that are faster to deploy, easier to use, and more approachable for teams outside of dedicated research departments.

Sprig represents this newer generation of enterprise survey platforms. Rather than adding AI onto legacy workflows, Sprig was built around AI-powered survey creation, participant recruitment, survey delivery, and analysis from the beginning. The platform combines enterprise-grade governance with modern user experience, native email delivery, integrated participant recruitment, and AI agents that help teams move from research question to actionable insights in dramatically less time.

Both platforms are capable of supporting enterprise research programs, but they are optimized for different priorities.

This guide provides a detailed comparison of Sprig and Qualtrics across every major evaluation category, including:

AI capabilities
Survey creation
Native email delivery
External research panels
Market research
Product research
Customer experience
Advanced research methods
Enterprise administration
Security and compliance
Reporting and analytics
Integrations
Scalability
Total cost of ownership

Whether you’re evaluating enterprise survey platforms for the first time or considering a migration from Qualtrics, this guide explains where each platform excels and which organizations are likely to benefit most from each approach.

At a Glance

Which Platform Should You Choose?

The right platform depends less on company size and more on how your organization approaches research.

Choose Sprig if…

Sprig is best suited for organizations that want to modernize how surveys are designed, distributed, and analyzed.

It is particularly well suited for organizations that:

Want AI embedded throughout the entire research workflow rather than added as a separate feature
Need a single platform for customer research, product research, market research, and in-product feedback
Want to recruit research participants without purchasing or managing separate panel vendors
Need native email delivery for large-scale customer surveys without relying on additional distribution tools
Value a modern user experience that enables researchers and business stakeholders to collaborate effectively
Want to move from research idea to executive-ready insights in hours instead of days

One notable differentiator is Sprig’s integrated participant recruitment. Researchers can source respondents from a panel of more than 5 million verified B2B and B2C participants, filtering audiences using more than 300 demographic, firmographic, geographic, behavioral, and professional targeting attributes without leaving the survey workflow.

Similarly, organizations can distribute customer surveys using native email delivery directly within Sprig, eliminating the need to export surveys into separate campaign tools. Combined with web links, QR codes, SMS, APIs, and patented in-product SDKs, this allows teams to design, recruit, field, and analyze research from a single platform.

For organizations adopting AI across their research organization, these integrated workflows can substantially reduce operational complexity while accelerating time to insights.

Choose Qualtrics if…

Qualtrics remains an excellent choice for organizations with mature experience management programs, particularly those already invested in the broader Qualtrics ecosystem.

It may be the better fit if your organization:

Has significant existing investments across multiple Qualtrics products or modules
Requires specialized experience management workflows beyond survey research
Has internal administrators dedicated to managing a large-scale Qualtrics deployment
Prioritizes maintaining established enterprise processes over adopting newer AI-native workflows
Has already standardized reporting, governance, and integrations around the Qualtrics platform

For many global enterprises, the cost and operational complexity of migrating from an existing Qualtrics implementation may outweigh the benefits of switching platforms. In those cases, continuing with Qualtrics can be the pragmatic choice.

Comparison Methodology

Every enterprise survey platform claims to be the most powerful, easiest to use, or most innovative. Those claims are only useful if they are evaluated consistently.

This guide compares Sprig and Qualtrics across the capabilities that matter most to enterprise buyers:

Survey authoring and questionnaire design
AI-assisted survey creation
AI-powered analysis and reporting
Participant recruitment and external panels
Native email delivery and survey distribution
Product research capabilities
Customer experience research
Market research support
Advanced quantitative methodologies
Enterprise governance and security
Administration and user management
Analytics and reporting
Integrations and extensibility
Scalability and operational complexity

Rather than focusing on lengthy feature lists alone, this comparison also considers how efficiently each platform enables organizations to complete the end-to-end research lifecycle, from defining a research question to delivering actionable insights to stakeholders.

What You’ll Learn

By the end of this guide, you’ll understand:

How Sprig and Qualtrics differ in platform architecture and philosophy
Which platform provides the strongest AI capabilities today
How native participant recruitment changes market research workflows
Why integrated email delivery can simplify enterprise survey operations
Which platform is easier for researchers and business users to adopt
Where each platform excels for product, customer, and market research
Which organizations are likely to achieve the greatest return on investment with each platform

In the next section, we’ll examine how the two platforms were built, the problems they were originally designed to solve, and why those architectural differences continue to shape the experience for enterprise research teams today.

What Are Sprig and Qualtrics?

Although Sprig and Qualtrics both help organizations collect customer feedback through surveys, they were built in different eras to solve different problems. Understanding that distinction is one of the best ways to evaluate which platform aligns with your organization’s future.

At a high level:

Qualtrics is a comprehensive enterprise experience management platform that has evolved over more than two decades. It serves a wide range of use cases across customer experience (CX), employee experience (EX), market research, brand tracking, and strategy.
Sprig is an AI-native enterprise survey platform designed to help organizations create, recruit, field, and analyze surveys significantly faster using modern AI workflows while maintaining enterprise-grade security, governance, and scalability.

Both platforms can support sophisticated enterprise research programs. The difference is less about whether they can accomplish a task and more about how efficiently teams can move from a research question to an actionable decision.

Qualtrics Overview

Founded in 2002, Qualtrics helped define the enterprise survey software category. Over the years, it expanded from survey software into a broader experience management platform used by thousands of organizations worldwide.

Today, Qualtrics supports a wide variety of enterprise initiatives, including:

Customer satisfaction (CSAT)
Net Promoter Score (NPS)
Customer Experience (CX)
Employee engagement
Voice of the Customer (VoC)
Brand research
Market research
Strategic planning
Contact center feedback
Website feedback
Digital experience analytics

One of Qualtrics’ greatest strengths is the breadth of its ecosystem. Large enterprises can standardize many different experience measurement programs on a single platform while taking advantage of mature governance capabilities, extensive integrations, and sophisticated administration.

For organizations that have invested in multiple Qualtrics products over many years, the platform can become deeply embedded in business operations.

However, that breadth also contributes to one of the most common themes heard from organizations evaluating alternatives: complexity.

Many teams describe Qualtrics as highly configurable, but requiring significant training, administrative expertise, and implementation effort to unlock its full capabilities. Organizations often designate platform administrators or research operations specialists to configure permissions, maintain libraries, manage governance, and support internal users.

That model made sense when surveys were primarily created by centralized research teams. Today, many organizations want to empower product managers, marketers, customer success managers, and business leaders to conduct high-quality research without relying on specialized platform experts.

Sprig Overview

Sprig approaches enterprise surveys from a different starting point.

Instead of asking, “How can we build the most configurable survey platform?” Sprig asks, “How can AI help organizations answer important questions faster?”

That shift influences nearly every part of the product.

Rather than treating AI as a feature layered onto existing workflows, Sprig uses AI throughout the research lifecycle to reduce manual work and help researchers spend more time interpreting insights instead of managing surveys.

Organizations use Sprig to support:

Customer surveys
Market research
Product research
Brand research
In-product feedback
Concept testing
Pricing research
Competitive research
User segmentation
Longitudinal tracking studies

The platform combines several capabilities that organizations have historically assembled from multiple vendors:

AI-assisted survey creation
Native email survey delivery
Patented in-product survey SDKs
Shareable links and QR codes
External participant recruitment
AI-powered analysis
Enterprise security and governance

Instead of exporting surveys between separate systems, research teams can complete the entire workflow within one platform.

AI-Native vs. AI-Enabled

One of the biggest differences between modern survey platforms is not whether they include AI, but how AI is integrated into the overall research experience.

Many established enterprise platforms have introduced AI capabilities in recent years, including question generation, text summarization, and reporting assistance. These features can deliver meaningful productivity improvements while preserving familiar research workflows.

Sprig takes a different architectural approach by designing the platform around AI-assisted workflows from the outset. Instead of introducing AI at a single point in the process, it assists researchers throughout the entire research lifecycle.

For example, researchers can begin with a research objective rather than a blank survey. AI helps:

Generate an initial survey draft
Recommend question wording
Suggest appropriate methodologies
Assist with revisions before launch
Identify recurring themes
Summarize qualitative feedback
Highlight statistically meaningful findings
Generate stakeholder-ready reports

The result is that AI becomes part of the entire research workflow rather than a feature used only after data collection. For organizations conducting dozens or hundreds of studies each year, this workflow-first approach can meaningfully reduce research cycle times while allowing researchers to focus more on methodology and decision-making.

From Point Solutions to Unified Survey Platforms

Historically, enterprise research often required stitching together multiple vendors.

A typical workflow might look like this:

Design the survey in one platform.
Purchase respondents from a panel provider.
Send customer surveys through an email marketing platform.
Collect responses.
Export data to another analytics tool.
Build reports manually in presentation software.

Each handoff introduces additional time, operational overhead, and opportunities for error.

Modern enterprise survey platforms increasingly aim to consolidate these workflows.

Sprig brings together survey design, participant recruitment, survey distribution, analysis, and reporting into a single system.

For example, researchers can:

Create a survey with AI assistance.
Recruit participants from an integrated marketplace of more than 5 million B2B and B2C respondents.
Target audiences using 300+ demographic, firmographic, geographic, behavioral, and professional attributes.
Launch customer surveys through native email delivery, without exporting to a separate campaign tool.
Collect responses from email, web links, QR codes, APIs, SMS, or patented in-product SDKs.
Analyze results using AI-generated summaries and insights.

Rather than managing multiple vendors and disconnected workflows, organizations can complete the end-to-end research process from a single platform.

A Different Definition of Speed

When evaluating survey platforms, buyers often ask which product is “faster.” That question can mean several different things. A platform may load quickly or offer a responsive interface, yet still require days of manual work before a survey reaches participants and insights are delivered.

A more meaningful metric is time to insight: how long it takes to move from an initial business question to a decision supported by reliable evidence. That journey typically includes:

Defining research objectives
Designing the questionnaire
Reviewing and refining questions
Recruiting participants
Launching the study
Monitoring fieldwork
Analyzing responses
Sharing results with stakeholders

Reducing friction at each stage of this workflow often has a greater impact than optimizing any single feature. For organizations conducting research continuously, shortening the time from question to insight enables teams to answer more questions, validate more ideas, and make decisions with greater confidence.

Choosing Between Breadth and Workflow Efficiency

Both Sprig and Qualtrics are capable of supporting enterprise-scale research, but they reflect different product philosophies.

Qualtrics emphasizes breadth, configurability, and a mature experience management ecosystem that has evolved over more than two decades. Sprig emphasizes workflow efficiency, AI-assisted research, and an integrated platform that brings survey design, participant recruitment, distribution, analysis, and reporting together in a single workflow.

Neither approach is inherently better for every organization. The right choice depends on your existing investments, operating model, and how you expect research to evolve over the coming years.

The next section examines one of the most important evaluation criteria for modern survey platforms: the survey creation experience, including questionnaire design, collaboration, AI-assisted authoring, templates, and the overall process of building enterprise-quality surveys.

Survey Creation: How Sprig and Qualtrics Compare

The quality of a survey platform is often judged by its analytics or reporting, but in practice, the survey creation experience has an outsized impact on the speed and quality of research.

Every decision made during authoring, from question wording and survey logic to collaboration and review, influences the quality of the data that follows. A poorly designed survey cannot be rescued by great analytics. Conversely, a well-designed survey makes every downstream step easier.

Both Sprig and Qualtrics provide enterprise-grade survey builders capable of supporting sophisticated research. The difference lies in how those surveys are created, how much manual work is required, and how AI assists throughout the process.

At a Glance

Both platforms are capable of producing sophisticated enterprise surveys. The primary distinction is that Qualtrics emphasizes flexibility and configuration, while Sprig emphasizes speed and AI-assisted authoring.

Starting With a Blank Page vs. Starting With an Objective

Traditionally, survey software begins with an empty questionnaire. Researchers are responsible for deciding every aspect of the study, including:

Which questions to ask
Which question types to use
Survey flow and branching logic
Randomization
Screening questions
Demographic questions

Experienced researchers can complete this process efficiently, but it still requires significant manual effort before the first survey is ready to launch.

Modern AI changes this workflow. Instead of asking researchers to begin with a blank survey, AI can start with the research objective itself. For example, a researcher might simply describe what they want to learn:

Measure customer satisfaction after onboarding.
Understand why enterprise prospects are choosing competitors.
Run a pricing study for a new B2B SaaS product.

Rather than constructing every question manually, researchers receive an initial survey draft that can then be reviewed, refined, and customized. This approach reduces one of the biggest sources of friction in survey research by making it much faster to move from a research objective to a launch-ready study.

AI Survey Generation

Artificial intelligence has become one of the most significant areas of innovation in enterprise survey platforms.

Both Sprig and Qualtrics have introduced AI capabilities, but they are integrated differently into the authoring experience.

Sprig

Sprig’s Design Agent is designed to help researchers create higher-quality surveys more efficiently. Rather than simply generating individual questions, AI assists throughout the survey authoring process by:

Drafting complete questionnaires from a research objective
Recommending appropriate research methodologies
Suggesting screening questions
Improving question wording
Identifying ambiguous or leading language
Detecting duplicate questions
Recommending logical survey flow
Suggesting answer choices
Reducing respondent burden
Drafting survey introductions and completion messages

Researchers remain responsible for methodology, study design, and interpretation, but AI significantly reduces the repetitive work involved in survey authoring. Rather than replacing research expertise, the Design Agent helps teams move from a research objective to a launch-ready survey more quickly while maintaining high methodological standards.

Qualtrics

Qualtrics also provides AI-assisted survey creation and question generation that can help reduce authoring time and improve consistency, particularly for common survey types.

Organizations already invested in Qualtrics can benefit from these capabilities without significantly changing existing workflows. Like many established enterprise platforms, Qualtrics has integrated AI into an already mature survey authoring experience, helping researchers automate parts of the design process while preserving the configurability and workflows that existing customers are familiar with.

Which Approach Produces Better Surveys?

One of the most common misconceptions about AI-generated surveys is that they replace research expertise. In reality, experienced researchers continue to make the decisions that determine the quality and validity of a study.

Researchers remain responsible for decisions such as:

Selecting the appropriate audience
Choosing the right methodology
Defining hypotheses
Identifying the most important tradeoffs
Determining how findings should influence business decisions

AI primarily accelerates execution rather than replacing judgment. It reduces the time spent on repetitive authoring tasks, such as drafting demographic questions or formatting answer choices, allowing researchers to focus on study design, measurement quality, and interpreting results.

For organizations conducting hundreds of studies each year, saving even one or two hours per survey can translate into substantial productivity gains while allowing research teams to spend more time on the work that requires human expertise.

Question Types

Enterprise research often requires much more than simple multiple-choice questions.

Both Sprig and Qualtrics support a broad range of question formats suitable for customer research, market research, employee research, and product feedback.

These include:

Multiple choice
Single select
Multi-select
Open-ended text
Long-form responses
Rating scales
Likert scales
Matrix questions
Ranking
Numeric inputs
Date and time
Dropdowns
Constant sum
File uploads
Image-based questions
Net Promoter Score (NPS)
Customer Satisfaction (CSAT)
Customer Effort Score (CES)

Organizations conducting advanced research generally won’t find themselves limited by either platform’s question library.

Instead, differences tend to emerge in how quickly those questions can be assembled into a high-quality study.

Survey Logic

Enterprise surveys rarely present every respondent with the same experience.

Researchers often need to:

Skip irrelevant questions
Screen participants
Randomize answer choices
Randomize blocks
Branch respondents
Pipe previous answers into future questions
Personalize surveys
End surveys early
Display follow-up questions conditionally

Both platforms support sophisticated survey logic.

Examples include:

Screening

Only allow qualified participants to continue.

Display Logic

Show questions only when relevant.

Branch Logic

Send respondents down different paths.

Embedded Variables

Personalize surveys using CRM or panel data.

Randomization

Reduce order bias.

Quotas

Control sample composition while fielding.

For organizations running advanced quantitative research, these capabilities are foundational rather than optional.

Templates

Most enterprise research follows recurring patterns, making reusable templates an important way to improve both efficiency and consistency across teams. Common examples include:

Customer satisfaction surveys
Product feedback
Brand awareness
Competitive research
Feature prioritization
Employee engagement
Event feedback
Pricing research
Concept testing
User segmentation

Templates reduce setup time while encouraging methodological consistency across the organization. Both Sprig and Qualtrics provide template libraries covering these common enterprise research use cases. AI further accelerates the process by adapting templates to each organization’s specific research objectives rather than requiring researchers to manually customize generic questionnaires.

Collaboration

Survey creation has become increasingly collaborative. Researchers rarely work in isolation and often partner with cross-functional stakeholders throughout the research process, including:

Product managers
Designers
Marketing teams
Customer Success
Executives
Analysts
Data scientists

Modern survey platforms therefore need to support more than questionnaire authoring. Teams also need tools that make it easy to:

Review drafts
Suggest edits
Share previews
Approve surveys
Manage versions
Reuse previous work

Both Sprig and Qualtrics support collaborative workflows suitable for enterprise teams. Sprig emphasizes a modern collaborative experience designed to reduce friction between research specialists and business stakeholders, making it easier for cross-functional teams to participate in the research process without extensive platform training.

Reusable Survey Assets

As organizations scale, maintaining consistency across research programs becomes increasingly important. Rather than recreating common survey components for every study, research teams often rely on reusable assets such as:

Brand-approved introductions
Demographic questions
Screening questions
Consent language
Methodology templates
Logic blocks
Closing messages

Centralized libraries reduce duplicated work while helping teams maintain consistent research standards across the organization. This becomes particularly valuable for enterprises operating across multiple business units, regions, or countries, where standardization helps ensure surveys follow the same methodological and governance practices.

The Researcher’s Experience Matters

One of the biggest changes in enterprise research over the past decade has been who creates surveys. Previously, research was largely conducted by centralized insights teams. Today, organizations increasingly expect product managers, marketers, customer success teams, UX designers, and business leaders to conduct research independently.

This shift places much greater importance on usability. A platform that requires extensive training may be appropriate for dedicated Research Operations teams, but it can slow adoption when research expands across the broader organization.

An intuitive interface combined with AI-assisted workflows enables more employees to conduct high-quality research while allowing centralized research teams to focus on methodology, governance, and more strategic initiatives.

Survey Creation Verdict

Both Sprig and Qualtrics provide enterprise-grade survey authoring capabilities capable of supporting sophisticated research programs.

Qualtrics offers one of the most mature and configurable survey builders available, making it well suited for organizations with established research operations and administrators who manage complex deployments.

Sprig takes a more AI-native approach, helping researchers move from an initial research objective to a complete, enterprise-ready survey with less manual effort. Rather than replacing methodological expertise, AI assists with drafting, refining, and organizing surveys so teams can spend more time designing effective studies and less time assembling questionnaires.

For organizations that prioritize faster research cycles, broader adoption across business teams, and AI-assisted workflows, Sprig offers a compelling modern authoring experience while still supporting the advanced logic and question types expected of an enterprise survey platform.

AI Capabilities: How Sprig and Qualtrics Compare

Artificial intelligence has become one of the most important criteria when evaluating enterprise survey platforms. Just a few years ago, AI features were largely limited to sentiment analysis or keyword extraction. Today, organizations expect AI to accelerate nearly every stage of the research lifecycle.

The question is no longer whether a platform includes AI. Nearly every major survey vendor now does.

The more useful question is:

How much of the research workflow does AI actually improve?

For enterprise research teams, AI should help answer questions like:

How quickly can we design a high-quality survey?
Can AI recommend the right methodology?
How much manual analysis can AI eliminate?
Can AI identify patterns we might otherwise miss?
How easily can executives understand the findings?
Can AI help researchers answer follow-up questions without starting over?

These capabilities have a direct impact on research velocity. Organizations that reduce repetitive work can run more studies, answer more business questions, and make better-informed decisions.

At a Glance

Both platforms continue to invest heavily in AI. The primary difference is how deeply AI is integrated into the research workflow.

AI Should Accelerate the Entire Research Lifecycle

Many people think about AI as something that happens after survey responses have been collected. In reality, AI has the potential to improve nearly every stage of the research lifecycle, from designing a study to communicating findings.

A complete workflow typically includes:

Defining the research objective
Choosing the appropriate methodology
Designing the survey
Recruiting participants
Launching the study
Monitoring fieldwork
Analyzing results
Identifying themes
Generating reports
Sharing insights
Answering follow-up questions

Each stage presents opportunities to reduce manual work, improve research quality, and accelerate decision-making. Rather than asking whether a platform includes AI, organizations should evaluate how much of the research lifecycle AI meaningfully improves.

AI Survey Design

Survey quality begins with good design. Poorly written questions can introduce bias, confuse respondents, and reduce the reliability of results. Experienced researchers understand these risks, but designing high-quality surveys still requires significant time and manual effort.

Sprig

Sprig’s Design Agent is designed to help researchers create better surveys more efficiently. Rather than generating isolated questions, it assists with the overall structure and quality of the study by:

Drafting complete questionnaires
Recommending screening questions
Suggesting demographic questions
Improving question clarity
Identifying leading or ambiguous language
Detecting duplicate questions
Recommending appropriate rating scales
Improving survey flow
Reducing respondent burden

Researchers remain responsible for selecting the appropriate methodology and interpreting the results, but AI significantly reduces the repetitive work involved in survey authoring. This allows teams to move from an initial research objective to a launch-ready survey more quickly while maintaining high methodological standards.

Qualtrics

Qualtrics also provides AI-assisted survey creation and question generation that can help reduce authoring time and improve consistency, particularly for common survey types. Organizations already invested in Qualtrics can benefit from these capabilities without significantly changing existing workflows.

Like many established enterprise platforms, Qualtrics has integrated AI into an already mature survey authoring experience. These capabilities can improve productivity while preserving the configurability and workflows that existing customers are familiar with.

AI Methodology Recommendations

One of the most valuable applications of AI in survey research is helping teams choose the right methodology. Business stakeholders often know the question they want answered but not the research method best suited to answering it.

For example, a product team might ask:

“I need to understand whether customers prefer Feature A or Feature B.”

Rather than defaulting to a simple survey, AI might recommend methodologies such as:

MaxDiff
Concept testing
Preference ranking

Similarly, a pricing team might ask:

“I need to understand customers’ willingness to pay.”

Instead of creating a basic rating question, AI could recommend:

Van Westendorp
Gabor-Granger
Conjoint analysis

By recommending methodologies that align with a team’s research objectives, AI helps organizations design more rigorous studies before a single response is collected. Experienced researchers remain responsible for validating the final approach, but AI can reduce the expertise required to get started while encouraging better research practices across the organization.

AI During Fieldwork

Historically, researchers waited until fieldwork was complete before analyzing results. Modern AI makes it possible to monitor studies as they are running, helping teams identify issues and emerging patterns before data collection is finished.

During fieldwork, AI can assist with tasks such as:

Detecting low-quality responses
Identifying emerging themes
Monitoring quota completion
Spotting unusual response patterns
Flagging incomplete data
Highlighting statistically significant trends as they develop

As enterprise research becomes increasingly continuous, these capabilities shorten the time between collecting data and acting on it, allowing researchers to make adjustments while studies are still in progress.

AI Analysis

For many organizations, AI analysis is where the greatest productivity gains become immediately apparent. Rather than manually reading hundreds or thousands of survey responses, researchers increasingly expect AI to perform the initial analysis, organize findings, and surface the insights that deserve further investigation.

Sprig

Sprig’s Synthesize Agent helps transform raw survey responses into actionable insights.

Examples include:

Theme Identification

One of the most valuable applications of AI is automatically identifying recurring themes across thousands of open-ended responses. Rather than reading every comment individually, researchers receive organized summaries that highlight the topics appearing most frequently.

Qualitative Summaries

AI also summarizes large volumes of qualitative feedback into concise, stakeholder-ready findings.

For example, instead of reading 3,000 onboarding responses, researchers might receive a summary such as:

Setup was generally straightforward.
Users struggled with permissions.
Pricing confusion appeared repeatedly.
Mobile onboarding satisfaction exceeded desktop.

Researchers can then drill into the supporting responses behind each finding to validate conclusions, understand the underlying context, and explore representative customer comments.

Sentiment Analysis

Identify positive, neutral, and negative feedback across open-ended responses.

Key Drivers

Help identify which issues appear most closely associated with satisfaction, dissatisfaction, or purchase intent.

Executive Reports

Instead of exporting data into presentation software, AI can generate stakeholder-ready summaries.

Executives often want:

Top findings
Biggest opportunities
Recommended actions
Supporting evidence

AI significantly reduces the time required to prepare these reports.

Qualtrics

Qualtrics also provides AI-powered analytics that help researchers interpret qualitative data more efficiently. Capabilities such as text analysis, summarization, and automated insights reduce the effort required to analyze large volumes of open-ended feedback and identify important patterns.

Organizations already using Qualtrics can incorporate these capabilities into their existing research workflows without changing platforms. As with survey creation, Qualtrics has expanded its AI capabilities significantly in recent years while preserving the reporting and analytics experience familiar to existing customers.

AI Should Support Exploration, Not Just Reporting

Traditional dashboards require researchers to anticipate stakeholder questions in advance by creating charts, filters, and reports before anyone asks them. Follow-up questions often require building new reports or modifying existing dashboards, slowing down the decision-making process.

Modern AI enables a more conversational approach. Instead of creating new reports, researchers and stakeholders can ask questions directly, such as:

What did enterprise customers think about onboarding?
How do responses from directors compare with managers?
Which themes appeared most often among detractors?
Did satisfaction improve after the pricing change?

Rather than navigating dashboards or manually building filters, users can interact with survey data using natural language. This makes it easier to explore findings, answer follow-up questions, and uncover new insights without continually recreating reports.

AI for Executives

Researchers are not the only audience for survey insights. Executives need to understand what happened, why it happened, and what actions the organization should take, but they rarely have time to review dozens of charts or detailed dashboards.

Instead, they increasingly expect concise, AI-generated summaries that include:

Key findings
Major trends
Risks
Opportunities
Supporting evidence

The most valuable executive summaries go beyond describing the data. They answer the questions leaders care about most:

What happened?
Why did it happen?
What should we do next?

By reducing the effort required to consume research, AI helps insights reach decision-makers more quickly and increases the likelihood that research influences strategic business decisions.

AI as a Research Partner

Perhaps the biggest shift in enterprise research is that AI is evolving from an automation tool into a research partner. Rather than simply generating outputs, AI increasingly helps researchers make better decisions throughout the research process.

Future workflows may include AI that:

Suggests additional questions while designing a study
Recommends increasing sample sizes for underrepresented audiences
Identifies missing demographic segments
Detects methodological risks before launch
Recommends follow-up studies
Compares results against previous research
Surfaces historical findings relevant to new projects

This represents a broader shift from AI performing individual tasks to AI collaborating with researchers throughout the entire research lifecycle. As these capabilities continue to evolve, the greatest value will come not from automating isolated activities, but from helping researchers design better studies, uncover deeper insights, and make more informed decisions.

AI Is Most Valuable When Combined With Native Distribution

One of the biggest limitations of standalone AI tools is that they operate outside the survey platform. Researchers often need to prepare data before AI can analyze it by:

Exporting survey questions
Copying responses
Uploading spreadsheets
Cleaning data manually
Recreating context for every analysis

Native AI eliminates much of this friction because it already understands the full context of the study, including:

Survey structure
Question logic
Response metadata
Audience characteristics
Distribution channels
Historical studies

This additional context allows AI to generate more relevant recommendations while reducing the manual work required to prepare data for analysis. For organizations conducting continuous research, keeping AI integrated within the survey platform streamlines the entire workflow, from survey design and participant recruitment through analysis, reporting, and decision-making.

Looking Beyond Today’s AI Features

Most buyers evaluate AI by comparing feature lists. That’s understandable, but it may not be the best way to assess a platform’s long-term potential.

AI capabilities are evolving rapidly across the industry, and many features that feel differentiated today are likely to become standard over the next several years. Rather than focusing exclusively on what a platform can do today, organizations should ask a more durable question:

Which platform’s architecture is best positioned to incorporate future AI capabilities?

The answer depends less on individual features and more on how AI is integrated into the product. Buyers should consider whether AI is primarily an add-on to existing workflows or a core part of how surveys are designed, recruited, fielded, analyzed, and shared.

As AI continues to reshape enterprise research, this architectural foundation is likely to have a greater impact on long-term innovation than any single AI feature available today.

AI Verdict

Both Sprig and Qualtrics are investing heavily in AI, and both platforms now offer meaningful capabilities that help organizations create surveys and analyze results more efficiently.

Qualtrics has expanded AI across its mature enterprise platform, giving existing customers new ways to automate parts of the research process while preserving familiar workflows.

Sprig’s approach is more deeply integrated into the end-to-end research lifecycle. AI assists with designing surveys, recommending methodologies, improving question quality, analyzing responses, generating executive summaries, and enabling conversational exploration of findings. Rather than treating AI as a standalone feature, Sprig positions it as a core part of how modern enterprise research is conducted.

For organizations that view AI as a strategic investment in research productivity, this workflow-centric approach can reduce manual effort across every stage of the research process while helping teams move from question to insight more quickly.

Survey Distribution & Participant Recruitment

Designing a survey is only the first step in the research process. Once a study is ready, organizations need to determine how they will reach the right audience, recruit qualified participants, distribute surveys across multiple channels, manage quotas during fieldwork, and do it all with as little operational overhead as possible.

For many enterprise research teams, fielding surveys requires just as much effort as designing them. Historically, organizations assembled multiple tools to complete this workflow. A typical research project often involved:

Building the survey in one platform
Purchasing respondents from a panel provider
Exporting survey links
Sending customer surveys through an email marketing platform
Monitoring quotas in a spreadsheet
Exporting data for analysis

Each additional system introduces more operational complexity, slows execution, and creates additional opportunities for errors.

Modern enterprise survey platforms increasingly consolidate these workflows into a single system, allowing research teams to move more efficiently from survey design to participant recruitment, fieldwork, and analysis. This integrated approach represents one of the biggest architectural differences between Sprig and Qualtrics.

At a Glance

| Category | Sprig | Qualtrics | |:-------------------------:|:-------------------------:|:---------------------------------:| | Native Email Delivery | ✅ | ✅ | | In-Product Surveys | ✅ | ✅ | | Web Links | ✅ | ✅ | | QR Codes | ✅ | ✅ | | SMS Distribution | ✅ | ✅ | | API Distribution | ✅ | ✅ | | External Research Panels | Native marketplace | Available | | B2B & B2C Recruitment | Yes | Yes | | Audience Targeting | 300+ targeting attributes | Extensive targeting options | | Participant Marketplace | 5M+ participants | Available through panel offerings | | Unified Fielding Workflow | Excellent | Good |

Both platforms support enterprise-scale survey distribution. The primary difference is how integrated participant recruitment and distribution are within the overall research workflow.

Native Email Delivery

Email remains one of the most important channels for enterprise survey research.

Organizations routinely send surveys to:

Customers
Prospects
Product users
Trial users
Event attendees
Partners
Employees
Advisory boards
Beta programs

For many companies, email is the primary method of collecting structured customer feedback.

Why Native Email Matters

Email remains one of the most important channels for enterprise survey research, but historically many organizations relied on separate email platforms to distribute surveys. That often required researchers to:

Export survey links
Build email campaigns in another system
Manage recipient lists separately
Track responses across multiple platforms

Each additional handoff introduces operational complexity and makes it more difficult to manage the research lifecycle from survey creation through response collection.

Native email delivery eliminates much of this friction by keeping survey creation and distribution within the same platform. Researchers can:

Build surveys
Create recipient lists
Schedule campaigns
Send invitations and reminders
Monitor response rates throughout fieldwork

Without switching tools, organizations gain a more streamlined workflow while improving visibility into survey delivery, response collection, and overall fieldwork performance.

Sprig

Sprig includes native enterprise email delivery as part of its survey platform, allowing researchers to manage the entire email workflow without exporting surveys into a separate campaign system.

Capabilities include:

Email invitations
Reminder campaigns
Scheduled sends
Branding
Response tracking
Survey lifecycle management

For organizations conducting frequent customer research, this integrated approach reduces operational complexity and shortens the time between designing a survey and collecting actionable feedback. Rather than treating survey creation and email distribution as separate projects, teams can manage the entire workflow within a single platform.

Qualtrics

Qualtrics also supports enterprise email distribution and has long been used for customer and employee survey programs. Organizations running large-scale experience management initiatives can distribute surveys through email alongside other collection channels as part of broader CX and EX workflows.

For enterprises with established Qualtrics deployments, email distribution integrates naturally with existing research and experience management processes, allowing teams to manage recurring survey programs within the broader Qualtrics ecosystem.

External Participant Recruitment

One of the biggest operational challenges in market research is finding qualified participants.

Unlike customer surveys, market research often requires people who are not already in your customer database.

Examples include:

CFOs at companies with more than 500 employees
Small business owners
Healthcare professionals
IT decision makers
Parents with children under five
Recent electric vehicle buyers
Software developers
HR leaders
College students
Financial advisors

Finding these audiences manually can take days or weeks.

Traditional Panel Procurement

Historically, recruiting participants for market research involved coordinating with one or more external panel vendors. A typical workflow looked something like this:

Finish the survey.
Contact one or more panel vendors.
Request feasibility estimates.
Negotiate pricing.
Complete procurement.
Launch fieldwork.
Monitor quotas.
Adjust targeting if necessary.

While this process can be effective, it also introduces significant operational overhead. In many organizations, research timelines are constrained less by survey design than by the time required to recruit participants and coordinate multiple vendors.

Native Panel Recruitment

Modern enterprise survey platforms increasingly integrate participant recruitment directly into the research workflow. Instead of coordinating with separate panel vendors after a survey is complete, researchers can define their target audience and recruit participants as part of the survey creation process.

This integrated approach dramatically reduces the time between completing a survey and launching fieldwork while eliminating much of the operational overhead associated with traditional panel procurement.

Sprig

Sprig includes a native participant marketplace directly within the survey platform, allowing researchers to recruit from more than 5 million verified B2B and B2C participants without leaving the research workflow.

Rather than coordinating with separate panel vendors, researchers define their target audience as part of survey setup using more than 300 demographic, firmographic, geographic, behavioral, and professional targeting attributes.

These targeting options include categories such as:

Demographics

Examples include:

Age
Gender
Income
Education
Household composition
Geography
Language

Professional Attributes

For B2B research:

Job title
Seniority
Department
Industry
Company size
Revenue
Decision-making authority

This allows organizations to recruit highly specific business audiences.

For example:

VP Product at SaaS companies
IT Directors at healthcare organizations
HR leaders at Fortune 1000 companies
Finance executives at mid-market businesses

Behavioral Targeting

Researchers can also target based on behaviors such as:

Recent purchases
Product usage
Technology adoption
Subscription history
Professional responsibilities
Consumer interests

Geographic Targeting

Organizations can recruit participants across:

Countries
States
Cities
Regions

This supports both global and localized market research initiatives.

External Participant Recruitment

One of the biggest operational challenges in market research is finding qualified participants.

Unlike customer surveys, market research often requires people who are not already in your customer database.

Examples include:

CFOs at companies with more than 500 employees
Small business owners
Healthcare professionals
IT decision makers
Parents with children under five
Recent electric vehicle buyers
Software developers
HR leaders
College students
Financial advisors

Finding these audiences manually can take days or weeks.

Traditional Panel Procurement

Historically, recruiting participants for market research involved coordinating with one or more external panel vendors. A typical workflow looked something like this:

Finish the survey.
Contact one or more panel vendors.
Request feasibility estimates.
Negotiate pricing.
Complete procurement.
Launch fieldwork.
Monitor quotas.
Adjust targeting if necessary.

Native Panel Recruitment

Sprig

These targeting options include categories such as:

Demographics

Examples include:

Age
Gender
Income
Education
Household composition
Geography
Language

Professional Attributes

For B2B research:

Job title
Seniority
Department
Industry
Company size
Revenue
Decision-making authority

This allows organizations to recruit highly specific business audiences.

For example:

VP Product at SaaS companies
IT Directors at healthcare organizations
HR leaders at Fortune 1000 companies
Finance executives at mid-market businesses

Behavioral Targeting

Researchers can also target based on behaviors such as:

Recent purchases
Product usage
Technology adoption
Subscription history
Professional responsibilities
Consumer interests

Why Integration Matters

The primary advantage of integrated participant recruitment isn’t simply access to more respondents. It’s the elimination of workflow fragmentation.

In a traditional research process, teams often move between multiple systems to complete a single study:

Design the survey in one platform.
Source participants from a panel vendor.
Complete procurement or vendor coordination.
Manage email communications separately.
Track quotas throughout fieldwork.

Each handoff introduces additional operational overhead and increases the time required to launch research.

An integrated survey platform streamlines this process by allowing researchers to:

Design the survey
Define the target audience
Review feasibility
Launch participant recruitment
Monitor quotas during fieldwork
Analyze results

All of these steps can be completed without switching platforms, significantly reducing operational complexity and helping organizations launch research faster, particularly when running continuous research programs.

Qualtrics

Qualtrics also supports participant recruitment through its panel capabilities and broader research ecosystem. Organizations conducting market research can source B2B and B2C respondents and field sophisticated studies across a wide range of audiences.

For enterprises with established research operations, these capabilities provide the flexibility to support diverse study types while integrating with existing research processes and workflows.

Organizations can recruit participants across:

Countries
States
Cities
Regions

This supports both global and localized market research initiatives.

In-Product Surveys

Not every survey should arrive by email. Many organizations want to collect feedback while customers are actively using a product, allowing them to capture reactions at the moment an experience occurs.

Common examples include:

After onboarding
Following checkout
Immediately after feature adoption
After customer support interactions
Following account upgrades
During product trials

Collecting feedback in context often improves response quality, increases response rates, and reduces recall bias because customers are describing experiences while they are still fresh.

Both Sprig and Qualtrics support in-product survey experiences. Sprig’s patented SDK infrastructure enables organizations to trigger surveys across web and mobile applications based on user behavior, allowing teams to collect feedback at the moments that are most relevant to the customer experience.

Additional Distribution Channels

Enterprise organizations rarely rely on a single collection method.

Both platforms support multiple ways to distribute surveys, including:

Shareable Links

Useful for:

Communities
Events
Social media
Customer portals

QR Codes

Common for:

Retail
Hospitality
Healthcare
Physical events
Printed materials

SMS

Useful when organizations need:

High response rates
Mobile-first experiences
Time-sensitive feedback

APIs

Developers can automate survey workflows by integrating with enterprise systems.

Examples include:

CRM platforms
Product analytics
Marketing automation
Customer support
Internal applications

Distribution Is About Speed

Many buyers compare survey distribution features one by one, but a more useful question is how quickly a platform enables teams to launch high-quality research. Individual capabilities matter, but so does the workflow that connects them.

Research inevitably slows down when:

Participant recruitment requires coordinating multiple vendors.
Email campaigns must be managed in a separate platform.
Quota management happens outside the survey tool.

Every additional handoff introduces more operational overhead and increases the time between asking a business question and receiving actionable insights. Platforms that integrate these workflows reduce friction, simplify research operations, and enable teams to move from survey design to decision-making much more quickly.

Survey Distribution Verdict

Both Sprig and Qualtrics provide enterprise-grade survey distribution capabilities, supporting email, web, mobile, APIs, QR codes, SMS, and sophisticated enterprise research programs.

The primary difference is workflow integration.

Qualtrics offers mature distribution capabilities within its broader experience management ecosystem and supports complex enterprise deployments.

Sprig places greater emphasis on bringing the entire fielding process into a single platform. Organizations can design surveys, distribute them through native email delivery, recruit respondents from a marketplace of more than 5 million B2B and B2C participants, target audiences using 300+ demographic, firmographic, geographic, behavioral, and professional attributes, monitor fieldwork, and move directly into AI-powered analysis without coordinating multiple systems.

For organizations conducting frequent customer or market research, this integrated approach can reduce operational overhead, shorten research timelines, and simplify how studies are executed from start to finish.

Advanced Research Methods

For many organizations, evaluating an enterprise survey platform is about more than creating surveys. The more important question is whether the platform can support the research methodologies teams rely on to make high-stakes business decisions.

While standard surveys can measure customer satisfaction or collect feedback, advanced quantitative methods help organizations answer strategic questions such as:

Which product should we build next?
Which pricing strategy will maximize revenue?
Which features matter most to customers?
How much are customers willing to pay?
Which concept performs best?
How should we prioritize investments?
Which market opportunity should we pursue?

The answers to these questions often shape product strategy, pricing, marketing, and executive decision-making. As a result, organizations evaluating enterprise survey platforms typically place significant weight on advanced research methodologies and the workflows required to execute them efficiently.

At a Glance

| Methodology | Sprig | Qualtrics | |:--------------------:|:-----:|:---------:| | Conjoint Analysis | ✅ | ✅ | | MaxDiff | ✅ | ✅ | | Van Westendorp | ✅ | ✅ | | Gabor-Granger | ✅ | ✅ | | Randomization | ✅ | ✅ | | Quotas | ✅ | ✅ | | Advanced Branching | ✅ | ✅ | | Skip Logic | ✅ | ✅ | | Embedded Variables | ✅ | ✅ | | Multilingual Studies | ✅ | ✅ |

Both platforms support sophisticated enterprise research methodologies. The difference lies less in whether they can execute these studies and more in how quickly researchers can design, launch, and analyze them using modern AI-assisted workflows.

Enterprise Research Has Changed

Historically, advanced quantitative methodologies were reserved for specialized research teams because they often required significant expertise and manual effort. Running a conjoint study, for example, typically involved:

Dedicated research software
Statistical expertise
Significant manual setup
External consultants
Custom analysis

As a result, many organizations conducted these studies only when the business impact justified the time, cost, and complexity.

Today, AI is lowering many of these barriers by helping researchers:

Recommend appropriate methodologies
Configure studies
Generate survey structures
Analyze results
Explain findings to stakeholders

Rather than replacing methodological rigor, AI makes advanced research more accessible by reducing the operational complexity required to design, execute, and interpret sophisticated studies.

Conjoint Analysis

Conjoint analysis is one of the most widely used methodologies for product strategy and pricing research. Rather than asking customers which feature they prefer in isolation, conjoint measures the tradeoffs people actually make when evaluating realistic product configurations.

Organizations commonly use conjoint studies to answer questions such as:

Which features drive purchasing decisions?
Which product bundle should we launch?
Which combination of capabilities maximizes demand?
Which attributes justify premium pricing?
Which customer segments value different features?

Because respondents evaluate realistic product combinations rather than isolated questions, conjoint analysis often provides more reliable guidance for product strategy, pricing, and packaging decisions.

Sprig

Sprig supports conjoint analysis as part of its enterprise survey platform. Researchers can design sophisticated conjoint studies while benefiting from AI-assisted survey creation, integrated participant recruitment, native email delivery, and AI-powered analysis.

Rather than assembling multiple vendors to complete the workflow, organizations can:

Design the study
Recruit qualified participants
Launch fieldwork
Analyze responses
Share executive-ready findings

All of these steps can be completed within a single platform. This integrated workflow reduces operational complexity while making advanced product and pricing research more accessible across the organization.

Qualtrics

Qualtrics also supports conjoint analysis and has long been used by enterprise research teams conducting sophisticated market research.

Organizations with established research operations can build complex conjoint studies and integrate them into broader research programs.

MaxDiff Analysis

When organizations need to prioritize features, messaging, or product investments, simple rating scales often produce inflated results because customers tend to rate nearly every option as “important.”

MaxDiff, or Maximum Difference Scaling, addresses this challenge by asking respondents to repeatedly identify the most and least important options within a set. This forces meaningful tradeoffs, producing clearer prioritization and stronger differentiation between competing ideas.

Organizations commonly use MaxDiff for:

Feature prioritization
Product roadmap planning
Messaging evaluation
Brand positioning
Marketing claims
Customer value propositions

Both Sprig and Qualtrics support MaxDiff studies suitable for enterprise research.

Van Westendorp Price Sensitivity Analysis

Pricing is one of the most consequential decisions an organization makes. Even small pricing changes can have a significant impact on:

Revenue
Conversion
Market share
Profitability

The Van Westendorp Price Sensitivity Meter helps researchers understand how customers perceive pricing by asking four complementary pricing questions. Rather than measuring willingness to pay directly, it identifies acceptable pricing ranges and price perception thresholds that can inform pricing strategy.

Organizations commonly use Van Westendorp for:

New product launches
Pricing optimization
Packaging decisions
Market expansion
Competitive pricing research

Both Sprig and Qualtrics support Van Westendorp studies as part of their enterprise market research capabilities.

Gabor-Granger Pricing Research

While Van Westendorp estimates acceptable pricing ranges, Gabor-Granger measures purchase intent at specific price points. Researchers present respondents with a series of prices and measure how demand changes at each level, helping organizations understand the relationship between price and purchase likelihood.

This methodology is commonly used to estimate:

Revenue potential
Demand curves
Pricing elasticity
Optimal price points

Many pricing teams use Van Westendorp and Gabor-Granger together because the methodologies answer different questions and provide complementary perspectives on pricing strategy.

Quotas

Representative samples are fundamental to reliable research. Without quotas, organizations risk collecting disproportionate responses from certain audiences, introducing bias into the results.

Researchers commonly establish quotas based on:

Geography
Gender
Age
Company size
Industry
Job role
Customer segment
Subscription tier
Product usage
Existing customer status

Both Sprig and Qualtrics support sophisticated quota management. These capabilities become particularly valuable when recruiting participants from external panels or conducting global research, where maintaining representative samples is essential.

Randomization

Question order can influence survey responses. For example, respondents often select options appearing earlier in a list more frequently than those appearing later, introducing unintended bias into the results.

Randomization helps reduce these effects through techniques such as:

Answer Choice Randomization

Prevent systematic ordering effects.

Question Randomization

Reduce fatigue and sequencing bias.

Block Randomization

Present major survey sections in different orders.

Concept Rotation

Balance exposure across experimental conditions.

Both platforms provide the randomization capabilities expected for enterprise-grade research.

Advanced Logic

Sophisticated research often requires more than simple skip logic.

Organizations may need to personalize studies using:

Previous answers
CRM attributes
Panel metadata
Purchase history
Geography
Customer segment
Product ownership

Both Sprig and Qualtrics support advanced survey logic that allows researchers to tailor experiences to individual respondents while maintaining methodological consistency.

AI Makes Advanced Research More Accessible

Historically, advanced quantitative methods were often confined to dedicated market research teams because selecting the right methodology required specialized expertise. Business stakeholders frequently knew the question they wanted answered but not the most appropriate way to answer it.

For example, a product leader might ask:

“We need to prioritize next year’s roadmap.”

A researcher might consider several methodologies, including:

MaxDiff
Feature ranking
Conjoint analysis
Preference testing

Similarly, a pricing team might ask:

“We’re launching a new pricing model.”

Depending on the research objective, the most appropriate methodology might be:

Van Westendorp
Gabor-Granger
Conjoint analysis
A/B testing

Modern AI can increasingly recommend appropriate methodologies based on a team’s research objectives, reducing the expertise required to initiate sophisticated studies while allowing experienced researchers to validate, refine, and oversee the final design.

Beyond Methodologies: End-to-End Execution

Supporting a methodology is only part of the equation.

Organizations also need to execute studies efficiently.

An advanced pricing study typically requires:

Designing the survey
Recruiting qualified respondents
Managing quotas
Monitoring fieldwork
Analyzing results
Communicating recommendations

Sprig’s integrated workflow allows organizations to complete these steps within a single platform by combining AI-assisted survey design, native participant recruitment from more than 5 million B2B and B2C respondents, 300+ audience targeting attributes, native email delivery, quota management, and AI-generated reporting.

For organizations running recurring market research, reducing operational complexity can have as much impact on productivity as the methodology itself.

Advanced Research Verdict

Both Sprig and Qualtrics provide the advanced methodologies expected of modern enterprise research platforms, including conjoint analysis, MaxDiff, Van Westendorp, Gabor-Granger, sophisticated branching logic, randomization, quotas, and support for large-scale quantitative studies.

Qualtrics has earned its reputation as a powerful platform for complex market research and remains a strong choice for organizations with mature research operations.

Sprig combines these same enterprise methodologies with AI-assisted study design, integrated participant recruitment, native email distribution, and AI-powered analysis, enabling organizations to complete advanced research within a unified workflow. Rather than treating advanced methods as isolated statistical tools, Sprig integrates them into a platform designed to accelerate the entire research lifecycle from study design through executive decision-making.

Analysis, Reporting & Turning Responses into Decisions

Collecting survey responses is only the beginning of the research process. The ultimate goal is to help organizations make better decisions. As enterprise teams collect more feedback than ever before, the challenge has shifted from gathering data to interpreting it quickly enough to influence the business.

Today, research teams routinely collect tens of thousands of responses each month across customer satisfaction programs, product research, pricing studies, employee engagement, and market research. The real challenge is understanding what those responses mean and translating them into clear, actionable recommendations.

Researchers need to answer questions like:

What changed?
Why did it change?
Which customer segments are affected?
How confident are we in these findings?
What should we do next?

Historically, answering these questions required significant manual effort. Researchers exported spreadsheets, built pivot tables and dashboards, wrote summaries, and assembled presentation decks before stakeholders could act on the results.

Modern AI is changing that workflow. Rather than spending days organizing and interpreting data, researchers increasingly expect their survey platform to identify patterns, explain findings, and generate executive-ready insights automatically. This shift from reporting data to accelerating decision-making is one of the most important areas of innovation in enterprise survey software.

At a Glance

| Category | Sprig | Qualtrics | |:------------------------:|:-----:|:---------:| | Real-Time Dashboards | ✅ | ✅ | | Cross-Tab Analysis | ✅ | ✅ | | Filtering & Segmentation | ✅ | ✅ | | AI Theme Extraction | ✅ | ✅ | | AI Response Summaries | ✅ | ✅ | | Sentiment Analysis | ✅ | ✅ | | Executive Reports | ✅ | ✅ | | Conversational Analytics | ✅ | Available | | Export to CSV & Excel | ✅ | ✅ | | API Access | ✅ | ✅ |

Both platforms provide robust analytics suitable for enterprise research. The primary difference is how much of the interpretation and reporting process is automated with AI.

From Data Collection to Decision Support

A survey platform should do more than display charts. Its primary purpose is to help organizations answer important business questions and make better decisions.

A VP of Product, for example, rarely wants to know that Question 12 received an average score of 4.3 out of 5. Instead, they want answers to questions like:

Which features are frustrating enterprise customers?
Why are onboarding scores declining?
Which customer segment should we prioritize?
Which pricing changes create the greatest risk?
What should our team do next quarter?

These are interpretation problems rather than reporting problems. The more effectively a platform helps researchers turn survey responses into actionable recommendations, the more valuable it becomes to the broader organization.

Real-Time Dashboards

Both Sprig and Qualtrics provide dashboards that allow researchers to monitor studies as responses are collected.

Researchers can typically track:

Response counts
Completion rates
Drop-off points
Demographic distributions
Quota progress
Survey duration
Satisfaction metrics
Trend lines

Real-time dashboards become particularly valuable during active fieldwork because researchers can identify issues before studies conclude.

For example:

A quota may be filling more slowly than expected.
One audience may be overrepresented.
A question may have unusually high abandonment.
Survey completion time may exceed expectations.

Detecting these issues early helps organizations avoid costly delays.

Cross-Tabulation

Cross-tab analysis remains one of the most important tools in quantitative research because averages across an entire sample rarely tell the full story. Instead, researchers compare results across different customer segments to identify meaningful differences that might otherwise remain hidden.

Common comparisons include:

Enterprise versus SMB customers
New customers versus long-term customers
Managers versus executives
United States versus Europe
Free users versus paid customers

Cross-tabs help researchers uncover patterns that would be obscured in aggregate results, enabling more targeted product decisions, customer strategies, and business recommendations. Both Sprig and Qualtrics support the segmentation and cross-tab analysis capabilities expected of enterprise research platforms.

Filtering & Segmentation

Organizations increasingly collect rich customer metadata alongside survey responses.

Researchers may want to filter by:

Geography
Industry
Company size
Revenue
Product plan
Customer tenure
Subscription level
Job function
Department
Usage behavior

Being able to move quickly between these segments is essential for identifying meaningful differences.

Both platforms support sophisticated filtering that allows researchers to explore results across different audiences.

Open-Ended Responses

For many research teams, qualitative feedback is the most valuable part of a survey because it explains the “why” behind quantitative scores. The challenge is that it’s also one of the most time-consuming types of data to analyze.

A single enterprise survey can generate:

500 open-ended responses.
5,000 open-ended responses.
50,000 or more open-ended responses.

Reading every comment manually quickly becomes impractical. This is where AI has had one of the greatest impacts on enterprise research, helping teams identify recurring themes, summarize qualitative feedback, and surface the insights that matter most in a fraction of the time.

AI Theme Extraction

One of the most valuable applications of AI is identifying recurring themes across thousands of open-ended responses. Rather than reading every comment individually, researchers receive organized summaries that highlight the topics appearing most frequently.

For example, AI might identify themes such as:

Customers struggle during onboarding.
Reporting is difficult to customize.
Pricing is viewed positively.
Mobile performance has improved.
Integration setup is confusing.

Researchers can then drill into the supporting responses behind each theme to validate findings, understand the underlying context, and explore representative customer comments. This dramatically reduces analysis time while making it easier to identify patterns that might otherwise be overlooked.

Sprig

Sprig’s Synthesize Agent is designed to transform large volumes of qualitative feedback into structured insights.

Instead of simply summarizing text, it helps researchers understand:

What customers are saying
How frequently issues occur
Which themes matter most
Which customer segments are affected
How findings connect to research objectives

Researchers can move seamlessly from high-level summaries to the underlying verbatim responses, preserving transparency while accelerating interpretation.

Qualtrics

Qualtrics also provides AI-powered text analysis that helps researchers categorize qualitative responses, identify themes, and summarize large datasets.

These capabilities significantly reduce the effort required to process open-ended feedback compared with traditional manual coding approaches.

Sentiment Analysis

Not every open-ended response carries the same emotional weight.

Sentiment analysis helps researchers understand:

Positive reactions
Neutral observations
Negative experiences

Organizations often use sentiment analysis to monitor:

Product launches
Customer satisfaction
Brand perception
Support experiences
Employee engagement

Both platforms provide sentiment analysis that can be incorporated into broader reporting workflows.

AI Executive Summaries

One of the largest bottlenecks in enterprise research is communicating findings. Researchers often spend as much time preparing presentations, executive readouts, and stakeholder updates as they do conducting the research itself. While analyzing the data is valuable, translating those insights into a format that executives can quickly understand is often one of the most time-consuming parts of the process.

AI increasingly automates much of this work. Rather than starting with a blank presentation, researchers can receive a structured summary that highlights:

Key findings
Major themes
Risks
Opportunities
Supporting evidence

This allows research teams to spend less time formatting slides and writing summaries, and more time validating conclusions, discussing implications, and helping stakeholders make informed decisions.

From Dashboards to Conversations

Traditional analytics platforms require researchers to anticipate stakeholder questions by building reports, dashboards, and filters before anyone asks them. Modern AI enables a much more flexible workflow.

Instead of creating dozens of dashboards in anticipation of future questions, stakeholders can simply ask questions like:

What did enterprise customers think about the onboarding experience?
How do responses from healthcare customers compare with financial services customers?
What changed after the pricing update?
Which issues appear most frequently among customers with low NPS?

This conversational approach reduces the need to continually create new reports while making research more accessible to executives, product managers, and other non-technical stakeholders.

Historical Research Becomes More Valuable

One limitation of traditional reporting is that every survey is often analyzed in isolation. Organizations may have years of customer research spread across presentations, spreadsheets, PDFs, and previous survey projects, making it difficult to connect new findings with historical insights.

Modern AI makes it increasingly possible to ask questions across an organization’s entire body of research, such as:

Has this issue appeared before?
Did customer satisfaction improve after last year’s redesign?
How does this pricing study compare with previous research?
Which customer complaints have become more common over time?

As organizations accumulate years of research, the ability to analyze historical studies alongside current results becomes increasingly valuable. Rather than treating each survey as a standalone project, future enterprise survey platforms will increasingly help teams build on previous research, uncover long-term trends, and make better-informed decisions over time.

Research Should Lead to Action

The ultimate purpose of analytics is not simply to describe data. It is to help organizations make better decisions.

The strongest research platforms help teams move efficiently from:

Raw responses
Identifying patterns
Generating insights
Making recommendations
Driving business decisions

The faster organizations can move through this process, the greater the impact research can have across the business. AI plays an important role by reducing the manual effort required at every stage, allowing researchers to spend less time organizing data and more time helping stakeholders take action.

Sharing Results Across the Organization

Research only creates value when insights reach the people responsible for making decisions. Enterprise organizations often need to share findings across a wide range of stakeholders, including:

Product teams
Marketing
Customer Success
Executive leadership
Sales
Operations
Design
Data science

Each audience needs a different level of detail. Executives may want a one-page summary of key findings, researchers often need access to the underlying data and individual responses, while product managers may only be interested in feedback related to a specific feature or customer segment.

Both Sprig and Qualtrics provide flexible reporting capabilities that allow organizations to tailor research outputs to different audiences, making it easier for teams across the business to access the insights most relevant to their decisions.

Analysis Is Becoming Continuous

Historically, research followed a predictable cadence. Teams would conduct a survey, analyze the results, present their findings, and then archive the project until the next research initiative.

Today, many organizations operate continuous research programs instead. Customer feedback arrives every day, products evolve every week, and market conditions change constantly. Rather than treating research as a series of isolated projects, organizations increasingly need platforms that support ongoing analysis and continuous learning.

This shift means analytics platforms must do more than generate one-time reports. They need to make it easy to process new responses, identify emerging trends, and answer follow-up questions as new data arrives. AI makes continuous research significantly more practical by reducing the manual effort required to analyze each new wave of responses, allowing teams to keep pace with an increasingly fast-moving business environment.

Analysis & Reporting Verdict

Both Sprig and Qualtrics provide enterprise-grade analytics capable of supporting sophisticated quantitative and qualitative research.

Qualtrics offers mature dashboards, segmentation, text analytics, and reporting tools that have supported enterprise research programs for many years.

Sprig combines these foundational capabilities with AI-native workflows that emphasize rapid synthesis and decision support. Rather than stopping at charts and dashboards, the platform is designed to help researchers identify themes, generate executive-ready summaries, answer follow-up questions conversationally, and reduce the time between collecting responses and taking action.

For organizations that view research as a continuous decision-making process rather than a periodic reporting exercise, this AI-centric approach can help teams spend less time producing reports and more time driving business outcomes.

Enterprise Security, Governance & Administration

For most enterprise software purchases, security and governance become the deciding factor long before feature comparisons.

A platform may have the best survey builder or the most advanced AI capabilities, but if it cannot satisfy security, compliance, and administrative requirements, it is unlikely to be approved for enterprise deployment.

Large organizations need confidence that customer data is protected, user access is controlled, regulatory requirements are met, and research programs can scale across hundreds or thousands of employees.

As a result, enterprise buyers typically evaluate three separate areas:

Security and compliance
Governance and administration
Scalability and operational management

Both Sprig and Qualtrics are designed to support enterprise organizations and provide the controls expected by security, IT, and procurement teams.

At a Glance

| Category | Sprig | Qualtrics | |:-----------------------:|:-----:|:---------:| | Enterprise Security | ✅ | ✅ | | Single Sign-On (SSO) | ✅ | ✅ | | SCIM User Provisioning | ✅ | ✅ | | Role-Based Permissions | ✅ | ✅ | | Audit Logs | ✅ | ✅ | | Data Encryption | ✅ | ✅ | | Enterprise APIs | ✅ | ✅ | | Administrative Controls | ✅ | ✅ | | Organization Management | ✅ | ✅ | | Enterprise Scalability | ✅ | ✅ |

Both platforms provide the governance capabilities required for enterprise deployments.

The primary differences generally relate to overall platform architecture, administration experience, and how organizations adopt the platform across different business teams.

Security Expectations Have Changed

Enterprise research increasingly involves sensitive information, making security and governance essential requirements rather than optional features. Organizations routinely collect data such as:

Customer feedback
Product strategy
Pricing research
Competitive intelligence
Employee feedback
Healthcare information
Financial information
Personally identifiable information (PII)

Protecting this data is fundamental to maintaining customer trust and meeting regulatory obligations. Enterprise security therefore extends well beyond preventing unauthorized access. Organizations also need confidence that:

Data is encrypted.
Access is appropriately restricted.
Administrative actions are traceable through audit logs.
Identity systems integrate with existing corporate infrastructure.
Compliance requirements can be met across multiple regions and regulatory frameworks.

Identity & Access Management

Large organizations rarely manage user accounts manually. Instead, they integrate enterprise applications with centralized identity providers to simplify user management and enforce consistent security policies across their software stack.

Both Sprig and Qualtrics support enterprise identity and access management capabilities, including:

Single Sign-On (SSO)
Centralized identity management
Role-based access controls
Organization-wide user administration

These capabilities streamline onboarding, simplify ongoing user management, and help organizations maintain consistent authentication and access policies across enterprise applications.

SCIM Provisioning

As organizations grow, manually creating, updating, and removing user accounts becomes increasingly difficult. System for Cross-domain Identity Management (SCIM) automates this process by synchronizing user accounts with an organization’s identity provider.

For example, when a new employee joins the company, SCIM can automatically:

Create their account
Assign the appropriate permissions
Grant access based on organizational policies

Similarly, when an employee leaves the organization or changes roles, access can be updated or removed automatically. This reduces administrative overhead, improves security, and helps ensure user access remains aligned with corporate identity systems.

Both Sprig and Qualtrics support SCIM provisioning as part of their enterprise identity management capabilities.

Role-Based Permissions

Not every employee should have the same level of access to an enterprise survey platform. As research expands beyond centralized insights teams, organizations need to ensure employees can access the information and capabilities relevant to their role without exposing sensitive data unnecessarily.

For example:

Researchers may need to create and manage studies.
Executives may only need access to dashboards and reports.
Administrators may manage organization settings and user permissions.
Customer Success teams may only need access to customer feedback.
Marketing teams may only need access to campaign surveys.

Role-based permissions help organizations control access to capabilities such as:

Survey creation
Editing
Publishing
Administration
Reporting
Data exports
User management

These controls become increasingly important as research programs scale across multiple departments, enabling organizations to balance broad access with enterprise governance and security.

Administrative Controls

Enterprise deployments often involve:

Multiple business units
Global teams
Regional administrators
Shared survey libraries
Standardized templates
Central governance

Administrative controls help organizations maintain consistency while allowing different teams to operate independently where appropriate.

Examples include:

Workspace management
Organizational settings
User administration
Shared assets
Permission inheritance
Team management

Both platforms provide administrative capabilities designed for enterprise environments.

Auditability

Research frequently informs high-impact business decisions, making transparency and accountability essential. Organizations need visibility into how studies evolve over time and who has made changes throughout the research lifecycle.

Administrative audit capabilities help answer questions such as:

Who modified the survey?
When was it published?
Which administrator changed permissions?
Who exported respondent data?
When was access granted?

Comprehensive audit trails improve accountability, support internal governance requirements, and provide the transparency organizations need for enterprise-scale research programs.

Enterprise APIs

Research rarely exists in isolation. Enterprise organizations often integrate survey platforms with the rest of their technology stack, including:

CRM systems
Product analytics platforms
Customer support systems
Marketing automation platforms
Business intelligence tools
Data warehouses
Internal applications

Enterprise APIs make these integrations possible, allowing survey data to become part of broader business workflows rather than remaining isolated within a single platform. Both Sprig and Qualtrics provide APIs that support enterprise integrations and automation.

Scaling Across the Organization

One of the biggest operational challenges isn’t supporting a single research team. It’s enabling dozens, or even hundreds, of teams to conduct high-quality research while maintaining consistent governance and administrative oversight.

As survey programs expand across the organization, enterprise leaders increasingly ask questions such as:

Can new teams onboard quickly?
Can governance remain centralized?
Can business users create surveys without compromising quality?
Can researchers maintain methodological consistency?
Can administrators manage growth without becoming a bottleneck?

The answers to these questions often determine whether a survey platform can scale successfully across the organization over the long term.

Governance Without Slowing Research

Historically, many organizations centralized research because survey platforms required specialized expertise. Surveys often passed through a dedicated Research Operations team, templates required administrator approval, and new users needed extensive training before they could launch studies. While this approach improved consistency, it also slowed the pace of research.

Modern enterprise survey platforms increasingly aim to balance governance with autonomy. Researchers continue to define standards and best practices, while business teams gain the independence to conduct high-quality research on their own.

AI plays an important role in this shift by helping users create better surveys from the start, while governance features ensure methodological consistency, security, and compliance across the organization. The result is a model that enables research to scale across more teams without sacrificing quality or increasing administrative overhead.

Enterprise Scale

Both Sprig and Qualtrics are designed to support large enterprise organizations operating across multiple teams, products, and geographies.

Enterprise buyers should evaluate factors such as:

Organizational complexity
Number of business units
Number of administrators
Global deployment requirements
Security policies
Integration requirements
Research maturity

Rather than asking whether a platform can support enterprise scale, organizations should evaluate how efficiently that scale can be managed over time.

Research Data Is Becoming Strategic

Five years ago, survey data often lived within individual projects and was primarily used to answer a single research question. Today, organizations increasingly view customer research as a strategic asset that informs decisions across the business.

Survey responses now contribute to areas such as:

Product strategy
Pricing decisions
Customer experience
AI model evaluation
Competitive intelligence
Executive planning

As research becomes more central to business decision-making, governance becomes increasingly important. Organizations need confidence that research assets remain secure, discoverable, reusable, and accessible to the right people, ensuring valuable customer insights continue to create value long after an individual study is complete.

AI Introduces New Governance Considerations

The rise of AI has introduced a new set of governance questions for enterprise buyers. Beyond evaluating traditional security and compliance capabilities, organizations increasingly need to understand how AI fits within their broader governance framework.

Common questions include:

How is organizational data used by AI?
Which users can access AI capabilities?
Can administrators control how AI is used?
How are AI-generated outputs reviewed?
How are sensitive research findings protected?

As AI becomes a core part of enterprise research workflows, these considerations are playing an increasingly important role in software evaluations. Organizations should ensure AI capabilities align with their existing security, compliance, and governance requirements before deploying them at scale.

Enterprise Readiness Verdict

Both Sprig and Qualtrics provide the enterprise security, governance, and administrative capabilities expected by large organizations.

Qualtrics has supported complex global deployments for many years and offers mature governance controls across its broader experience management platform.

Sprig combines enterprise-grade security, identity management, administrative controls, and governance with an AI-native survey platform designed for modern research workflows. Organizations evaluating Sprig do not need to trade enterprise readiness for ease of use or AI innovation. Instead, they can deploy a platform that meets enterprise security expectations while enabling researchers and business teams to design, field, and analyze surveys more efficiently.

Integrations, APIs & AI Workflows

Enterprise survey platforms rarely operate in isolation. Survey data becomes significantly more valuable when it can be connected with the rest of an organization’s technology stack and incorporated into everyday business workflows.

Customer feedback can inform product decisions, product analytics provide context for survey responses, CRM data enables richer customer segmentation, business intelligence platforms combine survey results with operational metrics, and AI assistants increasingly help researchers explore findings and generate reports.

The goal of integrations is not simply to move data between systems. It’s to make customer feedback accessible wherever decisions are being made across the organization.

Both Sprig and Qualtrics provide extensive integration capabilities suitable for enterprise environments. The primary difference is how each platform integrates with modern AI workflows and enables teams to interact with research using AI assistants.

At a Glance

| Category | Sprig | Qualtrics | |:-------------------------------------------:|:-----------------:|:----------------------:| | REST APIs | ✅ | ✅ | | Webhooks | ✅ | ✅ | | CRM Integrations | ✅ | ✅ | | Collaboration Tools | ✅ | ✅ | | Product Analytics | ✅ | ✅ | | Data Export | ✅ | ✅ | | Enterprise Automation | ✅ | ✅ | | AI Assistant Integration | Native MCP Server | Limited | | Analyze Surveys in Claude, ChatGPT & Gemini | Yes | No native MCP workflow |

Enterprise Research Lives Across Many Systems

Customer insights rarely exist in a single application. Organizations combine survey data with information from across their technology stack, including:

CRM platforms
Product analytics
Customer support systems
Marketing automation platforms
Business intelligence tools
Data warehouses
Customer Success platforms
Experimentation platforms

Survey data becomes significantly more valuable when viewed alongside these operational systems. For example:

Product Managers may compare survey responses with product usage data.
Customer Success teams may analyze satisfaction alongside renewal risk.
Marketing teams may evaluate brand perception against campaign performance.
Executives may compare survey trends with revenue or retention metrics.

Integrations make these workflows possible by connecting customer feedback with the systems where business decisions are already being made.

APIs

Enterprise organizations increasingly automate research workflows. Instead of manually creating surveys, exporting responses, and moving data between systems, APIs allow enterprise applications to interact directly with the survey platform.

Common examples include:

Automatically creating surveys after major product releases.
Sending customer feedback requests following support interactions.
Synchronizing customer attributes from a CRM.
Retrieving survey responses for internal reporting.
Building custom dashboards.
Connecting research with proprietary internal systems.

Both Sprig and Qualtrics provide enterprise APIs that support these types of automated workflows.

AI Is Changing How Researchers Work

For many years, integrations focused primarily on moving data between business applications. Today, researchers increasingly spend time working directly with AI assistants such as Claude, ChatGPT, and Gemini, making AI another important part of the enterprise technology stack.

Rather than exporting spreadsheets and manually preparing data, researchers increasingly expect to ask questions like:

Summarize this survey.
Compare enterprise customers with SMB customers.
Identify the biggest onboarding issues.
Highlight pricing concerns.
Generate an executive presentation.
Recommend follow-up research.

Traditional survey software was not designed for this style of interaction. Modern enterprise survey platforms increasingly need to support AI-native workflows that allow researchers to interact with customer feedback using natural language.

Sprig MCP

Sprig approaches AI interoperability differently through its native Model Context Protocol (MCP) server. Rather than requiring researchers to export spreadsheets or manually copy survey responses into AI tools, organizations can securely connect Sprig directly to modern AI assistants and analyze survey data using natural language.

Researchers can work with AI assistants such as:

Claude
ChatGPT
Gemini

Once connected, they can ask questions like:

Summarize the top five reasons enterprise customers are dissatisfied.
Compare customer feedback before and after our onboarding redesign.
Generate a board-ready summary of this pricing study.
Identify statistically significant differences between administrators and end users.

Because the AI assistant has secure access to the survey context, researchers spend less time exporting, cleaning, and reformatting data, allowing them to focus on interpreting results rather than preparing them for analysis.

Instead of repeatedly moving data between systems:

Export survey responses.
Open a spreadsheet.
Clean and organize the data.
Upload the file to an AI assistant.
Repeat the process for every new study.

Researchers can simply connect their AI assistant to Sprig and begin asking questions immediately.

Why MCP Matters

The value of MCP extends well beyond convenience. It fundamentally changes how organizations interact with research by shifting insights from static reports to continuous exploration.

Historically, researchers created reports and stakeholders consumed them. AI-native workflows enable a much more interactive model. Instead of waiting for new dashboards or presentations, teams can explore customer feedback directly.

For example:

Executives can ask follow-up questions without requesting another report.
Product teams can investigate customer feedback independently.
Researchers spend less time answering repetitive requests and more time interpreting complex findings.

Rather than relying on static dashboards, organizations gain a dynamic research environment where insights remain continuously accessible. As AI assistants become a standard part of enterprise knowledge work, this style of interaction is likely to become increasingly important.

From Integrations to Interoperability

The next generation of enterprise software will place greater emphasis on interoperability than isolated integrations. Rather than simply connecting dozens of point solutions, organizations increasingly expect their systems to work naturally with AI assistants that can retrieve information, perform analysis, and help teams make decisions.

Enterprise survey platforms are no exception. Increasingly, organizations want AI assistants that can answer questions such as:

Can our AI assistant understand customer feedback?
Can it compare findings across multiple research studies?
Can it prepare executive-ready summaries?
Can it recommend follow-up research?
Can it answer stakeholder questions instantly?

These capabilities represent a fundamental shift in how enterprise research is consumed. Instead of relying on static reports and dashboards, organizations can interact with customer insights through conversational AI, making research more accessible across the business.

Research as Part of the Enterprise AI Stack

The strongest enterprise survey platforms no longer operate as standalone survey tools. Instead, they become part of a broader customer intelligence ecosystem where survey data is connected with other sources of business context, including:

Behavioral analytics
CRM data
Operational metrics
Business intelligence platforms
AI assistants
Historical research

Bringing these systems together creates a richer understanding of customers and enables faster, more informed decision-making across the organization. Rather than existing as another isolated data source, enterprise research becomes an integrated part of everyday product, marketing, customer success, and executive workflows.

Integrations & AI Workflow Verdict

Both Sprig and Qualtrics provide the APIs, integrations, and enterprise connectivity expected of modern survey platforms. Organizations can connect surveys with CRM systems, analytics platforms, collaboration tools, data warehouses, and internal applications to incorporate customer feedback into broader business workflows.

Where Sprig differentiates itself is in its approach to AI interoperability. Through its native MCP server, organizations can securely connect survey data to AI assistants such as Claude, ChatGPT, and Gemini, enabling conversational analysis without manually exporting or preparing data. As AI assistants become a standard interface for knowledge work, this architecture positions research as something teams can actively explore rather than simply consume through static dashboards.

For organizations investing in AI across their business, this represents a shift from traditional software integrations toward a more natural, AI-native way of interacting with customer insights.

Implementation, Migration & Total Cost of Ownership

Selecting an enterprise survey platform is only the beginning of the evaluation process. Organizations also need to understand how quickly the platform can be deployed, how difficult it will be to migrate existing research programs, and what it will cost to operate over the long term.

Common implementation questions include:

How long will implementation take?
How difficult is migration?
Will researchers need extensive retraining?
Can existing surveys be reused?
How much operational work is required after launch?
What are the long-term administration costs?

For many enterprises, these practical considerations ultimately determine whether a platform is adopted successfully. The best enterprise software is rarely the platform with the longest feature list. Instead, it’s the platform that delivers value quickly, minimizes operational complexity, and remains sustainable to manage as the organization grows.

At a Glance

Every organization’s implementation will differ depending on scale, governance requirements, integrations, and the complexity of existing research programs.

Enterprise Implementations Are Rarely Greenfield

Most organizations evaluating a new enterprise survey platform are not starting from scratch. Instead, they already have an established research ecosystem that may include:

Hundreds of existing surveys
Historical response data
Established research processes
Business stakeholders
Enterprise integrations
Reporting workflows
Governance policies

Replacing an enterprise survey platform is therefore less about installing new software and more about managing organizational change. A successful migration minimizes disruption to existing research programs while allowing teams to adopt modern workflows gradually and begin realizing value early.

Why Organizations Consider Migrating

Every organization has different reasons for evaluating a new survey platform, but several common themes emerge when teams reassess legacy research tools. Rather than looking for a single missing feature, organizations are often trying to modernize how research is conducted across the business.

Common motivations include:

Reducing operational complexity
Improving researcher productivity
Increasing AI adoption
Making research more accessible across business teams
Modernizing the user experience
Consolidating multiple research workflows into a single platform
Accelerating research cycles
Improving stakeholder adoption

Migration decisions are rarely driven by any one capability. More often, they reflect a broader effort to simplify research operations, shorten the path from question to insight, and enable more teams to make data-informed decisions.

Migration Does Not Need to Be All or Nothing

One of the most common misconceptions about enterprise software migrations is that every team, workflow, and study must move to the new platform at the same time. In practice, the most successful organizations take a phased approach that minimizes disruption while allowing teams to adopt new workflows gradually.

A typical migration plan looks like this:

Continue running existing long-term research programs on the current platform.
Launch new studies on the new platform.
Evaluate adoption and gather feedback from early users.
Expand gradually across additional business units.
Retire legacy projects over time.

This incremental approach reduces implementation risk, allows researchers to become comfortable with new workflows, and enables organizations to realize value long before the migration is fully complete.

Existing Surveys

One of the first questions organizations ask during a migration is, “What happens to our existing surveys?” The answer depends on the complexity of each study, but many enterprise surveys are built from a common set of capabilities, including:

Standard questions
Branching logic
Randomization
Quotas
Embedded variables

These studies can often be recreated efficiently on a new platform. More specialized surveys may require additional planning, but organizations typically prioritize migrating the research programs that deliver the greatest ongoing value.

Common migration priorities include:

High-frequency surveys
Customer satisfaction programs
Market research
Product research
Recurring tracking studies

Older one-off projects that are no longer actively used can often be migrated later or archived.

Historical Research

Survey questionnaires are only one part of an organization’s research investment. Historical findings often represent years of institutional knowledge, including:

Customer feedback
Pricing studies
Brand tracking
Competitive research
Executive presentations
Open-ended responses
Trend analysis

When evaluating a migration, organizations should consider not only how future studies will be conducted, but also how historical insights will remain accessible. Increasingly, enterprise research teams view their historical research as a strategic asset that informs future decisions, rather than documentation that sits unused in archived reports.

Training & Adoption

A survey platform only creates value if people actually use it. Successful enterprise deployments therefore require more than technical implementation. They also require thoughtful onboarding, enablement, and user adoption across the organization.

Training often extends beyond dedicated researchers to include:

Researchers
Product managers
Marketing teams
Customer Success
Executives
Operations teams
Data analysts

The easier a platform is to learn, the faster organizations begin generating value. AI-assisted workflows can further reduce the learning curve by helping new users focus on their research objectives instead of learning complex software interfaces.

AI Can Reduce the Learning Curve

Historically, new researchers learned survey software by memorizing interfaces, navigation, and configuration menus. Modern AI changes this dynamic by allowing users to focus on the research problem rather than the software itself.

Instead of asking:

“Where is the branching menu?”

Researchers increasingly begin with questions like:

“I need to understand why enterprise customers churn.”

AI helps generate an appropriate starting point, allowing researchers to spend more time thinking about methodology and business questions rather than software mechanics. While training remains important, AI reduces many of the barriers that have traditionally slowed adoption across larger organizations.

Administration Over Time

Implementation is a one-time project, but administration continues for the lifetime of the platform. Enterprise buyers should therefore evaluate the ongoing operational effort required to support a growing research organization.

Key considerations include:

How many administrators are required?
How much governance is necessary?
How frequently are templates updated?
How are new users onboarded?
How are permissions managed?
How much ongoing maintenance is required?

Reducing administrative overhead can generate meaningful long-term savings that extend well beyond software licensing costs.

Time to Value

One of the most important implementation metrics isn’t deployment time, it’s time to value. The key question is how quickly an organization can begin answering important business questions after adopting a new platform.

A platform that launches quickly but requires months of training may not deliver value immediately. Conversely, a platform that combines streamlined administration with AI-assisted workflows can help organizations begin conducting high-quality research much sooner.

Enterprise buyers should evaluate not only how quickly software can be deployed, but how quickly researchers can move from implementation to meaningful business outcomes.

Total Cost of Ownership

Software licensing represents only one component of the total cost of an enterprise survey platform. Organizations should also consider the ongoing operational costs associated with:

Implementation
Training
Administration
Professional services
Research operations
Maintenance
Workflow complexity
Researcher productivity

Over the lifetime of a deployment, these indirect costs often exceed software licensing fees.

For example, reducing survey creation time by several hours per study can generate significant productivity gains for organizations conducting hundreds of studies each year. Similarly, consolidating participant recruitment, native email delivery, AI-assisted analysis, and reporting into a single platform can reduce the number of vendors required to operate an enterprise research program.

Taken together, these operational efficiencies can have a significant impact on total cost of ownership and the long-term return on an organization’s investment.

Questions Enterprise Buyers Should Ask

Rather than focusing exclusively on feature comparisons, enterprise buyers should evaluate how a platform will operate over the lifetime of the deployment. Practical questions often have a greater impact on long-term success than individual product capabilities.

Key questions include:

How quickly can our teams become productive?
How many separate systems will researchers need to manage?
How much manual work remains after implementation?
Can we consolidate existing vendors?
How difficult is onboarding new teams?
Will AI reduce ongoing operational effort?
Can the platform scale with our research organization over the next five years?

The answers to these questions often determine whether a platform delivers lasting business value rather than simply meeting a list of functional requirements.

A Practical Migration Strategy

Organizations replacing Qualtrics often benefit from a phased rollout rather than attempting a single, organization-wide migration. A staged approach allows teams to realize value early, validate new workflows, and reduce implementation risk before expanding adoption.

Phase 1: Pilot

Begin with a single business unit or research team to validate the platform and establish early success.

Launch several representative studies.
Evaluate research workflows.
Train key users and gather feedback.

Phase 2: Expansion

Extend the rollout to additional departments while establishing consistent ways of working.

Introduce additional business units.
Standardize survey templates.
Establish governance policies.
Connect enterprise integrations.

Phase 3: Consolidation

Once adoption is established, expand the platform across recurring research programs and reduce operational complexity.

Migrate recurring research programs.
Expand participant recruitment.
Standardize reporting.
Retire redundant tools where appropriate.

Phase 4: Optimization

Focus on maximizing long-term value by embedding AI and automation into everyday research workflows.

Refine AI-assisted workflows.
Automate recurring research programs.
Improve executive reporting.
Build reusable research libraries.
Continue expanding adoption across the organization.

A phased implementation allows organizations to modernize their research operations incrementally while minimizing disruption to existing programs. Rather than waiting until every migration task is complete, teams can begin realizing business value early and continue improving as adoption grows.

Implementation & Migration Verdict

Both Sprig and Qualtrics are capable of supporting enterprise-scale deployments, and both can serve as the foundation for sophisticated research programs.

Qualtrics is often deeply embedded within large organizations, making continuity an important consideration for existing customers with extensive deployments and established operational processes.

Sprig offers organizations an opportunity to modernize how research is conducted by combining AI-assisted survey creation, native email delivery, integrated participant recruitment, AI-powered analysis, and enterprise governance within a single platform. For many organizations, a phased migration strategy allows teams to adopt these newer workflows incrementally while minimizing disruption to ongoing research programs.

Ultimately, implementation should be evaluated not only by how quickly software can be deployed, but by how quickly the organization begins generating better decisions from customer feedback.

Which Platform Is Right for Your Team?

There is no single “best” enterprise survey platform. The right choice depends on your organization’s goals, how research is conducted, who creates surveys, and how insights are ultimately used across the business.

Some organizations prioritize maximum configurability, mature governance, and established enterprise processes. Others prioritize faster research cycles, AI-assisted workflows, and making high-quality research accessible beyond centralized insights teams.

The following sections summarize which platform is generally the best fit for common enterprise use cases, helping you evaluate how each aligns with your organization’s research strategy, operating model, and long-term priorities.

Product Management

Modern product organizations rely heavily on customer feedback.

Product teams regularly ask questions such as:

Which features should we build next?
Why are customers struggling with onboarding?
Which product changes improve satisfaction?
How should we prioritize roadmap investments?
What do customers think about a beta feature?

Product research is increasingly continuous rather than project-based.

Teams expect to launch surveys quickly, analyze responses immediately, and incorporate findings directly into product development.

Why teams choose Sprig

Sprig is particularly well suited for product organizations that want to shorten the feedback loop between product decisions and customer insights.

Capabilities that support this workflow include:

AI-assisted survey creation
Patented in-product survey SDKs
Native email delivery
AI-powered synthesis
Integrated participant recruitment
Product analytics integrations
Conversational AI through the Sprig MCP server

Together, these capabilities enable product teams to move from an idea to validated customer feedback in significantly less time.

Why teams choose Qualtrics

Organizations with mature centralized research organizations may prefer Qualtrics when product research is part of a broader enterprise experience management strategy.

Market Research

Market research often requires the broadest set of capabilities.

Researchers may need to conduct:

Pricing studies
Brand tracking
Concept testing
Competitive research
Segmentation
Message testing
Purchase intent studies

These projects frequently require recruiting respondents who are not existing customers.

Why teams choose Sprig

Sprig combines advanced methodologies with an integrated participant marketplace.

Researchers can:

Design studies using AI
Recruit from more than 5 million B2B and B2C participants
Target audiences using 300+ demographic, firmographic, geographic, behavioral, and professional attributes
Launch studies immediately
Analyze findings with AI-generated summaries

Because participant recruitment is built directly into the platform, researchers spend less time coordinating external vendors and more time conducting research.

Why teams choose Qualtrics

Qualtrics remains a strong platform for sophisticated market research, particularly for organizations with established research operations and existing Qualtrics deployments.

Customer Experience (CX)

Customer experience teams measure satisfaction across the customer lifecycle.

Common programs include:

NPS
CSAT
CES
Relationship surveys
Transactional surveys
Journey measurement

These programs often involve recurring surveys and large respondent volumes.

Why teams choose Sprig

Sprig enables organizations to combine customer experience measurement with AI-powered analysis and modern survey workflows within a single platform. Native email delivery simplifies recurring customer feedback programs, while AI accelerates analysis, reporting, and the delivery of executive-ready insights.

Why teams choose Qualtrics

Qualtrics has extensive experience supporting large-scale customer experience (CX) programs and remains a widely adopted platform for enterprise experience management. Organizations already invested in broader CX initiatives may benefit from continuing to build on that established ecosystem.

User Research & UX

User researchers often combine qualitative and quantitative methods to understand not just what users are doing, but why they behave the way they do.

Common research questions include:

Why do users behave the way they do?
Which experiences create friction?
Which concepts resonate most with customers?
Which usability improvements will have the greatest impact?

Why teams choose Sprig

Sprig combines in-product feedback with broader survey research in a single enterprise platform. Researchers can pair behavioral context with customer feedback while using AI to rapidly synthesize qualitative findings and identify recurring themes.

Integrated participant recruitment also makes it easier to recruit non-customers for exploratory studies, concept testing, and other user research projects.

Marketing

Marketing organizations frequently conduct research around:

Brand awareness
Campaign effectiveness
Message testing
Concept evaluation
Competitive positioning

Marketing teams often need to move quickly while involving multiple stakeholders.

Why teams choose Sprig

AI-assisted survey generation helps marketers launch research more quickly, while integrated participant recruitment removes the need to coordinate separate panel vendors for many studies.

Native email delivery also simplifies customer research campaigns.

Executive Leadership

Executives rarely interact directly with survey builders. Instead, they rely on research teams to deliver clear, actionable insights that support strategic decision-making.

Executive stakeholders typically need:

Clear recommendations
High-level summaries
Strategic insights
Reliable supporting evidence

Why teams choose Sprig

Sprig’s AI-generated summaries and conversational analysis reduce the effort required to consume research, allowing executives to focus on decisions rather than navigating dashboards or reviewing detailed reports.

The Sprig MCP server also enables executives and strategy teams to explore customer research directly from AI assistants such as Claude, ChatGPT, and Gemini using natural language. Rather than waiting for custom reports, leaders can ask follow-up questions, investigate customer feedback, and explore research findings as new business questions arise.

Research Operations

Research Operations teams are responsible for maintaining consistency across an organization.

Their priorities often include:

Governance
Templates
Training
Methodological quality
User management
Platform administration

Why teams choose Sprig

Organizations seeking to democratize research often appreciate workflows that reduce training requirements while maintaining enterprise governance.

AI assists business users in creating higher-quality surveys, allowing Research Operations teams to focus more on methodology and enablement rather than day-to-day survey support.

Why teams choose Qualtrics

Qualtrics remains a strong option for organizations with dedicated Research Operations teams managing large, mature enterprise deployments.

IT & Security

Technology teams evaluate enterprise survey platforms differently than researchers. While research teams often focus on usability and methodology, IT organizations are primarily concerned with security, governance, scalability, and long-term architectural fit.

Key evaluation criteria typically include:

Security
Compliance
Identity management
APIs
Governance
Scalability
Vendor stability

Both Sprig and Qualtrics provide the enterprise controls expected by large organizations. For IT teams, the decision typically comes down to how well each platform aligns with existing enterprise architecture, integration requirements, and long-term technology strategy.

Global Enterprises

Large multinational organizations often require:

Multiple business units
Regional administrators
Global governance
Multilingual research
Enterprise identity management
Scalable administration

Which Platform Fits Different Organizations?

The Bigger Strategic Question

The decision between Sprig and Qualtrics is ultimately about more than choosing survey software. It reflects two different visions for how enterprise research should operate over the next decade.

Qualtrics emphasizes a mature, comprehensive experience management ecosystem that has evolved over more than two decades and supports a wide range of enterprise research and experience management programs.

Sprig takes a different approach, focusing on an AI-native survey platform where survey creation, participant recruitment, native email delivery, analysis, and reporting are unified into a streamlined workflow designed to accelerate the entire research lifecycle.

Both approaches can successfully support enterprise research. The right choice depends on your organization’s long-term strategy, existing investments, and how you expect research to evolve over the next three to five years.

Frequently Asked Questions

Is Sprig a replacement for Qualtrics?

For many organizations, yes.

Sprig is an enterprise survey platform designed to support customer research, market research, product research, and in-product feedback within a single platform. It includes AI-assisted survey creation, native email delivery, integrated participant recruitment, advanced quantitative methodologies, enterprise security, and AI-powered analysis.

Whether it is the right replacement depends on your organization’s existing workflows, governance requirements, and long-term strategy.

Organizations heavily invested in multiple Qualtrics products may choose to continue expanding within that ecosystem, while organizations looking to modernize their research workflows often evaluate Sprig as an AI-native alternative.

What is the biggest difference between Sprig and Qualtrics?

The biggest difference is platform philosophy.

Qualtrics evolved into a comprehensive enterprise experience management platform over more than two decades.

Sprig was designed as an AI-native enterprise survey platform that helps organizations design, recruit, field, and analyze surveys more efficiently.

Both platforms support enterprise-scale research, but Sprig places greater emphasis on AI-assisted workflows, integrated participant recruitment, native email delivery, and reducing operational complexity throughout the research lifecycle.

Which platform has better AI?

Both Sprig and Qualtrics are investing heavily in artificial intelligence, and both platforms use AI to accelerate survey creation, analysis, and reporting.

Qualtrics has introduced AI capabilities across areas such as survey authoring, text analysis, and reporting, helping organizations automate parts of the research process while building on familiar enterprise workflows.

Sprig takes a broader AI-first approach by integrating AI throughout the research workflow, including:

Survey generation
Question refinement
Methodology recommendations
Response analysis
Theme extraction
Executive summaries
Conversational analytics
Native integration with AI assistants through its MCP server

Organizations evaluating AI should consider not only individual features but also how much of the research lifecycle AI helps accelerate.

Which platform is easier to use?

Both Sprig and Qualtrics are designed for enterprise organizations, but they prioritize different user experiences.

Qualtrics provides extensive configurability and flexibility, making it well suited for organizations with mature research operations, dedicated platform administrators, and established governance processes.

Sprig emphasizes a modern user experience supported by AI-assisted workflows that reduce manual effort, shorten the learning curve, and help researchers and business users become productive more quickly.

Ultimately, the right choice depends on who will be conducting research within your organization. Teams that rely on centralized research operations may prioritize configurability, while organizations looking to expand research across product, marketing, customer success, and other business teams may place greater value on ease of use and AI-assisted workflows.

Does Sprig support advanced market research?

Yes.

Sprig supports advanced methodologies commonly used in enterprise market research, including:

Conjoint analysis
MaxDiff
Van Westendorp pricing research
Gabor-Granger pricing research
Quotas
Randomization
Advanced branching logic
Embedded variables

These capabilities are combined with AI-assisted survey design, integrated participant recruitment, native email delivery, and AI-powered analysis.

Does Sprig have its own research panel?

Yes.

Sprig includes a native participant marketplace with access to more than 5 million verified B2B and B2C participants.

Researchers can recruit respondents directly within the platform without coordinating separate panel vendors for many studies.

The marketplace supports more than 300 demographic, firmographic, geographic, behavioral, and professional targeting attributes, making it possible to recruit highly specific audiences for both consumer and business research.

What kinds of participants can I recruit?

Organizations can recruit participants across a wide range of audiences, including both B2B and B2C populations.

Examples include:

Consumers
Parents
Students
Small business owners
Software developers
Product managers
HR leaders
Healthcare professionals
IT decision makers
Finance executives
Marketing leaders

Researchers can combine multiple targeting attributes to build highly specific audiences for specialized studies.

Does Sprig support native email surveys?

Yes.

Sprig includes native email delivery, allowing organizations to create, schedule, send, and monitor customer surveys directly within the platform.

Because email distribution is integrated into the survey workflow, researchers do not need to export surveys into separate email marketing tools simply to launch customer research.

In addition to email, Sprig supports:

In-product surveys
Web links
QR codes
SMS
APIs
External participant recruitment

Can Sprig replace separate panel vendors?

For many organizations, yes. Because participant recruitment is integrated directly into the survey platform, many market research studies can be completed without coordinating separate panel vendors.

Researchers can manage the entire participant recruitment workflow within a single platform by:

Designing the survey
Defining the target audience
Recruiting participants
Monitoring quotas
Launching fieldwork
Analyzing results

This integrated approach reduces operational complexity, shortens the time between survey design and fieldwork, and eliminates many of the manual handoffs associated with traditional panel procurement.

Organizations with specialized procurement requirements or highly customized sampling needs may still choose to work with additional providers, depending on the nature of their research.

Does Sprig support B2B research?

Yes. Sprig’s integrated participant marketplace supports B2B research through extensive professional targeting capabilities, making it possible to recruit highly specific business audiences directly within the survey workflow.

Researchers can target respondents using attributes such as:

Industry
Company size
Revenue
Department
Job title
Seniority
Professional responsibilities

These targeting capabilities enable organizations to recruit audiences ranging from startup founders and functional leaders to Fortune 500 executives, supporting everything from product research and pricing studies to enterprise market research.

Can Sprig support global research?

Yes. Sprig enables organizations to recruit participants and conduct surveys across multiple countries and regions, making it well suited for both regional and global research programs.

Global studies often combine capabilities such as:

Multilingual surveys
Regional quotas
Demographic and geographic targeting
Enterprise governance and administration

These capabilities allow organizations to conduct international research while maintaining consistent standards across markets.

Does Sprig support in-product surveys?

Yes. Sprig includes patented in-product survey SDKs that allow organizations to collect feedback directly within web and mobile applications.

Teams can trigger surveys based on user behavior, enabling them to capture feedback while customers are actively using the product and experiences are still fresh.

In-product surveys complement other distribution channels, including:

Native email delivery
Shareable links
SMS
External participant panels

Together, these distribution methods enable organizations to collect feedback wherever it is most relevant, whether inside the product or through broader customer and market research programs.

Does Sprig integrate with AI assistants?

Yes. Sprig includes a native Model Context Protocol (MCP) server that allows organizations to securely connect survey data with modern AI assistants, including:

Claude
ChatGPT
Gemini

Once connected, researchers can analyze survey results using natural language without manually exporting data or uploading spreadsheets. This makes it easier to explore customer feedback, answer follow-up questions, generate executive-ready summaries, and uncover insights directly within the AI tools teams already use.

By bringing survey data directly into AI-assisted workflows, Sprig helps organizations reduce manual preparation while making customer research more accessible across the business.

Can I migrate from Qualtrics to Sprig?

Yes. Many organizations adopt a phased migration strategy rather than replacing every survey at once. This approach allows teams to begin using new workflows while minimizing disruption to existing research programs.

A typical migration plan includes:

Continue running existing long-term research programs.
Launch new studies in Sprig.
Train research teams and gather feedback.
Expand adoption gradually across additional teams.
Retire legacy projects over time.

This incremental approach reduces implementation risk, allows organizations to realize value early, and provides flexibility to modernize research operations at a pace that aligns with business priorities.

How long does implementation typically take?

Implementation timelines vary depending on:

Organization size
Governance requirements
Number of users
Integrations
Existing research programs

Many organizations begin launching new surveys within days, while broader enterprise rollouts typically follow a phased implementation plan.

Is Sprig secure enough for enterprise organizations?

Yes.

Sprig includes enterprise capabilities such as:

Single Sign-On (SSO)
SCIM provisioning
Role-based permissions
Administrative controls
Audit capabilities
Enterprise APIs
Encryption

Organizations should evaluate security requirements alongside their internal compliance policies during procurement.

Which platform is better for product teams?

The right platform depends on how product research is conducted within your organization. Teams running continuous product research often prioritize capabilities such as:

AI-assisted survey creation
In-product surveys
Rapid iteration
Fast analysis
Integrated participant recruitment
Modern user experience

These priorities align closely with Sprig’s AI-native platform, which is designed to help product teams move quickly from product questions to validated customer insights while reducing operational complexity.

Organizations that have already standardized product research within broader Qualtrics deployments may prefer to remain within that ecosystem, particularly if product research is closely integrated with existing enterprise experience management programs.

Which platform is better for market research?

Both Sprig and Qualtrics support sophisticated market research, including advanced methodologies such as conjoint analysis, MaxDiff, Van Westendorp, and Gabor-Granger.

Qualtrics has long been a trusted platform for enterprise market research and remains a strong choice for organizations with mature research operations and established workflows.

Sprig combines these same research methodologies with integrated participant recruitment, native email delivery, AI-assisted survey creation, and AI-powered analysis within a single platform. For organizations looking to streamline market research workflows and accelerate time to insight, this integrated approach can significantly reduce operational complexity.

Ultimately, the right choice depends on how your organization prioritizes workflow efficiency, AI adoption, and existing research infrastructure.

Which platform is better for customer experience programs?

Both Sprig and Qualtrics can support enterprise customer experience (CX) programs, including recurring customer satisfaction, NPS, CSAT, and transactional feedback initiatives.

Qualtrics has extensive experience supporting large-scale CX programs and remains a widely adopted platform for organizations with established experience management strategies.

Sprig combines customer surveys, product research, market research, participant recruitment, native email delivery, and AI-powered analysis within a single enterprise survey platform. This unified approach enables organizations to manage a broader range of research programs while reducing operational complexity and accelerating insight generation.

The right choice ultimately depends on whether your organization prioritizes continuity with an existing experience management ecosystem or a more AI-native approach to customer research.

Why are organizations evaluating alternatives to Qualtrics?

The reasons vary by organization, but common motivations include:

Modernizing research workflows
Increasing AI adoption
Reducing operational complexity
Improving usability
Accelerating survey creation
Consolidating multiple research vendors
Expanding research across more business teams

Many organizations are also reassessing how AI changes the way enterprise research should be conducted.

Can Sprig replace multiple research tools?

For many organizations, yes. Rather than relying on separate products for different stages of the research lifecycle, Sprig brings multiple capabilities together within a single enterprise survey platform, including:

Survey creation
Native email distribution
Participant recruitment
AI-powered analysis
Executive reporting

By consolidating these workflows, organizations can reduce operational complexity, eliminate unnecessary handoffs between systems, and help research teams move more efficiently from business questions to actionable decisions.

Which platform should my organization choose?

There is no universal answer. The right platform depends on your organization’s existing investments, research maturity, long-term strategy, and how you expect research to evolve over the coming years.

Qualtrics remains an excellent choice for organizations with mature experience management programs, dedicated Research Operations teams, and significant investments in the broader Qualtrics ecosystem.

Sprig is well suited for organizations looking to modernize enterprise research with AI-native workflows, integrated participant recruitment, native email delivery, advanced quantitative methodologies, enterprise governance, and conversational AI capabilities.

Ultimately, the best platform is the one that enables your teams to answer important business questions more quickly, share insights broadly across the organization, and consistently turn customer research into better business decisions.