SurveyMonkey remains a major online survey platform, but its current product direction is increasingly centered on AI-assisted survey creation and analysis. The company says its AI features are built on decades of survey science and platform data.

What SurveyMonkey does

SurveyMonkey AI can generate surveys from a natural-language description, helping users move from a research objective to a structured questionnaire.

AI can also import an existing survey and transform it into a structured SurveyMonkey questionnaire with appropriate question types and formatting.

Key capabilities to know

  • SurveyMonkey AI can generate surveys from a natural-language description, helping users move from a research objective to a structured questionnaire.
  • AI can also import an existing survey and transform it into a structured SurveyMonkey questionnaire with appropriate question types and formatting.
  • SurveyMonkey's AI Analysis Suite can help users analyze open-ended responses, identify themes, summarize findings, and explore results through conversational analysis.
  • Additional AI features include question-type prediction, answer-choice recommendations, survey tips, sentiment analysis, and response-quality detection.
  • SurveyMonkey has expanded its AI integrations, including a ChatGPT connector announced in August 2026 that allows users to create surveys, collect feedback, and extract insights from within ChatGPT.

How the workflow works

A good survey workflow starts with the research question rather than the tool. AI can accelerate drafting, but the researcher still needs to define the population, measurement goals, sampling approach, and decisions that will be made from the results.

Once the instrument is designed, SurveyMonkey can help with creation, collection, analysis, and reporting, reducing the amount of manual work between asking a question and interpreting the response.

  1. Define the research objective and target respondents.
  2. Generate or import a draft survey and review every question for bias, clarity, and relevance.
  3. Distribute the survey and monitor response quality and completion patterns.
  4. Use AI analysis to identify themes and patterns, then validate important conclusions against the underlying responses and sample.

Practical use cases

  • Customer satisfaction and NPS-style feedback.
  • Employee engagement and pulse surveys.
  • Product research and concept testing.
  • Event and post-purchase feedback.
  • Open-ended response analysis at scale.

What to consider before adopting it

AI cannot fix a poorly designed research question. Leading wording, biased samples, low response rates, and ambiguous answer choices can still produce misleading results even when the analysis is automated.

Privacy and data governance also matter, especially for employee, customer, or regulated information. Teams should understand what data is being processed, which integrations are enabled, and what plan or regional restrictions apply.

Bottom line

SurveyMonkey's 2026 direction is to shorten the distance between survey design and insight. The most useful approach is to treat AI as an accelerator around established research discipline, not as a substitute for it.