AI Tools for Research & Insights: Market Landscape

By Insight Platforms

• market map

• AI
• AI Agents
• AI Interviews
• AI Moderation
• Generative AI
• Artificial Intelligence
• Machine Learning
• Text Analytics
• Video Analytics
• Video Research
• Knowledge Management
• Research Repository
• Predictive Analytics
• CX Analytics
• Chatbots
• Chat/Messaging Surveys
• Conversational AI
• Synthetic Respondents
• Social Media Listening/Intelligence
• Competitor Analysis
• Trend Analytics
• Emotion Analytics
• Survey Software

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AI tools for research & insights have proliferated, and this landscape is an imperfect effort to represent nearly 250 of them in a coherent framework.

Jump to the graphic: AI Tools for Research & Insights Market Landscape.

There are some important caveats to bear in mind.

It’s not a definitive taxonomy.

The landscape is organised into 10 to 12 buckets, an attempt to group the various functionalities into meaningful clusters. The directory has many more granular categories that will be more descriptive and useful.

Some things are not on the map.

The AI tools landscape is for solutions intended for research and insights use cases. So we’ve not included:

• Generic AI Assistants like ChatGPT, Claude, Google Gemini, and Microsoft Co-Pilot;
• General Purpose Wrapper Tools like writing, transcription, or note-taking applications such as Otter.ai or Jasper.ai;
• Business Intelligence and Data Visualisation Tools like Tableau are or Power BI;
• Marketing Mix Modelling, Attribution, and Predictive Analytics Tools – we had to stop somewhere.

This is snapshot at a point in time.

Everything in AI changes quickly. Annoyingly quickly. We will try to update the landscape as changes occur.

We also acknowledge that we may have misclassified or missed some tools altogether. We’re open to politely submitted feedback and requests for updates.

Here’s a breakdown of the different categories of AI research tools in the landscape.

Video Capture and Analytics:

These AI tools capture video for research and feedback – real-time or asynchronous, mostly via smartphone or desktop – and analyse video content through transcription, translation, text analytics and computer vision.

Applications include mobile ethnography, video diaries, real time interviews and focus groups, in-home product testing, video open-end survey questions and many more.

Chatbot Interviews and Conversational Surveys

These tools use either LLMs or proprietary models built on Natural Language Processing to simulate dialogue between a researcher and research participant. They are used for qualitative research; enhancing survey research; and what is increasingly called ‘qual-at-scale’.

Most of these solutions use text-based interface for chat interactions; some are voice-enabled so that participants feed back with audio responses; and a handful use video avatars to pose questions.

Qualitative Data Analysis

These tools analyse, summarise or help generate reports based on qualitative research data, which may come from documents, interview notes, transcripts, audio or video files.

There is some overlap with other categories (Video Capture & Analytics; Repositories & Knowledge Management).

Some of these tools have a long pedigree in academic and qualitative research; some are very recent startups built largely around new generative AI capabilities in LLMs.

Survey Creation and Analysis

Tools here include AI assistants for brainstorming survey ideas, drafting questionnaires, analysing data, creating charts and generating written or slide-based reports.

It does not include that only conduct text analytics and verbatim coding of survey data …

Text Analytics and Verbatim Coding

Many of the tools in this category have a long pedigree of using Natural Language Processing to understand and classify unstructured text data into topics, themes and sentiment. Some tools are also designed to work in a survey data workflow alongside human coders.

There is significant overlap with the CX & Product Feedback Analytics category …

CX & Product Feedback Analytics

Although ultimately built on the same principles, these tools are called out separately from the general purpose and survey-focused text analytics tools above.

They are much more numerous; they feature integrations with a wide range of unstructured feedback sources (reviews data, complaints, support tickets, feature requests, bug notes – as well as NPS verbatims); but the biggest distinction is that these tools focus on helping product management and customer experience teams to automate synthesis of feedback and pipe it into other systems – product roadmaps, call centre platforms or CRM.

Emotion AI

These tools are built to read signals from human behavior and generate models of implicit or emotional engagement. It may be changes in facial expression measure with webcams; changes in skin conductivity; or other measures used to estimate behavioural responses.

Synthetic and Augmented Data:

These solutions use LLMs and a variety of other data sources to generate or augment data sets.

This may be to create qualitative personas for exploratory interviews; quantitative virtual respondents to enrich or grow survey data sets; or entirely new groups of ‘digital twin’ synthetic data based on a range of different input sources.

Predictive Idea or Creative Testing

These tools use AI-based predictive models to simulate the responses that ‘real’ humans would give to certain inputs or stimuli.

For example: simulated eye tracking generates heat maps based on uploaded designs that show which areas are most likely to attract the attention of users. Simulated sensory testing might predict which combination of flavours will appeal most to particular consumer segments.

Tools in this category also use AI to run predictive models for static ads, product concepts, videos, movies and even political polls.

Social Competitor and Trend Intelligence

These tools provide social listening, competitor analysis and predictive trend capabilities. Most analyse publicly available web-based sources using NLP( topic analysis, sentiment analysis etc) or visual analytics (eg to identify brand assets and objects in images).

Competitor analysis solutions monitor specific websites and mentions in other channels; trend analytics tools try to identify emerging growth opportunities from online conversations or other public indicators.

Repositories and Knowledge Management

These tools help organisations to store, share, and improve the flow of research, data and insights data through organisation.

It includes research repositories, which have some features in common with the qualitative data analysis category. Repositories are mainly used by UX and product research teams to store interview transcripts, results of user tests, observations, and other (mainly qualitative) types of data.

It also covers knowledge management platforms design for insights and research data such as reports, presentations and video; along with integrations to other internal and external sources of data.

The AI Tools for Research & Insights: Market Landscape

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