Introduction
Market research is being revolutionized by generative AI. Today, you can obtain in-depth analysis, competitor data, and trend forecasts in minutes, without the need for manual surveys or tedious spreadsheets. This article demonstrates how to leverage the latest AI tools of 2026 to conduct fast and accurate market research.Why AI is transforming market research
AI is modernizing the entire research process, from data collection to result interpretation. Current models can process millions of tokens, understand audio and video, and perform actions autonomously. The result? Faster insights, fewer errors, and the ability to focus on strategic decisions rather than data management.
Current AI tools for market research
Claude Code Projects: parallel cloud sessions
Anthropic has redesigned Projects in Claude Code, transforming it into a continuous conversation that persists even after you close your laptop. This means you can start a research project, let it process data from multiple sources, and resume work later without losing progress. It’s ideal for complex, multi-level market analysis.
Qwen3.8-Omni-Flash: multimodal analysis
Alibaba has released Qwen3.8-Omni-Flash, a 1M token model capable of understanding audio, video, and text, with 45% lower token efficiency. You can upload a competitor’s promotional video, ask the AI to extract strengths and weaknesses, and receive a summary in seconds.
Agentforce Coworker: operational collaboration
Adecco Group has implemented Agentforce Coworker in over 40 countries, assigning AI the task of managing repetitive tasks such as price monitoring and candidate information gathering. This frees research teams from manual tasks, allowing them to focus on strategic interpretation.
How to build a market research workflow with AI
Step 1: Define the goal and collect raw data
Start with a clear prompt for an LLM that defines the scope of the research. Here’s a prompt template you can copy and adapt:
Analyze the Italian SaaS software market for personnel management in 2026.
Provide: total market size, quarterly growth trends, the top 5 competitors with their product features, and the key factors driving adoption.Use an LLM like Claude or GPT to generate this overview in seconds.
Step 2: AI-powered sentiment analysis
Collect reviews from Trustpilot, G2, and social media. Send a prompt like this to a multimodal model:
Extract sentiment from these 20 product reviews (positive, neutral, negative) and summarize recurring themes.Qwen3.8-Omni-Flash excels at processing large volumes of text and providing accurate sentiment analysis.
Step 3: Competitive extraction with code
Use Claude Code Projects to write a script that automatically extracts competitor prices, features, and reviews from their websites. Since Projects maintains state, you can resume processing after a break without losing progress.
Step 4: Visualization and reporting
Transform raw data into interactive dashboards with tools like Tableau or Power BI, powered by AI-generated datasets. Create shareable reports that showcase key insights, trends, and recommendations.
Practical example: from prompt to insight
Imagine you want to evaluate the launch of a new subscription package. Follow these steps:
- Goal definition:"What is the maximum willingness to pay for advanced analytics features among Italian SMEs?"
- Data collection:Extract competitor price pages with Claude Code Projects.
- Analysis:Upload the extracted text to Qwen3.8-Omni-Flash and ask for a price-by-feature analysis.
- Validation:Conduct a quick survey via Agentforce Coworker to gather direct feedback.
- Reporting:Generate a dashboard with price ranges and willingness-to-pay percentages.
The result is a strategic dataset ready to be discussed with the product team in less than an hour.
Conclusions
AI is democratizing market research. With tools like Claude Code Projects, Qwen3.8-Omni-Flash, and Agentforce Coworker, even a company with a limited budget can obtain professional-level market analysis in real time.
Final takeaways
- Start with a structured prompt to define clear goals and get concise answers.
- Leverage multimodal models to process non-textual data (video, audio) for a broader perspective.
- Use persistent code sessions for complex, multi-level data extraction projects.
- Automate repetitive tasks with Agentforce to free your research team for strategic activities.
- Iterate quickly: AI models allow you to test hypotheses in minutes, not weeks.
By implementing these AI-driven workflows, you’ll transform raw data into actionable insights, keeping you always one step ahead of the competition in 2026 and beyond.
IMPORTANT: We are in 2026. All temporal references must be current. Do not mention past years such as 2024, 2023, etc. The content must be fresh and relevant to the present. CURRENT CONTEXT (September 2026): Here are some recent trends and news to inspire you: - Fulcra Brings Universal Multiplayer to Any Agents You Choose: The Instinct-to-Instinct model that is the talk of Silicon Valley presents too small a future for personal AI. With Fulcra’s multiplayer capabilities... [2026-09-18] - Adecco Group rolls out Agentforce Coworker to 27,000 staff in 40-plus countries: The Adecco Group is rolling out Salesforce’s Agentforce Coworker across more than 40 countries following a pilot in the UK and France, the staffing... [2026-09-17] - Salesforce Agentforce: Bridging the Enterprise AI Gap from ‘Vibe Coding’ to Battle-Tested Orchestration: Building an AI prototype is easy, but operating autonomous agents at scale requires production-grade tooling. Salesforce Agentforce bridges the gap fr... [2026-09-18] Use this current information as inspiration to create an original and relevant prompt for 2026.