How to Conduct Effective Market Research with the Latest AI Tools
If you’re wondering how to conduct market research quickly, with fewer errors and at a lower cost, 2026 has the answer: advanced language and vision tools like GLM-5.3 and LFM2.5-VL-3B, combined with AWS and Novo Nordisk’s agentic AI.
1. The AI Tools Landscape in 2026
This year has seen the emergence of two models that are redefining what’s possible with AI in market research:
- GLM-5.3 by Z.ai
- LFM2.5-VL-3B by Liquid AI
Both solutions are already integrated into AWS cloud platforms and workflows at companies like Novo Nordisk, which is accelerating drug discovery with autonomous AI agents.
2. Building an Autonomous Workflow with GLM-5.3
GLM-5.3 excels at transforming complex text requests into executable code or detailed research plans. Here’s a practical example:
# Prompt for GLM-5.3: generate a market research plan
prompt = """
Create a comprehensive market research plan for a B2B technology product, including:
1. Definition of the research objective
2. Selection of method (interviews, surveys, data analysis)
3. Recommended tools (including LLMs and APIs)
4. Timeline and KPIs
"""
# Request to the model (example with API call)
response = zai_client.generate(prompt)
print(response)
"""
# Result (example):
# Objective: Assess adoption of feature X in sector Y
# Method: Online survey (n=500) + 8 in-depth interviews
# Tools: GLM-5.3 for sentiment analysis, LFM2.5-VL-3B for competitor screenshot analysis
# Timeline: 3 weeks, KPIs: response rate > 60%, NPS > 45The result is an execution-ready plan that you can directly input into a Jupyter notebook or an Azure/MLflow project.
3. Visual Analysis and Understanding with LFM2.5-VL-3B
When market research involves screenshots of websites, sales materials, or competitor screen recordings, LFM2.5-VL-3B provides real-time recognition and the ability to call external tools.
import torch
from transformers import AutoProcessor, AutoModelForVision2Seq
processor = AutoProcessor.from_pretrained("LiquidAI/LFM2.5-VL-3B")
model = AutoModelForVision2Seq.from_pretrained("LiquidAI/LFM2.5-VL-3B")
# Example: analysis of a competitor website screenshot
image = Image.open("competitor_homepage.png")
text = "Analyze key user interface elements, brand colors, and call-to-action buttons."
inputs = processor(images=image, text=text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(processor.decode(outputs[0], skip_special_tokens=True))
"""The model returns a structured description that you can store in a NoSQL database for automatic comparison over time.
4. Novo Nordisk and AWS Agentic AI: A Case Study
Novo Nordisk uses AI agents on AWS Bedrock to automate target identification and therapy hypothesis generation. The workflow is similar to what you can replicate for market research:
- Agent 1
- Agent 2
- Agent 3
The result is a fully autonomous research cycle that reduces processing time from weeks to days.
5. Practical Code Snippets: An End-to-End Workflow
Here’s a compact example that combines GLM-5.3, LFM2.5-VL-3B, and an AWS Bedrock agent for a production-ready market research pipeline.
# market_research_pipeline.py
import boto3
import json
# 1. Get the research plan from GLM-5.3
prompt = "Create a research plan for customer sentiment towards our brand."
plan = call_glm53(prompt)
# 2. Process raw data (JSON) with LFM2.5-VL-3B for visual analysis
visual_insights = []
for item in raw_data:
if 'screenshot' in item:
vis = analyze_with_lfm25(item['screenshot'])
visual_insights.append(vis)
# 3. Synthesize insights via an AI agent on AWS Bedrock
bedrock = boto3.client('bedrock-runtime', region_name='us-east-1')
payload = {
"plan": plan,
"visual": visual_insights,
"data": raw_data
}
response = bedrock.invoke_model(
modelId='anthropic.claude-v2',
body=json.dumps(payload)
)
print(response['body']['content'])
"""This script can be run on an EC2 Spot instance or an AWS Lambda job for scalability and cost savings.
6. Takeaways and Next Steps
- Start with a prompt: Use GLM-5.3 to generate a detailed research plan in seconds.
- Add the visual layer: Integrate LFM2.5-VL-3B to extract insights from competitor screenshots and images.
- Automate with agents: Combine everything with an AI agent on AWS Bedrock for an autonomous research cycle.
- Monitor and iterate: Store outputs in a time-series database to track trends over time.
Market research has never been so fast and data-driven. Experiment with GLM-5.3 and LFM2.5-VL-3B today to discover what an AI-powered workflow can do in 2026.
Conclusion
Start experimenting, tweak the prompts, and watch your market intelligence transform in real time.
**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 (August 2026):** Here are some recent trends and news stories to inspire you: - Z.ai Ships GLM-5.3 Without Retraining the Base Model: Better at Complex Coding and Long-Horizon Tasks: Z.ai released GLM-5.3 on August 14, 2026. The model reuses the 743B GLM-5.2 base unchanged. Every reported gain comes from scaled post-training: more ... [2026-08-14] - Liquid AI Releases LFM2.5-VL-3B: A 3B Vision-Language Model That Reads Screens, Grounds Objects, and Calls Tools On-Device: Liquid AI released LFM2.5-VL-3B, a 3.1B-parameter vision-language model built for on-device deployment. It averages 80.7 on ScreenSpot-v2 and lifts Re... [2026-08-13] - Google tests AMIE for clinical video consultations: Google’s research medical AI system, AMIE (Video), conducted synchronous video consultations with professional patient actors and received clinical ... [2026-08-12] Use this current information as inspiration to create an original and relevant prompt for 2026.