AI Agent: AI Research Data Manager

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How do your teams ground innovation decisions in shared evidence, not opinions?

Part 1:How Classical Design Thinking Techniques tackle the above question [Details]

Part 2: How AI Agents Complete 5 Critical Innovation Tasks in Minutes [Details]

Part 3: Why AI Agents Matter – Efficiency & effectiveness gains [Details]

Part 4:What Large Language Models (LLMs) Power the AI Agents [Details]

Part 5: How AI Agents Operate in the Daily Workplace [Details]

Call us: Seeking further information [Details]

Part 1: How Classical Design Thinking Techniques tackle the above question

In the Discover stage of the 6D Design Thinking Execution Model (shown above), the Evidence Wall (shown below) serves as a reference tool that helps teams consolidate interview quotes, observations, survey data, and market facts in a single shared visual space. It does this by clustering sticky notes, photos, and data points into themed segments around a central question, so patterns, contradictions, and evidence gaps become easier to spot and discuss.

The Evidence Wall is good but can be messy, time‑consuming, and hard to keep up to date, so the AI Agent can help transform scattered research artifacts into a living, structured evidence base that everyone can rely on.

Part 2: How AI Agents Complete 5 Critical Innovation Tasks in Minutes

  1. The AI agent ingests raw research inputs (interview transcripts, survey results, CRM data, desk research) and automatically tags and groups them into themes aligned with the Evidence Wall segments.
  2. It extracts key quotes, customer stories, metrics, and anomalies, turning them into concise evidence snippets that can be “pinned” to the relevant section of the digital Evidence Wall.
  3. It continuously scans for converging and conflicting signals, highlighting where evidence is strong, weak, or missing, and suggesting where additional research is needed.
  4. It links each insight to its source, so teams can quickly trace claims to the underlying data and avoid debates based solely on opinion or memory.
  5. It updates and reorganizes the Evidence Wall as new data arrives, helping teams maintain a single, up‑to‑date view of evidence that supports or challenges key innovation decisions.

Part 3: Why an AI Agent Matters – Efficiency & effectiveness gains

  • Manual transcription and note extraction drop from 1–2 hours to 3–5 minutes (over 95% time savings).
  • Teams can include many more interviews and sources with minimal additional effort, improving the depth of insight.
  • Fewer important themes are missed, reducing costly blind spots during development and launch.

Part 4: What Large Language Models (LLMs) Power the AI Agents

The core processing and document‑format creation of our AI Design Thinking Agents are powered by the latest generation of OpenAI GPT large language models, delivering enterprise‑grade reasoning, precision, and consistency across all analytical and narrative outputs.

Complementing this, Google Gemini Flash Image (Nano Banana) drives high‑quality visual and image generation, enabling rapid production of clear, executive‑ready canvases, blueprints, and illustrative assets that make complex innovation insights immediately understandable and action‑oriented.

Part 5: How AI Agents Operate in the Daily Workplace

To enable leaders to drive innovation across multilingual, cross‑border operations, the AI Agents are designed for seamless integration into everyday work. They operate in a truly always‑on model, available 24 hours a day, 7 days a week, from any location through computer devices. In addition, they natively understand and analyze 10 languages, including English, Traditional Chinese (繁體中文), Simplified Chinese (簡體中文), French, German, Japanese, Spanish, Portuguese, Korean, and Polish.

Please watch the short, two‑minute video below to see how the AI Agent supports employees in solving different challenges throughout the innovation process. For English subtitles, please click the [CC] button.

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