Background and initial situation

A multinational technology company developing innovative hardware and software solutions was facing challenges in product development and research. The team needed quick access to extensive data analysis, academic studies and market research results in order to make informed decisions. Manual research and data analysis was time-consuming and often led to delays in the development process.

Aims of the project

The main objective of the project was to increase the efficiency and accuracy of product development and research processes by providing automated data analysis and instant access to relevant information. Specific objectives included:

  • Reducing time spent on data research and analysis.
  • Improving the quality and relevance of research results.
  • Supporting the team in making data-driven decisions.
  • Relieving the research teams of administrative tasks.

Solution implementation

To achieve these goals, the AI was integrated into the company’s existing research and development environments. The implementation process followed a structured plan:

a) Needs analysis and planning:
  • Identification of information needs: Analyzing the most common information requests and data requirements through feedback and team workshops.
  • Determination of integration points: Relevant systems, including data analysis tools (e.g. Tableau) and research platforms (e.g. SciFinder), were determined.
b) Development and integration:
  • Creation of the interview workflows: Development of specific workflows for data analysis, research and provision of research results.
  • Technical integration: The AI was seamlessly integrated into the data analysis tools and research platforms.
c) Training and knowledge database:
  • Data import and structuring: Historical research data and relevant studies were used to expand the AI knowledge base.
  • Regular updates: Planning regular updates to the knowledge base to ensure the relevance and accuracy of the information provided.

Application example and process scenario

Scenario: Support with data analysis for a new product

  • Step 1: A product developer makes a request for data analysis via the internal chat system.
    – Product developer: “AI, can you analyze the latest market research results on the acceptance of wearable technology?”
  • Step 2: The AI processes the request and searches the research platforms for current studies and data.
    – AI: “One moment please, I’m checking the latest data on the acceptance of wearable technology.”
  • Step 3: The AI retrieves relevant data and carries out an analysis.
    – AI: “The latest market research results show an increasing acceptance of wearable technology, especially in the 18-35 age group. Important factors are ease of use and health monitoring. Would you like to see a detailed analysis or specific study results?”
  • Step 4: The product developer decides on a detailed analysis.
    – Product developer: “Yes, please perform a detailed analysis of the age group data.”
  • Step 5: The AI provides the detailed analysis and relevant study results.
    – AI: “Here are the detailed results: In the 18-25 age group, 80% of respondents show interest in wearables for fitness and health. In the 26-35 age group, the focus is more on professional applications and ergonomics. Here are also the references to the specific studies that support this data.”
  • Step 6: The product developer uses the information provided for further product development.
    – Product developer: “Thank you very much! That will help us a lot in the development of our new product.”

Results and benefits

Following the implementation of AI, there were significant improvements in product development and research:

  • Reduced research time: The average time for data research and analysis was reduced by 50%.
  • Improved decision quality: Teams were able to access up-to-date and relevant data, leading to better-informed decisions.
  • Increased efficiency: Researchers and developers were able to focus on creative and innovative tasks as data analysis was automated.
  • Faster time to market: Accelerated data sourcing and analysis shortened the time to market for new products.

Conclusion

The integration of AI into the company’s product development and research processes led to a significant increase in the efficiency and quality of research work. By automating data analysis and providing up-to-date information, valuable time was freed up and decision-making was significantly improved. Future enhancements could include the implementation of additional analysis functions and the integration of further knowledge sources to further optimize development processes.