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Author's profile photo Dzmitry Mazouka

The Role of Generative AI in Product Line Development



In today’s fast paced business environment, product managers face a formidable task of building and expanding product lines that meet market demands while keeping costs in check. The dynamic nature of society, economy and business requires agile approach, with fast reaction to changes in the markets, rapid experimentation with new products, and faster production cycles. In order to achieve proficiency at this type of business, companies need to collect and manage large collections of product data, which sometimes can grow out of hand with ever increasing complexity.

Product managers are the bridge between a product’s conception and its delivery to the market. Creation of a new product requires strategic vision, market understanding and resource management skills. However, the success of a product manager also depends on the tools and systems that he uses at his job. Even simple tasks can turn into a time-wasting nightmare when the required information is scattered and difficult to find.

Generative AI can be a great assistant in this process. Its ability to systematise and find patterns in vast amounts of data can bring a lot to the table of product line development. For example:

  • Rapid prototyping – generation of complex product structures by text prompts will allow to cut the time needed when a new product line is being drafted. The structure in general does not have to be perfect, but it removes the need of manually putting together trivial components and concentrate on important ones.
  • Data-driven decision making – with additional information about customer preferences, ideas for new product lines can be generated and brainstormed.
  • Cost reduction – understanding of the entire production pipeline can optimise production costs, select better alternatives for components and find cuts in unexpected places.

What could be an example of a product line development system that employs generative AI in its functions? Let’s consider a case for rapid prototyping.

Prototyping a Product Structure

Imagine you are a product manager working in a well-established bicycle company with a long history of successful products. Your task is to develop a new line of electric bicycles. You start with designing a general product structure, but you don’t have to create it from scratch: with a click of a button, the system provides you with a bicycle product structure derived from the many products the company has released over the years. For example:

  1. Frame
  2. Wheels
    • Front Wheel
    • Rear Wheel
    • Tires
  3. Drivetrain
    • Pedals
    • Chain
  4. Steering
  5. Seating
  6. Accessories
  7. Safety Equipment
  8. Maintenance Tools
  9. Packaging and Manual

Next, you will populate the structure template with existing components. Since you don’t have to reinvent new wheels, steering or accessories, they can be reused from the products developed before. You query the system to search the database for the wheel products, and recommend the ones for driving on asphalt, the system recommends Road Bike Wheels. With the other options be:

  1. Mountain Bike Wheels
  2. Hybrid Bike Wheels
  3. BMX Wheels
  4. Touring Bike Wheels
  5. Cyclocross Bike Wheels
  6. Fat Bike Wheels
  7. Track Bike Wheels

In the same way, you describe what type of products you would like to use at any level of the hierarchy or ask the system to prefill it with a typical configuration.

After this is done, next step is to work on the electrical equipment. New frame and drivetrain will have to be developed to support this product. Those components cannot be preselected, so you enter them manually.

The final touch is to ask the system to validate your structure and find any potential issues or improvements. For example, the configuration that you entered has incompatible forks for the specific wheels that you selected. The system will point out that fact and recommend a replacement.


In conclusion, Generative AI holds immense potential for revolutionizing product line development. It can automate numerous mundane tasks, significantly reducing development time and costs. The emerging business paradigm will incorporate the construction of Large Language Models, serving as extensive business knowledge bases, in conjunction with databases storing product or operational data. This capability promises to catalyse a qualitative leap forward not only for individual businesses but also for the broader economy. Harnessing the power of Generative AI could well be the key to unlocking unprecedented levels of efficiency and innovation.


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