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In a series of articles, we examine how the processes and technologies used in PIM will change due to (generative) AI. In the framework of PIM, we have determined three significant phases of AI application, and in this article, we delve deeper into the first of those phases. 

AI tools run “on the side” in the AI Relay Phase as independent tools in present workflows. By employing ChatGPT, a copywriter might create essential product summaries or in-depth articles. A perfectly personalized email for a lead might also be made by using ChatGPT by a Sales Development Representative. 

AI in Product Data Workflows 

In this article, we will focus on the Product Information Management process and how AI will influence three critical systems in the Product Data Workflow: 

  •  The ERP 
  • The PIM 
  • The e-Commerce system 

We will explore each application in detail, starting with the system where most products are created: the ERP system. 

 ERP (Enterprise Resource Planning) Systems

Enterprise Resource Planning (ERP) systems are the backbone of modern business operations, unifying various processes to modernize workflows, enhance productivity, and ensure data accuracy. ERP systems are crucial in product management in handling diverse aspects, from inventory management to project tracking. Let’s explore two key applications of Generative AI within ERP systems that revolutionize product specification drafting and internal communications. 

Drafting Product Specifications: Product specifications are often complex and require high precision. This can be time-consuming and prone to human error when done manually. With Generative AI, you can input the technical parameters of a product, and it can generate a draft specification document. For instance, if you’re manufacturing a new line of computer monitors, you might enter information like screen size, resolution, refresh rate, connectivity options, etc., and GPT-4 can generate a well-structured and easy-to-understand draft specification. You’ll still need to review and adjust this draft, but it saves much time and reduces errors in the drafting process. 

Creating Internal Communications: ERP systems often include project management and internal communication modules. Generative AI can draft regular project updates or team communication based on the data in these modules. For example, if your team is working on implementing a new product line, GPT-4 can generate a weekly project update summarising the work completed during the week, the tasks lined up for the next week, and any challenges or blocks encountered. A project manager must review and adjust this before being sent out. 

 PIM (Product Information Management) Systems 

Product Information Management (PIM) systems are vital cores coordinating the organization, enrichment, and distribution of product data in the product management landscape. PIM systems have evolved to accommodate a flow of information, bridging attributes, specifications, and descriptions, all essential for appealing customers and driving sales. This section examines the transformative potential of AI within PIM systems, focusing on attribute mapping and data enrichment as prime examples of how AI augments product management workflows. 

Attribute Mapping: AI can learn and understand the structure and required attributes of different marketplaces or channels and then help map your product attributes to these requirements. This reduces the complexity and effort of maintaining separate mappings for each channel. For example, GPT-4 could learn that your ‘Color’ attribute needs to be mapped to ‘Product Color’ for Amazon and that for Shopify as a ‘Variant Color’. 

Data Enrichment: AI can enhance your product descriptions based on attributes stored in the PIM. It can generate additional text or metadata that helps your product stand out, aids in search engine optimization, and improves the shopping experience. For example, given a list of technical specifications for a laptop, the AI could generate text explaining what these specifications mean for a non-technical customer. 

 E-commerce Platforms 

E-commerce platforms, the bustling digital marketplaces of our time, serve as the frontiers where products meet customers in the virtual state. These platforms use immense potential to shape customer experiences and drive sales, relying heavily on the precision and appeal of product data. With the blend of Generative AI, e-commerce platforms are controlled to exceed their current capabilities, introducing automation, intelligence, and enhanced engagement. This section explores the profound impact of Generative AI on e-commerce platforms, uncovering how it revolutionizes product tagging and customer interactions. 

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Product Tag Generation: Generative AI can analyze your product descriptions and attributes to suggest relevant product tags. These tags can improve searchability on your e-commerce site and help customers find what they’re looking for. For example, for a ‘red, vintage, wooden chair’, the AI could suggest tags like ‘vintage’, ‘wooden’, ‘red’, and ‘chair’. 

 Automated Responses to Product Queries: Generative AI can handle common customer queries about your products, freeing up your customer support team to address more complex issues. For example, if a customer asks, “Does this phone have wireless charging?” the AI can analyze the product attributes to provide a relevant response. 

Remember, during the “AI Relay” phase, the results from the AI must be manually transferred into the relevant system, potentially leading to errors or inconsistencies. But even in this phase, generative AI can save significant time and effort when managing complex product data across your ERP, PIM, and e-commerce systems. As integration improves in later phases, the benefits become even more remarkable. 

 All in all, we have enjoyed the process of revealing the potential of AI in Product Data Workflows. We have witnessed the transformative power of technology in enhancing efficiency and accuracy at every stage of the AI Relay Phase and in the exciting phases to come.

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