A Journey from ERP to PIM to E-Commerce
In our ongoing article series, we’re exploring how AI transforms Product Information Management (PIM). In our previous installments, we looked at which parts of the PIM process will be impacted through generative AI. This article will deeply dive into the progressive stages of AI application within (Product Data) tools and workflows.
While many of us have experimented with ChatGPT and various open-source models, transforming that excitement into practical, real-world products can be challenging. Creating chatbots and having them interact with your data and documents is a logical starting point, see also this article. However, we believe that AI holds the power to reshape Product Data Workflow and Product Information Management. This evolution will unfold across three key phases, which we’ll explore below.
Phase 1: Independent Generative AI Tools – The ‘AI Relay’ Era
In the primary phase, generative AI tools are separated from the central systems that manage and publish product data. In this case, data is manually transmitted from one system to another.
At the beginning of this workflow, product data is stored in an ERP (Enterprise Resource Planning) system, which handles data related to inventory, pricing, and SKU details. Afterwards, this data is manually transferred to a generative AI tool to create engaging product descriptions. After which the generated descriptions are manually moved to the PIM (Product Information Management) system, which acts as the central hub for product data, which then publishes the products on the e-commerce platform and other output channels. Through this process you benefit from AI directly without changing your workflow.The required manual effort for data transfer between systems can potentially lead to data inconsistencies and even though the descriptions are generated automatically, the manual transfer and context switching takes valuable time.
Phase 2: Integrated AI Features – The ‘AI Bridge’ Era
As we approach the following stage, we observe the smooth integration of generative AI elements into already-existing technologies.
This represents a change from separate AI tools to a smooth integration in the user experience. The true strength comes from the ability of ERP and PIM systems to interact directly with built-in AI features. For example, the ERP can use a built-in AI to categorize products and cleanse inconsistencies from the Master Data. The PIM system’s AI tool may then automatically create product descriptions and sets of goods. There is no need for manual data copy-pasting as the enriched data can be automatically provided into the e-commerce platform and other output channels. This helps reduce human-made mistakes while significantly improving process uniformity and efficiency.
Phase 3: AI-First Systems – The ‘AI Highway’ Era
The final phase is characterized by a complete overhaul of the existing system, where generative AI is embedded at every step of the workflow – a transition from ‘data bridges’ to ‘data highways’. The systems are built from the ground up with generative AI at the core, enabling a seamless flow of data.
In this scenario, the ERP system feeds product data into the PIM system, where the integrated AI not only generates descriptions and categorizes products but also predicts optimal pricing and sales performance. This enriched and insightful data is then automatically shared with the e-commerce platform and other output channels, ensuring consistent, real-time data availability across all platforms.
Furthermore, the AI system continually learns and develops based on feedback, user interactions, and incoming data. The customer experience is improved across all output channels, and it may even provide personalized product suggestions and modify product descriptions to fit specific consumer preferences.
Overall, we have explored the stages of artificial intelligence deployment in Product Information Management (PIM), which offer a roadmap for leveraging AI’s benefits. The ‘AI Relay’ phase to the ‘AI Highway’ phase represents a process to an optimized and AI-driven future.
These phases will be discussed in our upcoming deep dives, revealing their nuances and benefits. Through a deep dive into AI, we’ll examine how it can simplify workflows, improve efficiency, and enhance the user experience through consistent integration.