The evolution and promises of agentic PIM, illustrated by ATAMYA
The PIM market is undergoing significant change. Growing channel complexity, increasingly demanding customer expectations, the rise of social commerce, pressure on margins and supply chains, and rapid advances in artificial intelligence are changing how companies create, manage, and distribute product information. At the same time, businesses need to respond to new markets, channels, data requirements, and technologies faster than traditional system landscapes often allow.
ATAMYA was created in response to this changing environment. Rather than continuing to evolve the existing and well-established PIM platform eggheads, the company took a greenfield approach to PIM, designing a new platform around modern architectural principles and the requirements of business users. The result is the ATAMYA Product Cloud, a cloud-native platform designed to combine flexibility, scalability, integration, and ease of use.
After cloud-native, API-first, and SaaS, artificial intelligence is the one trend shaping the market’s current discussions. Assisting, generative, or agentic: AI has entered the software communication space just as it has entered board meetings in companies across the industries and regions.
Given the rapid evolution of AI technology, the massive funding of AI-centered companies, and the widespread adoption of consumer-facing AI models, the hype is not hard to explain. However, companies have yet to figure out ways to generate tangible business value from their AI implementations.
In PIM, most of today’s AI advancements are centered around task automation such as the generation of marketing copies, translations, and supplier data onboarding. While these efforts may increase the PIM team’s productivity and help reduce the time-to-market for new products, direct attributions to an organization’s core business KPIs are difficult to make.
Additionally, successful implementation of AI enhancements in data management workflows typically requires considerable preparation efforts in order to provide the appropriate data quality, rule sets, and definitions of responsibility and accountability – for both the AI tools and the humans in the loop.
Without these guardrails, the risk of underperformance increases significantly. Incomplete product data can lead to more AI hallucinations, the lack of clear business rules can lead to accidental publication of non-compliant content, and with no defined roles, it becomes very difficult to create workflows that are secure and efficient.
For companies, it becomes increasingly important to fix these problems as quickly as possible. AI is evolving at an ever-increasing pace, and more and more business leaders are starting to evaluate the potentials of agentic AI for their business operations. Without a structured and governed data foundation, however, the agentification of business operations can quickly lead to cascading issues and business risks instead of the expected benefits.
Later in this analyst guide, we will take a closer look at how businesses can make sure to prepare comprehensively for the implementation of agentic AI in their core workflows. First, we will dive a little deeper into the meaning of agentification in PIM and its potential benefits for companies across the industries.
