Why AI is further strengthening the role of business systems like PIM

The rapid advancements in the field of AI are causing more and more business decision-makers to wonder: Is it even still worth investing in enterprise software today, or will AI completely replace traditional solutions in the future? This question seems justified when considering what is already possible with generative AI and Agentic AI. On the other hand, this is contrasted by usually complex selection and implementation projects for a wide variety of software solutions that must be integrated and orchestrated across the entire company.

In a corporate context, Agentic AI, in particular, promises a whole range of business benefits. Instead of navigating through complex software interfaces, users could, for example, simply tell an AI agent what goal they wish to achieve. The AI could then retrieve information, analyze it, interact with various applications, and even execute entire workflows autonomously. If this is already possible today, why should companies invest in the underlying enterprise software at all?

Will PIM systems be replaced by AI?

In fact, the role of PIM is only being strengthened by the increasingly extensive use of AI in the corporate environment. AI requires trustworthy data, business context, rules, defined workflows, and clear roles and permissions to achieve effective and precise results.

Traditional enterprise applications such as ERP, CRM, PIM, MDM, or DAM store and structure business-critical information, enforce rules and permissions, manage transactions, orchestrate processes, and provide interfaces through which humans interact with these functions.

As an intelligence and interaction layer over these existing enterprise systems, AI will increasingly contribute to ensuring that cross-system processes are managed and executed efficiently, with guaranteed quality, and in a resource-saving manner. Consequently, users may increasingly interact with AI agents rather than navigating directly within individual software solutions, manually entering data into forms, or switching between various applications. Enterprise applications will therefore move more and more into the background—but they will not lose their importance.

However, these changes in software interaction will clearly transform traditional solutions. User interfaces will become increasingly less important, while the focus will shift toward providing central corporate data—such as product, customer, or image data—as well as business and quality rules for target systems, users, and AI tools.

In the following, we outline the essential reasons why AI will not replace PIM systems.

Reason 1: AI needs reliable systems of record

One of the most significant limitations of Large Language Models (LLMs), which usually form the foundation for Agentic AI, is simultaneously one of the strongest arguments for enterprise software. PIM systems are deterministic systems of record. Data queries lead to unambiguous, precise answers. AI models are probabilistic systems that achieve business results based on structured data that are most likely to fulfill the predefined process goal. This is an outstanding quality for creating product descriptions, translations, or attribute assignments—but for identifying unambiguous information such as the permitted voltage of a specific product, the current stock level of an SKU, or the contractually agreed price of a customer order, this characteristic can quickly lead to problems.

Suppose products without regulatory approval must not be published. PIM provides a clear business logic:

Regulatory approval = false  Publication blocked

An AI cannot offer added value here by interpreting, based on available information, that a product likely fulfills the requirements or not—and in case of doubt, assessing the situation differently with multiple queries.

Nevertheless, AI can play a valuable role. It can analyze the missing approval and explain why publication was blocked. It can identify the responsible employee and initiate a new approval workflow.

The same principle applies to financial transactions, access permissions, compliance requirements, pricing rules, approval limits, and numerous other business processes.

Reason 2: AI needs structured data and business context

For AI to be able to control and support central business processes at all without falsifying results, it depends on a perfect data foundation. Structured product information, strong data models, clear taxonomies, relationships, and metadata are the foundation for the most effective possible further processing of this information by an AI system.

Which image belongs to which product? Which products are variants of another product? Which spare parts and accessories belong to it? Which supplier provides a specific component? Which product attributes are mandatory for a specific marketplace? Which digital asset is approved for which country? These rules are essential for the use of product information in marketing, sales, and customer communication. And all these rules are documented in the PIM.

The better this underlying structure is, the better conclusions the AI can draw about the company, its products, and customers. PIM thus increasingly provides the machine-readable business context that AI needs to work effectively.

Reason 3: Enterprise software creates the necessary governance and control

With the increasing autonomy of AI, clear guidelines are becoming more and more important. Unintended price changes, the publication of incorrect product information, or sending confidential data to the wrong recipients are major risks in the corporate context.

Agentic AI therefore needs clear boundaries regarding what information the AI is allowed to access, which decisions agents are permitted to make, and which actions they are authorized to carry out. Enterprise systems like PIM already provide many of these mechanisms today via roles, permissions, workflows, approval processes, audit trails, and governance rules.

Thus, if companies want to integrate autonomous AI into their business processes, they need a strong, reliable, and seamless governance infrastructure with clear escalation rules—for example, to handle exceptions that the AI cannot process—and with validation and approval processes that incorporate responsible employees into the workflows, thereby ensuring data quality and compliance.

What is changing: Interoperability is becoming increasingly important

Many business applications are still primarily designed today for humans to interact with them directly. Users open the application, navigate through its interface, and perform tasks. With Agentic AI, this is changing fundamentally. The idea is that the AI system receives a specific business goal. This could be, for example: “Analyze the sales figures for Product A and optimize the product content in the online shop to increase the conversion rate.” The AI must consequently interact with multiple systems in the background, for instance, retrieving customer information from a CRM, checking stock in the ERP, analyzing product performance in the online shop, and updating product content in the PIM independently.

For this to work, enterprise applications must make their functions available in a machine-readable way. APIs, event architectures, semantic layers, and new standards for communication between agents and systems (Machine Communication Protocol, or MCP) are therefore becoming increasingly important.

Therefore, it can be assumed that the competitive advantage of PIM systems in the future will depend less on the number of their functions or the views in the user interface, and much more on the quality of their data models, their business logic, as well as their APIs and MCPs.

How AI can increase the value of PIM

Historically, the value of a business application like PIM was largely determined by what human users could accomplish within the solution. In an agentic environment, its value depends much more on what humans and AI agents can achieve with the functions and data that this application provides—and that is independent of the processes and user guidance within the solution itself. Several system characteristics are therefore gaining more and more importance:

• Provision of high-quality, structured data: AI requires a reliable business context to optimally support business goals and deliver precise results. 
  
  
• Flexible data models and semantics: The relationship between data objects is essential to clearly understand contexts and use them for decisions and product communication. 
  
  
• Powerful APIs and interoperability: Agentic AI only generates maximum added value when processes can also be controlled across different systems. 
  
  
• Governance and permissions: Without clearly defined guidelines, the risk increases due to autonomous decision-making processes. Control mechanisms ensure compliance and data quality. 
  
  
• Deterministic business rules: Critical processes must not be based solely on probabilistic reasoning – they require clear rules that can only be interpreted in one way.

What AI will actually replace

A valid question remains, however, as to which parts of today's enterprise software stack AI will make redundant in the future. Repetitive manual interactions, rigid navigation structures, simple search interfaces, manual data entry, basic reporting tasks, and workflows where employees currently have to transfer information between different systems will, in all likelihood, very soon belong to the past in many corporate realities.

For PIM teams, this means that in the future they will work less and less directly in the system, but rather interact with an AI agent that will carry out these tasks (partially) autonomously. This will fundamentally change their way of working, and they will increasingly slip into the role of true data stewards, whose responsibility lies in ensuring the quality of product data and business rules.

Conclusion: PIM is changing – but its importance to companies remains

AI is undoubtedly changing enterprise technology, but it does not weaken its role in business success. AI offers efficient reasoning, interpretation, and a fundamentally easier way to interact with technology. Enterprise systems provide trusted data, structures, business rules, governance, and the ability to execute real business processes reliably.

Deterministic systems such as PIM, ERP, MDM, DAM, and CRM will focus more and more on providing quality-assured data, relational knowledge, and business logic, while AI agents will orchestrate central processes across systems and interact directly with users, neighboring systems, data recipients, and applications.

User interfaces will be unified and increasingly intelligent and intuitive – workflows will become more autonomous and the underlying systems less visible. However, their data, logic, and governance will become more important than ever.

Frequently Asked Questions

Will AI replace PIM systems?

AI can automate many PIM tasks, including product data onboarding, data enrichment, classification, quality controls, and content generation. However, these functions still require structured product data, data models, relationships, channel requirements, governance, and authoritative data sets. AI will therefore increase the importance of a strong PIM foundation rather than eliminate it.

Will AI agents replace business applications?

AI agents could replace part of the direct interaction with business applications. Users could increasingly define a desired outcome, while agents determine which systems and processes are required to achieve this goal. The underlying applications may become less visible as a result, without becoming redundant.

How will agentic AI change enterprise software?

Agentic AI will shift enterprise software from application-centric interaction to outcome-oriented workflows. Applications will increasingly provide their data, business logic, and actions to AI agents via APIs and other machine-readable interfaces, while agents will coordinate work across multiple systems.

Why does AI need enterprise data?

AI models possess general knowledge, but they do not automatically know the current state of an individual company. Enterprise data provides information on products, customers, suppliers, transactions, assets, relationships, and the business context required for relevant and reliable results.

Does AI make data management more important?

Yes. The more companies use AI for important decisions and processes, the more important the quality, structure, accessibility, and governance of enterprise data become. Poor data can limit the performance of AI, while trusted and well-structured data forms the foundation for reliable enterprise AI.

Author | The Yellow Selection Editorial Team