Product Information Management (PIM)Digital Asset Management (DAM)

Single Source of Truth Isn't Enough: What Agentic AI Needs from PIM and DAM

Christoph Könninger9/24/2026

The role PIM and DAM play in the age of agentic AI, and how to make the move to your own AI agent

Table of contents
  1. What exactly is a single source of truth?
  2. What roles do PIM and DAM play today?
  3. What can AI agents do in data maintenance?
  4. Do we still need a PIM with agentic AI?
  5. Do we still need a DAM with agentic AI?
  6. Why do PIM and DAM remain important with agentic AI?
  7. How do you make the move to agentic AI?
  8. How will agentic AI change PIM and DAM in the future?
Das Wichtigste in Kürze
  • Agentic AI does not replace PIM and DAM systems, but rather relies on their core functions to maintain a reliable single source of truth.
  • For AI agents to work autonomously and accurately, product data and digital assets must be structured, connected, and completely machine-readable.
  • Clear governance, including defined relationships, permissions, and approval workflows, is essential to prevent AI from rapidly spreading incorrect information.
  • Companies should transition to agentic AI pragmatically by optimizing specific processes step-by-step rather than completely replacing their existing system landscape.
 
 
Do we even still need Product Information Management (PIM) and Digital Asset Management (DAM) with agentic AI? My answer is: Yes, but their role is changing. As AI agents work more independently with product data, assets and content, a reliable single source of truth (SSOT) becomes more important. An agent needs to know which information is valid, how it relates to other information and what it is allowed to do with it. Unlike generative AI, agentic AI can plan and execute several steps on its own, even across different systems. Processes that currently run through PIM, DAM, CMS, interfaces and manual handovers can therefore increasingly be handled end to end.
That’s why I don’t believe agentic AI means the end of PIM and DAM. Their core functions are still needed. What’s changing is how product data, assets, content, context, permissions and processes work together and how people, systems and AI agents work with them.

What exactly is a single source of truth?

A single source of truth, also called a single point of truth, is a central source of information for people and systems. Product information has a defined source instead of being spread across different data silos and disconnected systems. Changes are made there and made available to other areas. This reduces data redundancy and creates a shared information base.
An SSOT doesn’t necessarily mean that all data physically sits in one system. What matters is that information is clearly assigned, up to date and traceable.
For agentic AI, a central data source alone isn’t enough. An AI agent needs to recognize which information is current and approved, what belongs together, which market and language version applies and which rules are in place. This requires a shared layer for structure, relationships, context, permissions and processes regardless of where the data physically lives.

What roles do PIM and DAM play today?

A PIM system brings structure to internal processes so customers receive consistent and accurate product information. It supports teams with three main tasks: collecting, managing and distributing data. This makes PIM an important part of master data management (MDM): product information is maintained centrally, structured and made available across different channels.
Solutions such as viingx or Akeneo show what a modern PIM can cover. Besides centrally managing product information, it’s about data quality, enrichment and making information available across different channels. In my previous article, you can find out more about PIM systems and how to implement them; the OMR Product Information Management category also gives you an overview of different tools.
A DAM, on the other hand, manages images, videos, PDFs and other digital assets. It handles versions, metadata, usage rights and distribution across channels. AI already supports tasks such as tagging, metadata generation and semantic search.
In short: PIM knows what a product is. DAM knows what it looks like.
In reality, things are more complicated. Product data, media, marketing content, translations, markets, channels and approvals belong together. Yet product data often lives in an ERP or PIM, images in a DAM, marketing content in a CMS and additional information in Excel. Data integration connects these sources and makes relevant information usable across systems.
For agentic AI, this separation quickly becomes a problem. An agent needs to understand what belongs together, which version is valid, which market it applies to and what it is allowed to do. The systems often hold the information but the context is still hidden in processes and employees’ knowledge.
I see this often in practice. That’s why I increasingly think in terms of connected content processes: product data, assets and marketing content need to be connected, with context that is machine-readable.

What can AI agents do in data maintenance?

Data maintenance sounds simple at first. In practice, product data must be collected, checked, enriched, translated and prepared for different channels, with approvals and quality checks along the way.
With agentic AI, individual AI helpers turn into connected workflows. An agent can pull information from different sources, act within defined rules and trigger the next step in a process.

1. Bring data together from different sources

Product data often comes from ERP, PIM, CMS, manufacturer documents, PDFs and spreadsheets. An agent can collect and compare this information and spot gaps or inconsistencies. This data consolidation creates a shared foundation for preparing product information across channels and reduces system switching.

2. Check data and spot errors

An agent can check whether required fields are complete, units are correct or product descriptions contain contradictions. Alongside fixed validation rules, it can increasingly evaluate information in context.

3. Enrich product data and create content

If information is missing or data needs to be prepared for different channels, an agent can create texts, translate them or adapt existing material. For assets, it can select suitable images, add metadata and check whether the right material is available.

4. Execute tasks independently

Based on defined rules, an agent can act independently: read product information, spot gaps, add missing data, create language versions, assign assets and move the workflow to “Review”. At a defined approval step, it stops and hands the process over to a human.

Do we still need a PIM with agentic AI?

Yes. The functions of a PIM remain relevant and may even become more important. What changes is how people and AI agents interact with them.
AI agents can take over tasks previously handled directly in the PIM: bringing data together, spotting gaps, enriching content or creating translations. They still need a clean golden record, clear relationships, status information and quality rules.
The PIM therefore becomes a reliable working foundation for people, systems and AI agents. At the same time, AI systems themselves are becoming recipients of product data. OpenAI is expanding product search in ChatGPT and agentic commerce scenarios. For manufacturers, this means product data needs to be complete, up to date and machine-readable not just for shops and marketplaces, but increasingly for AI systems too. The vendor landscape is changing as well. , for example, connects PIM and DAM with AI-powered automation. Whether these functions will ultimately live in a standalone PIM, broader platforms or across connected systems will depend on the individual company.
At viingx, that’s why we’re focusing on a Marketing Content Hub that connects PIM, DAM and headless CMS functions on a shared data and process foundation, while bringing in external systems via APIs when needed. Relationships, roles, permissions, approvals and workflows become usable for people, systems and AI agents instead of living only in individual employees’ heads.

Do we still need a DAM with agentic AI?

Yes. A DAM remains important even with agentic AI. When AI agents select and use assets independently, metadata, relationships, versions, usage rights and approvals need to be machine-readable.
Images and videos don’t become less important just because AI can search for them. It still needs to be clear which asset belongs to which product variant, which market it is approved for and whether it can be used in a given channel. Metadata, versioning, usage rights and access controls therefore remain essential.
The DAM becomes a foundation for agentic asset processes. An agent needs to understand which image fits, its context and whether it can be used.

Why do PIM and DAM remain important with agentic AI?

Agentic AI doesn’t automatically replace PIM and DAM. For me, five things are particularly important before an AI agent can act reliably: structure, relationships, rules, context and process.

1. Structure

Products, attributes, variants and other content types need an understandable data model. An agent shouldn’t have to guess which information is valid every time it runs a process.

2. Relationships and context

An image belongs to a product, a variant, a market or a campaign. Language, channel and status also need to be clearly defined. These relationships make information reliably usable for agents.

3. Rules and permissions

Not everything that can technically be found is allowed to be used or changed. Usage rights, roles and access restrictions therefore also need to apply to AI agents.

4. Approvals and boundaries

An AI agent can change data, create content or run workflows. Companies need to define which steps can run autonomously and where human approval is required.

5. Process and governance

An agent needs to know what comes next and what it is allowed to do. Its actions should also remain traceable. A Deloitte study of 3,235 business and IT leaders across 24 countries shows the gap: only 21 percent report having a mature governance model for agentic AI. (“The State of AI in the Enterprise: The Untapped Edge”, Deloitte, 2026)
As automation scales, so does the quality, or lack of quality, of its information. A human might stop when data contradicts itself and ask someone to clarify. An agent can spread the same wrong decision across multiple languages, content and channels within seconds. That makes a reliable single source of truth even more important.

How do you make the move to agentic AI?

Getting started with agentic AI doesn’t require a completely new system landscape. I would start with one specific process and clarify which information, relationships, rules and approvals it needs. McKinsey reports that eight out of ten companies see limitations in their data as an obstacle to scaling agentic AI. (“Building the foundations for agentic AI at scale”, McKinsey, 2026)

1. Choose a relevant process

Start with a clearly defined process that involves lots of manual handovers – for example, launching a new product, translating content for a new market or preparing product data for a specific channel. This quickly shows which information is actually needed and where context is missing.

2. Make information and systems agent-ready

ERP, PIM, DAM, CMS or your shop don’t all have to be replaced. What matters is having a reliable layer that brings together relevant information, relationships, rules and processes and connects external systems via APIs. People can continue working there through the GUI, systems through APIs and AI agents increasingly through interfaces such as the Model Context Protocol (MCP). viingx provides a native MCP interface for this. Agents can recognize the configured data model and approved functions and work within existing roles and permissions. Their changes remain traceable.
If you want to bring PIM and DAM together with other systems on one platform, Pimcore is one possible approach. 

3. Define context, rules and approvals

For the selected process, it should be clear which information belongs together, which version is valid, which permissions apply and which steps an agent is allowed to execute independently. With agentic AI, governance is therefore part of the working foundation from the start.

4. Test the process with an agent

An AI agent doesn’t need to run your entire company right away. A clearly defined process shows much faster where agentic AI actually takes work off your team’s plate, where the limits are and where human control still makes sense.

5. Expand autonomy step by step

Not every process step needs permanent human approval. But companies should expand autonomy deliberately: start with clear boundaries, monitor the results and then gradually widen the agent’s scope of action.

How will agentic AI change PIM and DAM in the future?

I wouldn’t create a rigid five-year plan. The market is changing too quickly. Still, the direction is becoming clearer: PIM and DAM providers are adding agentic features, while platforms such as ChatGPT are increasingly searching for products, comparing them and bringing them into commerce processes. Some day-to-day work is therefore moving from the interface into agentic workflows.
A little skepticism is still healthy. Gartner predicts that more than 40 percent of agentic AI projects will be discontinued by the end of 2027 among other things because of costs, unclear business value or insufficient risk controls. (“Gartner Predicts Over 40% of agentic AI Projects Will Be Canceled by End of 2027”, Gartner, 2025) That’s a good reason to take a pragmatic approach instead of racing to deploy as many agents as possible.
My outlook: PIM and DAM will stay, but their role will shift. People work through user interfaces, systems through APIs and agents through interfaces such as MCP. A shared content layer connects company data with assets, relationships, rules and processes and makes changes traceable. This creates infrastructure built for people and increasingly for agents.
Christoph Könninger
Author
Christoph Könninger
Product Information Management (PIM)
Digital Asset Management (DAM)

Christoph Könninger ist Director Business Development bei viingx, einem Marketing Content Hub, der PIM, DAM und Headless CMS in einer Plattform vereint. Er verantwortet den Ausbau von Kundenbeziehungen und strategischen Partnerschaften und begleitet Unternehmen bei der Digitalisierung und Transformation von Marketingprozessen. Sein Schwerpunkt liegt auf der Vernetzung und Automatisierung von Produktinformationen, Content und Prozessen – vom Daten-Onboarding bis zur Bereitstellung in Marketing- und Vertriebskanälen. Ein besonderer Fokus seiner Arbeit ist heute der Einsatz von KI und Agentic AI.

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Software mentioned in the article

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Product or service categories mentioned in the article

Product Information Management (PIM)
Product Information Management systems, also known as PIM or PIM systems, are applications with which product information is structured, managed, enriched, translated and distributed through various marketing and sales channels. The centralized, location-independent collection and management of product data can standardize and effectively execute business processes using a PIM system and operate omni-channel marketing. This is due to all information in the PIM system always being up-to-date, consistent, and complete. As a result, PIM software supports companies in disseminating high-quality, medium-neutral product information on all channels. In addition to this high data quality, the use of a product information system saves time, as many steps are eliminated through standardized processes and the distribution of information takes place more quickly. Using PIM software is worthwhile - regardless of company size - if companies have a wide range of products and need to aggregate and subsequently distribute a lot of product information from suppliers, service providers and customers through many channels. PIM systems can process countless data formats. These include marketing data (e.g., names, descriptions, and labels), media files (e.g., photos, graphics, videos, and documents), technical data (e.g., dimensions, weight and other specifications) as well as design data (e.g., sizes, colors and materials). PIM software distribute data on marketing and sales channels such as e-commerce, point of sale (PoS), websites and content management systems, apps, social media or print advertising (e.g., catalogs, labels, data sheets, flyers and manuals). Various export formats such as .XLS or .CSV are also available. PIM software often contains features of Digital Asset Management Software (DAM) and provides interfaces to CRM Systems, CMS and ERP Systems.
Digital Asset Management (DAM)
A Digital Asset Management (DAM) software allows for easy management of media files such as photos, audio, and videos. Users of Digital Asset Management software can group, search and share files with other users. Many solutions feature additional functions, such as preview and thumbnail views of assets optimized for quick processing, or the automatic conversion of files into other file types and sizes. DAM systems are often used to reduce the duplication of certain assets within companies, maintain collections, and control the security of assets with role-based permissions. Many Digital Asset Management tools can also be integrated into other management software, for example, in Content Management Systems, as well as creative software such as website builders or Content Marketing Tools.

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