AI in omnichannel marketing: Why the tools aren’t the problem – it’s the integration

AI has long since made its mark in marketing – at least on the surface. In many retail companies, tools for product descriptions, translations or simple image generation are now part of everyday life. But this is precisely where the real challenge begins: using AI is no longer a competitive advantage, but the norm.

What sounds like a simple ‘AI transformation’ at conferences often feels more like fragmentation within the company. Teams work with different tools, results are difficult to compare, and instead of efficiency gains, new coordination efforts arise. At the same time, the pressure is growing to manage more and more channels – faster, more personalised and with fewer resources.

The real challenge, therefore, is not the use of AI itself, but rather the question: how can AI be effectively integrated with existing systems and processes? This is precisely where leading retail companies are focusing their efforts – building not isolated solutions, but integrated, AI-powered content ecosystems.

Marketing teams are facing a new level of complexity: which AI tools are actually useful? Which ones can be seamlessly integrated with existing PIM and DAM systems? And how do you prevent ten individual AI solutions from quickly turning into an unmanageable proliferation of tools? Added to this are very practical questions that are often underestimated in day-to-day operations: licensing models, API costs, usage limits, data protection requirements and integration into existing workflows.

Which AI tools do marketing planners really need today?

The question is no longer whether AI is used – but where it has the greatest impact within the process. Because the key point is this: value is not created by individual tools, but through their interaction.

Rather than listing the results, it is worth looking ahead: what would the same process look like if AI were properly orchestrated?

Orchestrated target process (simplified):

1. Trigger in PIM: new or modified product (e.g. price, product range, promotion)

2. Rule-based enrichment: Attributes are checked, missing data automatically supplemented (taxonomies, mandatory fields)

3. AI generation (context-based):

• • • Product texts per channel (e-commerce, brochure, social media)
    • Image variants from existing assets (e.g. scenes, backgrounds)
    • Claims/USPs per target group

     

4. Versioning in DAM:

All assets are stored in a structured manner (including metadata, variants, language versions)

5. Template-based advertising material production:
Brochure pages, banners and newsletters are automatically populated from templates

6. Channel distribution & feedback:

• • • Publishing in shop, print and social media
    • Performance data is fed back (closed loop)

     

The difference is not ‘more AI’, but clear orchestration along a consistent data and process model.

What this means in practical terms for marketing planners

The real change is taking place within existing omnichannel systems – not alongside them.

Where automated processes already exist (e.g. template-based brochure production, rule-based management of product ranges), AI can provide targeted reinforcement:

1. Context rather than individual functions

• AI accesses PIM data directly (attributes, prices, promotions)
• Generation is channel-specific (not ‘one text for all’)
• Brand and campaign rules are taken into account by the system

2. Automated variant creation

• Multiple display variants are automatically generated from a single product
• Differences by channel, region, target group
• A/B testing logic can be implemented directly within the system

3. Closed feedback loops

• Performance data (e.g. CTR, sales) is fed back
• AI iteratively adapts content (claims, image selection, prioritisation)
• Decisions are data-driven rather than “gut-driven”

4. Governance & scaling

• Centralised rules prevent uncontrolled growth (approvals, versions, rights)
• Traceability (who generated/adapted what and when?)
• Scaling across product ranges and countries