AI in marketing: How collaboration tools are reinventing retail – and why silos must now be eliminated

The invisible revolution in retail

The retail sector is in the midst of a fundamental transformation – and this is not being driven by new products or sales channels, but by the way teams work together. Whereas departments such as marketing, sales and IT used to operate in isolated silos, today’s omnichannel reality demands seamless, data-driven collaboration. But how can this transformation be achieved? The answer lies in a combination of collaboration tools, artificial intelligence (AI) and well-planned change management.

According to the ‘State of AI in Retail 2026’ study, 82 per cent of leading retail companies in the DACH region are already using AI-powered tools to optimise their product data, marketing campaigns and team processes. At the same time, a recent survey shows that 63 per cent of companies implementing Product Information Management (PIM) or Digital Asset Management (DAM) systems fail without accompanying change management. The reason: technology alone does not solve cultural or structural problems.

In this article, we explore the following questions:

  • How PIM and DAM systems act as the technological foundation for smart omnichannel strategies – and where their limitations lie.
  • Which specific AI applications are already revolutionising collaboration within marketing teams.
  • Why change management is the decisive factor in breaking down silos and establishing a data-driven culture.
  • Which future trends will shape the retail sector in the coming years.

The technological foundation – PIM and DAM as the backbone of omnichannel retail

PIM systems: More than just a product database

Product Information Management (PIM) is no longer a niche topic, but a necessity for any retailer operating across multiple channels. But what exactly do modern PIM systems do – and what technical and organisational challenges need to be overcome?

Core functions of PIM systems

PIM systems enable companies to manage all product-related information in one central location and to make it available consistently across all channels. This includes:
      • Centralised data management: All product information – from descriptions and technical specifications to translations – is managed in a single, unified database. This avoids duplication and ensures that all departments have access to the same, up-to-date information.
      • Cross-channel distribution: Product data is automatically distributed to various sales channels (online shops, marketplaces, print catalogues, POS systems). This reduces manual errors and speeds up the time-to-market for new products.
      • Data enrichment: Through the integration of AI and machine learning, missing attributes (e.g. weight, material, certifications) can be automatically detected and added. Furthermore, validation rules help to identify inconsistencies (e.g. incorrect units or formatting).
      • Workflow management: Approval processes for product data (e.g. the dual-control principle for price changes or new product descriptions) ensure transparency and quality assurance.
      • Integration with other systems: PIM systems can be seamlessly integrated into ERP, CRM or e-commerce platforms to automate data flows and break down silos.
       
 

Challenges in implementing PIM systems

Despite the many benefits, there are common pitfalls that companies need to be aware of:
      • Data quality: Many companies underestimate the effort involved in cleaning up and standardising their existing product data. Without a clean database, a PIM system cannot realise its full potential.
      • Data migration: Transferring data from legacy systems (e.g. Excel, outdated databases) into a new PIM system is often complex and prone to errors. Phased migration strategies and data mapping tools can help here.
      • Staff acceptance: Without training and change management, new systems often meet with resistance. It is particularly important to highlight the benefits for day-to-day work (e.g. fewer manual entries, faster processes).
      • Scalability: PIM systems must keep pace with the company’s growth. It is crucial to opt for a flexible and expandable architecture right from the start.
       
 

DAM systems: the unsung heroes of content marketing

Whilst PIM systems manage structural product data, Digital Asset Management (DAM) systems are responsible for visual and media content. However, modern DAMs can do far more than simply store images:

What modern DAM systems can do

 
      • Centralised storage and management: All images, videos, PDFs, audio files and other media are collected in one place. This makes it easier to search for, reuse and update assets.
      • Intelligent metadata management: AI-powered analysis enables metadata (e.g. tags, categories, descriptions) to be automatically generated or supplemented. This allows assets to be found more quickly and used in a targeted manner.
      • Versioning and rights management: DAM systems manage different versions of an asset (e.g. different image sizes or language variants) and ensure that only licensed and up-to-date content is used.
      • Collaborative workflows: Teams can comment on, edit and approve assets directly within the system – without the hassle of email forwarding. This speeds up creative processes and reduces communication errors.
      • Integration with other systems: DAM systems can be connected to PIM, CMS or e-commerce platforms to enable seamless workflows.
       
 

Challenges in implementing DAM systems

 
      • Data chaos: Many companies have assets scattered across various servers, cloud storage platforms or local hard drives. Consolidating and standardising this data is often a time-consuming process.
      • User-friendliness: DAM systems must be intuitive to use; otherwise, they will not be adopted by teams. Simple search functions and clear access control structures are particularly important.
      • Scalability and performance: When dealing with large volumes of data (e.g. tens of thousands of images), DAM systems must remain fast and reliable. Cloud-based solutions often have an advantage here.
      • Compliance and data protection: Particularly when dealing with personal data (e.g. photos containing faces) or licensed content, companies must ensure that their DAM systems are GDPR-compliant.
       
 

AI as a game-changer – How algorithms are redefining marketing collaboration

 

Generative AI: From content bottlenecks to scaling

One of the biggest bottlenecks in marketing is content production. This is where generative AI comes in – and changes the rules of the game. But how does it work in practice, and what are its limitations?

Use Cases for Generative AI in Marketing

Generative AI can save time and boost creativity in various areas of marketing:

  • Product descriptions: AI generates SEO-optimised text based on product data, keywords and predefined tone guidelines. This takes the pressure off copywriters and enables the rapid creation of large volumes of content (e.g. for extensive product catalogues).
  • Social media content: AI helps create post templates, hashtag suggestions and captions. This is particularly useful for regular posts (e.g. daily social media updates) or localised content for different markets.
  • Email campaigns: AI generates personalised subject lines and content that boost open and click-through rates. Through A/B testing, the AI can learn which phrasing resonates best with the target audience.
  • Translations: AI translates product data, marketing copy or websites into various languages – contextually aware and, where appropriate, automatically. This is a game-changer, particularly for international retailers.

Challenges and limitations of generative AI

Despite its many advantages, there are also challenges that businesses need to be aware of:
      • Quality versus quantity: AI can produce vast amounts of content, but it often lacks the human touch (e.g. emotion, storytelling, brand voice). The solution: hybrid workflows, in which AI creates rough drafts that are then edited and refined by humans.
      • Brand voice and consistency: AI must be trained to match a brand’s tone and style. This requires sample data and feedback loops to improve the AI.
      • Ethical and legal issues: Who is liable for inaccurate or discriminatory content generated by AI? How can one ensure that copyright is not infringed? Clear guidelines and human oversight are essential here.
       
 

Predictive Analytics: Data-driven decisions in real time

AI enables marketing teams not only to react, but to act proactively. Through predictive analytics, businesses can forecast trends, minimise risks and capitalise on opportunities – in real time.

How AI is revolutionising marketing decisions

 

  • Demand Forecasting: AI analyses historical sales data, market trends and external factors (e.g. weather, public holidays, economic developments) to generate sales forecasts. This helps companies optimise their stock levels and make their supply chains more efficient.
  • Dynamic pricing: AI adjusts prices in real time – based on competitor data, demand and stock levels. This maximises turnover and margins without deterring customers with excessively high prices.
  • Personalised recommendations: AI generates individual product suggestions for each customer – not only in the online shop, but also in emails, social media adverts and chatbots. This boosts conversion rates and customer satisfaction.
  • Customer Lifetime Value (CLV) prediction: AI identifies high-potential customers and suggests tailored marketing measures to increase their loyalty and value.

Example: Predictive analytics in practice

A major electronics retailer uses AI-powered algorithms to:

  1. Segment customers in real time (e.g. based on purchasing behaviour, demographics and interactions with the brand).
  2. Display personalised product recommendations on the homepage, in emails and in retargeting campaigns.
  3. Increase the conversion rate by 12 per cent – whilst reducing marketing costs by 8 per cent through more targeted marketing.

AI in PIM and DAM: Automation taken to the next level

Modern PIM and DAM systems are increasingly integrating AI to improve data quality, search functions and workflows. But how exactly does this work – and what specific benefits do these technologies offer?

AI features in PIM systems

 

  • Automatic data enrichment: AI identifies missing attributes (e.g. weight, material, certifications) and suggests appropriate values. This reduces the manual effort required for data maintenance and improves the completeness of product information.
  • Data validation: AI checks the consistency and plausibility of product data (e.g. whether a price is in the correct format or whether units are consistent). This minimises errors in catalogues or online shops.
  • Semantic search: Rather than searching only for exact terms, AI understands meanings and contexts (e.g. ‘laptop’ = ‘notebook’ or ‘smartphone’ = ‘mobile phone’). This makes searching for products or data significantly more efficient.
  • Predictive data management: AI analyses sales data and market trends to make predictions about future product requirements. This enables companies to proactively adapt their product data (e.g. by adding new attributes for emerging trends).

 

AI Features in DAM Systems

 

  • Image and Video Analysis: AI recognises objects, faces, text and emotions in images or videos. This enables the automatic tagging and classification of assets – without the need for manual intervention.
  • Automatic Metadata Generation: AI suggests tags, categories and descriptions for uploaded assets. This makes it easier to search for and reuse content.
  • Content moderation: AI detects inappropriate or copyright-protected content and flags or blocks it. This reduces legal risks and ensures that only compliant content is published.
  • Personalised asset recommendations: AI suggests suitable images or videos for specific campaigns or target audiences – based on user behaviour and preferences.

Change Management – The Human Factor in Digital Transformation

 

Why technology alone is not enough: The psychological barriers

The introduction of PIM, DAM or AI tools often fails not because of the technology, but because of the human factor. The biggest hurdles – and how to overcome them:

The most common barriers and their causes

 

Barrier Cause Solution
Fear of job loss  Employees fear being replaced by AI or automation.  Transparent communication: Emphasise that AI supports, not replaces. Highlight how new tools take over tedious routine tasks so that teams can focus on strategic and creative tasks.
Habits & convenience “That’s how we’ve always done it.”  Pilot projects: Start with a small, motivated group to test the tool and gather success stories. This allows you to demonstrate concrete benefits (e.g. time savings, fewer errors).
Lack of digital literacy Employees feel overwhelmed by the new technology.  Training & mentoring: Offer step-by-step training – from the basics to advanced features. Practical exercises and one-to-one coaching are particularly effective.
Scepticism about data  “Our data is too unstructured for AI.”  Data cleansing as a first step: Show how AI improves data quality (e.g. through automatic validation or enrichment).
Lack of vision  Staff do not understand why the new tool is being introduced.  Communicate clear objectives: Explain which specific problems the tool solves (e.g. less manual work, faster time-to-market) and how each individual will benefit.

Fact: According to a recent study, 70 per cent of digitalisation projects fail due to a lack of acceptance amongst staff. The key to success: involving staff from the outset and delivering visible quick wins.

Agile methods for introducing new tools

Traditional top-down approaches often fail when introducing collaboration tools. Instead, successful companies rely on agile methods that promote flexibility, transparency and teamwork.

Scrum for tool roll-out

Scrum is an agile project management method that is particularly well-suited to the rapid and iterative roll-out of new tools:

  • Sprints: The roll-out is divided into two-week phases (sprints). Each sprint has a clear objective (e.g. data migration in Sprint 1, training in Sprint 2).
  • Daily stand-ups: Short daily meetings (15–30 minutes) in which the team discusses progress, obstacles and next steps.
  • Retrospectives: After each sprint, feedback is gathered and the process is adjusted. This allows problems to be identified early on and solutions to be developed collaboratively.
Collaboration Team scrum

Image by vectorjuice on Magnific

Kanban for continuous improvement

Kanban is a visual project management method that is particularly well-suited to long-term optimisation processes:

  • Visualisation: All tasks (e.g. data cleansing, training sessions, tool customisations) are displayed on a Kanban board (e.g. in Trello, Jira or Asana).
  • Work-in-Progress (WIP) limits: Only a limited number of tasks are worked on at any one time to avoid overload and improve focus.
  • Continuous flow: New requirements (e.g. additional AI features) are integrated flexibly without interrupting the entire process.
Collaboration Team kanban

Image by vectorjuice on Magnific

Measuring success: KPIs for collaboration tools

To measure the return on investment (ROI) of PIM, DAM or AI tools, companies should define specific KPIs. Here are the key metrics:

KPI Measurement method Benefits
Time-to-Market The time from product concept to launch across all channels. Faster response to market changes.
Data quality  Number of missing or incorrect product data entries (e.g. incomplete attributes, incorrect units).  Fewer customer enquiries, higher conversion rate.
Team productivity  Time saved on routine tasks (e.g. data maintenance, content creation).  More time for strategic tasks.
Customer satisfaction  Reduction in complaints due to incorrect product information or inconsistent content. Greater customer loyalty and fewer returns.
Conversion rate Increase in sales through personalised content (e.g. AI-powered product recommendations).   Higher turnover with the same marketing costs.
User adoption Number of active users of the tool relative to the total number of staff. High adoption rates and efficient use.

 

“The biggest challenge in digitalisation is not the technology, but the culture. Companies that involve their staff from the outset and show them how they can benefit from the new tools are the most successful.” Dr Anna Berger, digitalisation expert (2026)

 

Future trends – What’s next

 

Generative AI: From text to multimodal content creation

The next stage of generative AI will be multimodal – combining text, images, audio and video. This opens up entirely new possibilities for marketing:

  • AI-generated product videos: Tools create realistic videos featuring avatars that explain or demonstrate products. This saves on the costs of professional video productions and enables quick adaptations for different target audiences.
  • Automatic image editing: AI automatically adapts images for different channels (e.g. removing backgrounds, resizing, colour corrections). This speeds up content production for social media, websites or print.
  • Voice & audio: AI generates natural-sounding voice-overs for podcasts, audio guides or voice assistants. This enables personalised audio content for different target audiences.
  • Interactive content: AI creates dynamic content that adapts in real time to user interactions (e.g. personalised product catalogues or interactive chatbots).

Blockchain for transparent supply chains and product data

Blockchain technology could revolutionise the reliability of product data – particularly in sectors with complex supply chains (e.g. food, fashion, electronics):

  • Traceability: Customers can view a product’s entire life cycle (from manufacture to sale). This strengthens trust in the brand and enables more sustainable decisions.
  • Counterfeit protection: Blockchain prevents tampering with product data (e.g. for luxury goods or certifications). This is particularly relevant for brands at high risk of counterfeiting (e.g. designer clothing, electronics).
  • Smart contracts: Automated contracts between retailers, suppliers and logistics partners that are triggered automatically when certain conditions (e.g. delivery, payment) are met. This reduces administrative costs and human error.

Metaverse & AR/VR: New channels for retail

The metaverse and augmented reality (AR)/virtual reality (VR) will transform the retail sector in the long term – particularly in the areas of product presentation, customer experience and team collaboration:

  • Virtual product presentations: Customers can view products in 3D or AR (e.g. how a piece of furniture would look in their living room or how a pair of glasses would sit on their face). This increases conversion rates and reduces returns.
  • Collaboration in the Metaverse: Teams work together in virtual spaces (e.g. for product design, campaign planning or training). This enables global collaboration without travel costs.
  • NFTs for digital products: Retailers sell digital twins of their products as NFTs (e.g. for collectors or as access to exclusive content). This opens up new revenue streams and customer loyalty strategies.
  • Virtual stores: Brands open digital flagship stores in the metaverse, where customers can experience products in an immersive environment. This is particularly appealing to luxury brands and tech companies.

Conclusion: Collaboration tools as the key to the future of retail

The future of retail will not be shaped by individual technologies, but by the intelligent interconnection of tools, data and people. PIM and DAM systems form the technological foundation, whilst AI makes collaboration more efficient, creative and data-driven. However, the real key to success is change management: only by adapting their culture and structures can companies realise the full potential of these tools.

Recommendations for businesses

 
  1. Assessment: Analyse where data silos or inefficient processes exist within your organisation. Identify pain points that can be resolved using collaboration tools.
  2. Launch pilot projects: Test PIM, DAM or AI tools in a single department or team before rolling them out company-wide. This will allow you to gain experience and build up success stories.
  3. Involve staff: Clearly communicate the benefits of the new tools and offer training and support. Highlight how the tools make day-to-day work easier.
  4. Define KPIs: Measure the success of the roll-out (e.g. time-to-market, data quality, team productivity). Use the data collected to continuously improve the process.
  5. Keep an eye on the future: Experiment with new technologies such as generative AI, blockchain or the metaverse. Be open to innovation and adapt your strategy flexibly to new developments.

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