From manual to automated: How to guide your team on the way to AI-supported product data processes

Why automating product data processes is crucial

For a long time, managing product data was a highly manual process — characterized by repetitive tasks, error-prone data entry, and inconsistent formats. But with the growing number of sales channels and increasing customer expectations, it is becoming increasingly important to provide product information quickly, precisely and scalably. This is where artificial intelligence comes into play.

AI-powered product data workflows help companies increase efficiency, minimize errors, and significantly shorten their time-to-market. But the transition from manual processes to an AI-first strategy requires more than just technological implementation — it is also about changing mindset, processes, and teams.

Challenges when moving to AI-powered workflows

Many companies face the same challenges when they want to automate their product data management:

• Skepticism and resistance in the team: Employees often fear that automation will replace their tasks instead of supporting them.
• Lack of know-how: Dealing with AI-based systems requires a certain level of technical understanding (at least that is the caveat).
• Data quality and structure: AI is only as good as the database it works with — chaotic or incomplete data can slow down the automation process.
• Lack of integration: Many companies still use isolated solutions that are not directly compatible with their existing systems.

But with the right strategy, these challenges can be overcome.

What does AI-supported product data processing mean?

Managing product data is a complex challenge: Different data sources, unstructured information, and constantly changing requirements make manual maintenance inefficient. Ainavio uses AI-based workflows that enable automated, scalable and intelligent product data processing through large language models (LLMs).

These workflows are carried out by Annotation, structuring and enrichment of product information to create a consistent, error-free and optimized database.

Best practices for introducing AI-powered product data processes

1. Involve the team right from the start

A successful transition to AI starts with the involvement of everyone involved. Employees should understand that automation is not intended to replace their work, but to increase their efficiency.

• Early communication about goals and benefits of AI integration.
• Clear task assignment: Which processes are automated, which remain manual?
• Workshops and internal training for all relevant departments.

2. Optimize data quality

Before an AI starts processing product data, the data must be brought to a consistent and standardized level.

• Eliminate duplicates and incorrect entries.
• Harmonization of measurement units, currencies and product categories.
• Complementing missing attributes with AI-based analysis and web research.

3. Step-by-step automation instead of big bang

Instead of changing all processes at once, it is recommended to start with individual areas and scale them gradually:

• Automated Attribute Generation for New Products.
• AI-supported text creation for consistent product descriptions.
• Automated translations and localization for international markets.

Change Management: How to Get Your Team Ready for AI

Create incentives and share success stories

People react positively to change when they see clear benefits. Companies should therefore make successes visible:

• Before/after comparisons of processing times and data quality.
• Success stories from colleaguesWho were able to increase their productivity with AI.
• Gamification Approachesto inspire employees to use AI tools.

Case Study: Successful AI Transformation with Ainavio

A leading online retailer with over 100,000 products was faced with the challenge of managing its product data efficiently. The previous manual processes led to slow market launches and incorrect information.

Solution with Ainavio:

• Automated text creation: AI generated consistent, SEO-optimized product descriptions in multiple languages.
• Data harmonization: Measurement and weight units were supported by AI.
• Quality check: The AI identified incorrect or missing attributes and corrected them automatically.

Results:

• 80% less manual processing time for new product data.
• 30% faster time to market through automated workflows.
• Higher data quality And fewer returns thanks to more accurate product information.

This success story shows that the right combination of AI and change management leads to measurable improvements.

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