AI-Driven Product Information Management: Transforming Workflows, Governance, and Growth

Product data is fundamental to how organizations manage operations and deliver customer experiences in commerce. As product assortments expand and distribution channels increase, coordinating product information at scale becomes more challenging.

In response to this growing complexity, companies are turning to artificial intelligence to transform how they operationalize product data across systems. According to Deloitte’s 2026 AI Report, 34% of organizations are using AI to transform their business models, while another 30% are redesigning key processes to incorporate AI.

These changes are reshaping how product data is structured, maintained, and applied across channels. As a result, product information management platforms are evolving to support these demands.

This article highlights how AI is transforming Product Information Management (PIM), including its impact on workflows, governance, and business performance throughout the commerce ecosystem.

The Evolution of PIM from Data Repository to an Intelligence Layer

As product assortments expand and distribution channels multiply, effective product data practices become a key requirement for commerce operations.

This shift is reflected in market expansion. Grand View Research reports that the global product information management market was valued at $11.49 billion in 2023 and is projected to grow at a 16.7% CAGR from 2024 through 2030, indicating a rising reliance on systems that help synchronize product data across teams, suppliers, and channels.

However, the demands of digital commerce are redefining how organizations manage product information, as manual enrichment, fragmented supplier inputs, and slow update cycles limit data quality and efficiency, impacting time to market and consistency as commerce environments become increasingly complex.

To address these challenges, organizations are embedding artificial intelligence into product data processes, with product information management (PIM) evolving from a centralized repository into an intelligence layer that enables coordinated workflows, improved data accuracy, and accelerated commercialization.

AI Automation in Product Information Management Workflows

AI supports dynamic, scalable execution of product data tasks across creation, enrichment, classification, and validation, enhancing data reliability and streamlining execution across systems and teams.

In practice, these capabilities are applied across product information management workflows, including:  

• Automated attribute extraction and enrichment: AI derives product attributes from specifications, images, and supplier feeds, increasing completeness and consistency while reducing reliance on manual input.

• AI-generated descriptions and multilingual content: AI generates product content and translates it for various markets, supporting faster global distribution and standardized messaging.

• Intelligent classification and taxonomy alignment: Machine learning models analyze product descriptions and images to categorize products accurately, strengthening discoverability and navigation across digital channels.

• Deduplication and normalization of supplier data: AI detects duplicate entries and aligns inconsistent supplier inputs, helping establish a unified record within the PIM system.

• Governance and validation with human oversight: AI-driven workflows incorporate review and approval processes to ensure compliance, accuracy, and adherence to brand standards.

Together, these functions simplify product onboarding while improving data reliability and operational efficiency. This aligns with Gartner’s projection that companies investing in AI-powered digital shelf analytics (DSA) to replace manual data-gathering could reduce operational costs by up to 30% by 2026, highlighting AI’s role in supporting scalable, cost-effective commerce execution.

Turning Product Data into Business Growth

AI-enabled product information management software also drives value by enhancing the customer journey, sales performance, and responsiveness to changing market conditions. When product data is accurate, complete, and up-to-date, it becomes a strategic growth asset that enables operational success and informed decision-making.

To translate product data into business value, organizations use PIM platforms to deliver seamless interactions across customer touchpoints, resulting in outcomes such as:

• Unified experiences across every channel: Creating and enriching product content allows organizations to tailor information for marketplaces, regions, and partners while maintaining consistent messaging.

• Improved discoverability and conversion: Structured data enhances search relevance and product visibility, helping shoppers find and evaluate products more efficiently.

• Increased revenue through personalization: AI enables dynamic recommendations and customized experiences, with 80% of consumers preferring personalization, increasing spending by up to 50%.

• Faster launches and updates: Streamlined workflows help teams bring products to market and update them more efficiently, enabling organizations to respond to opportunities and buyer needs without delay.

• Ongoing improvement through performance insights: AI-powered digital shelf analytics provide visibility into product details and customer feedback, supporting content refinement and stronger market positioning over time.

By connecting data quality to the purchasing experience and commerce agility, teams can accelerate revenue growth while establishing a foundation for sustained digital innovation across the enterprise.

Operationalizing Trust Through Governance, Accountability, and Control in AI-Driven PIM

AI adoption introduces risk alongside its benefits, making governance essential to ensure that data processes remain controlled, traceable, and aligned with defined standards as a foundation for building trust in AI-driven decisions.

To support this, Product Information Management (PIM) provides oversight of both product data and the activities that shape it. It establishes a structured environment where information flows, transformations, and AI-generated outputs are governed through rules and controls, shaped by the following priorities:

• Aligned data ownership: Establish a unified approach to product information across teams to reduce fragmentation and inconsistency

• Transparency and accountability: Maintain visibility into how data is handled to support monitoring and compliance

• Structured oversight: Balance automation with appropriate human review to help results meet business and brand expectations

• Standardization across channels: Ensure product details remain synchronized wherever they appear or are distributed

• Consistent governance practices: Apply uniform policies to reduce risk and strengthen operational reliability

When governance is embedded into an AI-enabled product information management system, organizations move from simply handling data to directing how it is used and validated. In turn, trust is built through monitoring and verification, enabling teams to adopt AI while upholding accountability.

AI-Enabled Product Information Management (PIM) in Action

Companies adopting product information management software, such as inriver and Akeneo, demonstrate how AI enhances data accuracy, automates key process, and scales product data operations, as illustrated in the following examples:

• Brunswick unified 150 brands using AI-driven automation, accelerating time-to-market by 28% and improving localization speed by 50% while simplifying content workflows.

• Swegon centralized product data and digital assets, reducing manual effort while advancing governance, efficiency, and customer experience.

• Vogel’s consolidated more than 5,000 SKUs and automated content generation and distribution, strengthening data accuracy and reinforcing compliance across global channels.

• Bata managed over 1.5 million products across 33 countries, using automation to reduce time to market by 40% and increase organic traffic by 15%.

• Trotec scaled its catalogue from 2,500 to 25,000 SKUs and expanded AI-driven enrichment across 20,000 products, contributing to an estimated 35% growth in sales.

• JC Perreault accelerated product data enrichment and translation, shortened launch cycles from two weeks to 24 hours, and grew its online catalogue fourfold.

Together, these examples reveal how AI-enabled Product Information Management (PIM) drives business impact through more efficient product launches, improved content quality, and the ability to manage expanding assortments across markets and channels.

As AI reshapes the commerce ecosystem, Tidal Commerce partners with organizations to implement AI-driven Product Information Management (PIM) strategies that combine automation, governance, and enrichment to manage product data and coordinate workflows. We align technology and operations to assist teams in maintaining accurate information, support efficient launches, and deliver consistent content across channels.

To explore how AI-enabled PIM can support your product data strategy, connect with us. Our team is ready to help you.