The 5 Key Factors of Highly Successful PIM Deployments in the AI Commerce Era

Why Product Intelligence—Not Just Product Data—Determines Digital Commerce Success

Most PIM projects fail not because organizations chose the wrong software, but because they treated PIM as a technology implementation instead of a product intelligence transformation. In the AI era, the stakes are even higher. Your PIM foundation determines whether you can:

• Deploy AI product assistants that don't hallucinate features
• Generate accurate product descriptions at scale using GenAI
• Power personalized recommendations across dozens of channels
• Automate complex B2B product configurations
• Syndicate optimized content to Amazon, Walmart, social commerce, and emerging channels
• Enable voice commerce and visual search

Organizations with mature PIM capabilities are deploying AI commerce experiences 3-5x faster than competitors. Those treating PIM as "just another system" are stuck in manual processes, unable to scale, watching their digital commerce ROI stagnate.

This guide reveals the 5 factors that separate PIM successes from expensive failures—updated for the demands of AI-powered commerce, omnichannel selling, and intelligent customer experiences.

The pattern is clear: Get PIM foundation right, and AI amplifies your competitive advantage. Get it wrong, and AI amplifies your chaos.

Why AI Changed Everything About PIM Success

The PIM challenge AI was supposed to solve—but made harder

The 2020 promise:
"AI will automatically write product descriptions. Machine learning will categorize products. You won't need clean data—the models will figure it out."

The 2025 reality:
AI for commerce depends on clean, structured, governed PIM data more than any technology before it:

AI hallucination without clean PIM:

• GenAI invents product features that don't exist (liability risk)
• Recommendation engines suggest incompatible products
• Chatbots provide incorrect specifications
• Voice commerce misunderstands product attributes

The brutal truth: Garbage in, amplified garbage out. AI makes bad PIM data dangerous, not better.

How GenAI exposed PIM gaps organizations didn't know they had

When organizations tried to use GenAI for product content, they discovered:

Problem 1: Attribute incompleteness breaks AI
GenAI product description generators need complete attribute data:

• Missing dimensions? AI can't describe product accurately
• No material information? AI hallucinates composition
• Incomplete technical specs? AI generates unsafe recommendations

Without 90%+ attribute completeness, GenAI creates more work than it saves.

Problem 2: Taxonomy inconsistency creates contradictions
When one category calls it "waterproof" and another "water-resistant," AI:

• Uses terms interchangeably (confusing customers)
• Can't understand the product hierarchy
• Generates contradictory content across channels
• Breaks filter/facet navigation

Problem 3: Cross-channel syndication became AI's stress test
Amazon, Walmart, TikTok Shop each require different:

• Attribute sets
• Content formats
• Character limits
• Image specifications
• SEO optimization

AI can help automate adaptation—but only if your PIM data is clean and structured enough for AI to work with.

Why B2B complexity demands PIM + AI orchestration

B2B product complexity (configurations, CPQ, technical specifications) makes AI even more critical—and more dependent on excellent PIM:

Complex product challenges:

• Thousands of SKUs with option variants
• Technical specifications for engineer buyers
• Compatibility rules (what works with what)
• Pricing tiers, contract pricing, volume discounts
• Multi-language, multi-region requirements

Without structured PIM:
Manual configuration processes, sales rep dependency, slow quotes, high error rates

With PIM + AI:
Automated configuration, guided selling, instant quotes, zero errors

The requirement: PIM must provide the product intelligence (relationships, rules, constraints) that AI systems orchestrate.

The composable commerce architecture demands API-first PIM

Modern commerce is headless, composable, and API-driven:

• Frontend decoupled from backend
• Best-of-breed components (payment, search, recommendations, CMS)
• Multiple customer touchpoints (web, mobile, voice, AR, in-store kiosks)

Legacy PIM approach:
Monolithic systems with proprietary interfaces, hard to integrate

AI-era PIM requirement:
API-first architecture that can:

• Stream product data in real-time to any channel
• Support event-driven updates (when product changes, everything updates)
• Feed AI systems (RAG, agents, recommendation engines)
• Enable composable commerce stacks

The shift: PIM isn't the destination—it's the intelligent data layer that powers the entire commerce ecosystem.

Factor 1 — Product Taxonomy & Attribute Architecture (The AI Foundation)

Why taxonomy is the #1 PIM success predictor

The research is clear: Organizations with well-designed product taxonomy before PIM implementation:

• Deploy 50% faster (less rework, clearer requirements)
• Achieve 40% higher data quality (consistent classification)
• Enable AI 3x faster (structured foundation ready)
• Scale 2x more efficiently (add new products easily)

Without taxonomy first:
PIM becomes a dump truck for unstructured product data—useless for AI, frustrating for users, impossible to scale.

With taxonomy first:
PIM becomes a product intelligence platform that enables search, navigation, recommendations, AI, and automation.

Designing taxonomy for AI and human navigation

Traditional approach (categories only):
Apparel → Shirts → Men's Dress Shirts

AI-era approach (faceted, multi-dimensional):

• Product type hierarchy: Apparel → Shirts → Dress Shirts
• Target audience facets: Men's, Women's, Unisex
• Use case facets: Business, Casual, Formal, Outdoor
• Attribute-driven facets: Sleeve length, collar type, material, fit

Why this matters for AI:

• Enables multi-dimensional product recommendations
• Supports natural language search ("men's casual long-sleeve shirt")
• Allows GenAI to understand product positioning
• Powers intelligent filter/facet navigation

The principle: Design taxonomy for how customers think AND how AI reasons.

Attribute strategy — the difference between data and intelligence

Basic PIM (data repository):
Product has name, SKU, price

Intelligent PIM (AI-ready):
Product has:

• Descriptive attributes: Dimensions, weight, color, material
• Technical attributes: Specifications, compatibility, certifications
• Selling attributes: Benefits, use cases, ideal customer
• Digital attributes: SEO keywords, search terms, category path
• Operational attributes: Stock status, lead time, channel availability
• Relationship attributes: Compatible products, alternatives, upgrades

Why completeness matters:

• GenAI needs complete attribute sets to generate accurate descriptions
• AI recommendations need relationship attributes
• Search needs descriptive and technical attributes
• Syndication needs channel-specific attribute mapping

The benchmark: 90%+ attribute fill rate is the threshold for reliable AI.

Multi-channel attribute mapping — AI automation enabler

The challenge:
Each channel (Amazon, Walmart, Google Shopping, TikTok Shop, your site) has:

• Different required attributes
• Different character limits
• Different naming conventions
• Different quality standards

Manual approach (fails at scale):
Team manually reformats product data for each channel (weeks of work per SKU)

AI-enabled approach (PIM orchestrated):

1. Master product data in PIM (single source of truth)
2. Channel-specific attribute mapping rules (defined once)
3. AI-powered content adaptation (automated transformation)
4. Syndication automation (publish to all channels simultaneously)

Business value:

• 70-90% reduction in syndication time
• Faster new channel onboarding (days instead of months)
• Consistent brand messaging across channels
• Real-time updates across all touchpoints

The requirement: PIM must be the orchestration hub for multi-channel product intelligence.

Factor 2 — Data Governance & Quality Management (The Sustainability Framework)

Why most PIMs fail in year 2 — governance neglect

Year 1 (implementation):
Clean data migration, well-designed taxonomy, trained team, executive support

Year 2 (without governance):

• Data quality degrades (missing attributes, duplicate entries)
• Taxonomy corrupts (junk drawer categories, inconsistent terms)
• Nobody owns maintenance (unclear responsibility)
• System becomes "shelfware" nobody trusts

The pattern: PIM success requires operational discipline, not just good intentions.

The AI amplification effect on data quality

Pre-AI era consequence of bad data:
Wrong product appears in search, customer sees incorrect info, frustrated experience

AI era consequence of bad data:

• GenAI generates thousands of wrong descriptions
• AI recommends incompatible products at scale
• Chatbots confidently provide incorrect specifications
• Voice commerce completely fails

The multiplier: AI amplifies data quality problems 100x—making governance non-negotiable.

Building a governance program that survives beyond implementation

Governance essentials:

1. Clear ownership model

• Taxonomy steward: Owns category structure, naming conventions
• Data stewards: Own data quality for specific domains (categories, brands, regions)
• Product managers: Own product-specific content and attributes
• Governance council: Resolves conflicts, approves major changes

2. Defined processes

• New product onboarding (checklist, approval workflow)
• Category change requests (impact analysis, approval)
• Attribute definition requests (standardization review)
• Data quality audits (regular checks, automated alerts)
• Taxonomy health checks (detect drift, enforce standards)

3. Metrics and accountability

• Data completeness: % of required attributes filled
• Taxonomy health: Consistency, duplicate detection, junk drawer categories
• User adoption: % of products entering through proper workflow
• AI readiness score: Attributes needed for AI vs populated

4. Tools and automation

• Automated data validation rules in PIM
• AI-assisted tagging and categorization
• Quality dashboards visible to stakeholders
• Workflow automation for approval processes

The pattern: Governance isn't bureaucracy—it's the immune system that keeps PIM healthy.

The "data fill" strategy for AI readiness

The reality:
You'll never have 100% of all possible attributes for all products. Prioritize what matters.

AI-driven prioritization framework:

Tier 1 — Essential for AI (90%+ required):

• Core descriptive attributes (what is it?)
• Key technical specs (how does it work?)
• Primary use cases (who is it for?)
• SEO attributes (how do people search for it?)

Tier 2 — Important for experience (70%+ target):

• Detailed technical specifications
• Compatibility relationships
• Lifestyle/benefit attributes
• Rich media assets (images, videos)

Tier 3 — Nice to have (opportunistic):

• Extended marketing content
• Region-specific variations
• Advanced personalization attributes

The strategy:
Focus limited resources on Tier 1 completeness—this unlocks 80% of AI value.

Factor 3 — System Integration & Data Flow Architecture (The Ecosystem Orchestrator)

PIM as the product data hub in the modern commerce stack

Old model (PIM as endpoint):
Product data flows: ERP → PIM → Ecommerce site

AI-era model (PIM as hub):
Product data flows:

• Inbound: ERP, PLM, suppliers, DAM, manufacturer feeds
• Outbound: Ecommerce, mobile app, marketplaces, AI systems, print catalogs, sales tools, channel partners
• Bidirectional: CRM (customer product preferences), analytics (performance data), reviews (customer feedback)

The requirement: PIM must orchestrate 360-degree product data flow—not just push data downstream.

AI systems as PIM consumers — the new integration requirement

AI systems that need PIM data:

1. RAG (Retrieval-Augmented Generation) for product Q&A

• Needs: Complete product specs, use cases, compatibility
• Integration: Real-time API access to structured product data
• Format: Semantic chunks with metadata

2. Recommendation engines

• Needs: Product relationships, attributes, customer behavior
• Integration: Event streams when product data changes
• Format: Attribute vectors for similarity calculation

3. GenAI content generators

• Needs: Master attribute data, brand voice guidelines, SEO keywords
• Integration: Batch or API access for content generation
• Format: Structured attributes + unstructured context

4. Product configurators (CPQ)

• Needs: Configuration rules, compatibility constraints, pricing logic
• Integration: Real-time validation API
• Format: Rule-based product relationships

5. Voice commerce (Alexa, Google Assistant)

• Needs: Natural language product descriptions, key attributes
• Integration: Optimized for voice response (concise, structured)
• Format: Voice-optimized product schema

The insight: AI doesn't just use product data—it depends on PIM being structured, complete, and accessible in real-time.

Event-driven architecture — real-time product intelligence

Legacy approach (batch updates):
PIM data syncs nightly → stale data all day → customer sees wrong info

AI-era approach (event-driven):

1. Product attribute changes in PIM
2. Event published to all downstream systems instantly
3. AI cache invalidated and refreshed
4. All touchpoints updated in real-time

Business value:

• Accurate inventory across channels (no overselling)
• Price changes reflected immediately
• New products available instantly
• Product recalls removed from all channels automatically

Implementation pattern:

• PIM publishes events to message bus (Kafka, EventBridge)
• Consuming systems subscribe to relevant events
• AI systems refresh knowledge when product data changes

The principle: In AI commerce, stale data is dangerous data—real-time is required.

Integration anti-patterns that break AI

Anti-pattern 1: Point-to-point integration spaghetti
PIM → 15 custom integrations → maintenance nightmare, scaling impossible

Fix: API-first PIM + integration layer (iPaaS, event bus)

Anti-pattern 2: Manual data exports
Team exports CSVs from PIM, manually reformats, uploads to channels

Fix: Automated syndication with attribute mapping rules

Anti-pattern 3: No data lineage
Product data comes from 5 sources, nobody knows what's authoritative

Fix: PIM as master + clear data provenance tracking

Anti-pattern 4: AI pulling from stale snapshots
AI systems cache product data, never refreshed → hallucinate discontinued products

Fix: Event-driven cache invalidation

Factor 4 — Change Management & Organizational Readiness (The Human Factor)

Why the best PIM technology fails without organizational buy-in

The research shows:
70% of PIM failures are organizational, not technological:

• Lack of executive sponsorship
• Insufficient training and adoption
• Resistance to new workflows
• No clear ownership or accountability
• Competing priorities and resource constraints

The pattern: Technology readiness is necessary but not sufficient. Organizational readiness determines success.

Stakeholder mapping — who must be aligned for PIM success

Core team:

• Executive sponsor: Budget authority, strategic alignment
• PIM project manager: Day-to-day coordination, delivery accountability
• Taxonomy architect: Design product intelligence structure
• Data governance lead: Define and enforce data quality standards
• Technical lead: Architecture, integrations, infrastructure

Extended stakeholders:

• Product managers: Supply product content, validate taxonomy
• Marketing: Brand compliance, content strategy, SEO
• Ecommerce team: User experience, conversion optimization
• IT/Engineering: System integration, API development, support
• Sales: B2B requirements, CPQ integration, customer data
• Supply chain: Inventory, fulfillment, supplier data
• Channel partners: Syndication requirements, data formats

The insight: PIM touches every group that deals with products—early alignment prevents late-stage conflict.

Training strategy — from power users to casual contributors

Training tiers:

Tier 1 — Power users (deep training):

• Taxonomy stewards
• Data governance team
• PIM administrators Coverage: Complete PIM functionality, governance processes, troubleshooting

Tier 2 — Regular contributors (workflow training):

• Product managers
• Marketing content creators
• Data stewards by category Coverage: Specific workflows they'll use (onboarding, updates, approvals)

Tier 3 — Occasional users (task-based training):

• Sales team (product lookups)
• Customer service (specs and compatibility)
• Channel partners (data access) Coverage: Minimal required functionality, self-service resources

Delivery methods:

• Live workshops (kickoff, deep training)
• Recorded modules (on-demand reference)
• Role-based job aids (quick reference guides)
• Office hours (ongoing support)

The principle: Right-sized training for each role—depth where needed, efficiency elsewhere.

Building a culture of data quality and accountability

The challenge:
PIM governance requires ongoing discipline—easy to deprioritize when busy.

Cultural enablers:

1. Make data quality visible

• Real-time dashboards showing completeness by category/product manager
• Leaderboards celebrating high-quality contributors
• Executive reports tying data quality to business metrics

2. Tie quality to business outcomes

• "Products with 90%+ attributes convert 2x better"
• "Complete data enables AI product descriptions (saves $X)"
• "Clean taxonomy reduced customer service calls by Y%"

3. Automate quality checks

• PIM flags missing required attributes automatically
• Workflow prevents product publishing until quality threshold met
• AI assists with attribute suggestions (reduce manual work)

4. Celebrate wins

• Recognize teams with excellent data quality
• Share success stories (faster product launch due to PIM)
• Communicate ROI regularly

The insight: Sustaining PIM excellence requires making data quality visible, valuable, and celebrated.

Factor 5 — Continuous Optimization & ROI Measurement (The Value Realization Engine)

Why "set it and forget it" PIMs become shelfware

The pattern:
Year 1: Successful implementation, clean data, happy stakeholders
Year 2: Gradual degradation, governance slips, adoption declines
Year 3: System becomes burden, ROI questioned, replacement considered

The root cause: No continuous improvement mindset—PIM treated as project, not program.

The solution: Build feedback loops, measurement, and optimization into PIM operations from day one.

PIM KPIs that drive business value (not just operational metrics)

Operational metrics (necessary but insufficient):

• Data completeness %
• Taxonomy health score
• User adoption rate
• System uptime

Business impact metrics (what executives care about):

Revenue impact:

• Product findability improvement: Search conversion rate increase
• Multi-channel revenue: Sales from new channels enabled by syndication
• Faster time-to-market: Revenue from products launched faster
• Personalization lift: Conversion improvement from AI recommendations

Cost reduction:

• Syndication automation savings: Labor hours saved vs manual processes
• Product onboarding efficiency: Time reduction for new SKU launch
• Customer service cost reduction: Fewer calls due to accurate product info
• Returns reduction: Fewer returns from incorrect product information

AI enablement (2025 addition):

• AI accuracy improvement: Reduction in AI hallucination rates
• GenAI content generation: Cost savings from automated descriptions
• Recommendation engine performance: Click-through and conversion lift
• Chatbot deflection rate: % of product questions handled by AI

The framework: Measure business outcomes, not just system health.

The AI feedback loop — using performance data to improve PIM

Traditional PIM improvement:
Periodic reviews based on stakeholder feedback

AI-powered PIM optimization:

1. AI systems generate usage data
   • Which attributes most correlate with conversion?
   • Which product descriptions perform best?
   • What relationships drive recommendations?
   • Where do customers get stuck in navigation?
2. Analytics identify PIM gaps
   • Products with missing high-value attributes
   • Categories with poor search performance
   • Content that doesn't resonate with customers
3. Prioritize PIM enhancements
   • Focus on attributes that drive business value
   • Fix taxonomy where customers struggle
   • Enrich product relationships that power recommendations
4. Measure improvement
   • Track metrics after PIM enhancements
   • Calculate ROI of specific improvements
   • Iterate continuously

The pattern: Use AI performance to guide PIM improvement—creating a virtuous cycle.

Roadmap evolution — scaling from foundation to intelligent product ecosystem

Phase 1: Foundation (0-12 months)

• Goal: Clean, governed product data in PIM
• Focus: Taxonomy design, data migration, basic integrations
• Success: 90%+ data completeness, governance operational

Phase 2: Activation (12-24 months)

• Goal: Multi-channel syndication and enrichment
• Focus: Automated syndication, AI-assisted content, advanced search
• Success: 5+ channels automated, 50% content generation cost reduction

Phase 3: Intelligence (24-36 months)

• Goal: AI-powered product experiences
• Focus: RAG product Q&A, recommendation engines, dynamic personalization
• Success: AI accuracy >95%, recommendation CTR +30%, revenue lift measurable

Phase 4: Ecosystem (36+ months)

• Goal: Product intelligence platform for entire enterprise
• Focus: Advanced AI (predictive inventory, dynamic pricing), partner ecosystem
• Success: PIM as strategic asset, competitive differentiation

The principle: PIM maturity is a journey—deliver value incrementally while building toward transformational capabilities.

Common PIM Failure Patterns and How to Avoid Them

Failure pattern 1 — Technology-first, strategy-last

The mistake:
"We'll buy the best PIM software and figure out how to use it."

Why it fails:

• Software doesn't match actual business requirements
• Team can't agree on how to organize products
• Implementation stalls due to lack of clarity
• End result: expensive shelfware

The fix:

1. Design taxonomy and data model first (before vendor selection)
2. Define success criteria (what business problems are you solving?)
3. Map current-state data flows (understand integration complexity)
4. Then evaluate tools (select based on proven requirements)

The principle: Strategy before software—know what you need before buying.

Failure pattern 2 — Perfect is the enemy of good

The mistake:
"We need to model every possible product attribute before launching."

Why it fails:

• Analysis paralysis (years of design, no deployment)
• Changing requirements make perfect plan obsolete
• Team exhaustion and stakeholder frustration
• Competitors move faster with "good enough"

The fix:

1. Start with 80% solution (core attributes, primary categories)
2. Deploy to pilot use case (prove value quickly)
3. Iterate based on real usage (add attributes as needed)
4. Expand methodically (don't boil the ocean)

The principle: Imperfect deployed beats perfect planned—launch and learn.

Failure pattern 3 — Governance as afterthought

The mistake:
"We'll worry about governance after implementation."

Why it fails:

• Year 1: Clean data
• Year 2: Degradation starts (no process to maintain quality)
• Year 3: Unusable mess (nobody trusts the data)

The fix:

1. Design governance model during implementation
2. Assign ownership before launch
3. Build quality checks into workflows
4. Make metrics visible from day one

The principle: Governance from day zero—build the immune system before bacteria arrive.

Failure pattern 4 — Ignoring change management

The mistake:
"The software is great—people will naturally adopt it."

Why it fails:

• Resistance to workflow changes
• Lack of training leads to workarounds
• Old systems become shadow IT
• ROI never materializes

The fix:

1. Provide role-based training
2. Show clear WIIFM (What's In It For Me)
3. Celebrate early wins (build momentum)

The principle: People over technology—adoption beats features.

PIM Success Checklist — Are You Ready?

Pre-implementation readiness assessment

Strategic alignment:

• Executive sponsorship secured (budget + political support)
• Clear business case with measurable ROI targets
• PIM linked to strategic initiatives (AI, omnichannel, B2B digital)
• Competing priorities aligned or deprioritized

Organizational readiness:

• Stakeholder mapping complete (all impacted groups identified)
• Cross-functional team committed (not just IT)
• Change management plan developed
• Resources allocated (people, time, budget)

Foundational work:

• Product taxonomy designed (categories, attributes, relationships)
• Data governance model defined (ownership, processes, metrics)
• Integration requirements mapped (systems, data flows, frequency)
• Success criteria and KPIs established

Technology readiness:

• Current-state data quality assessed (know your starting point)
• Integration architecture designed (how PIM fits in tech stack)
• AI/commerce roadmap aligned (PIM enables future capabilities)
• Vendor selection based on proven requirements

If you have <70% checked:
High risk of PIM failure—invest in foundational work before implementation.

If you have 70-85% checked:
Moderate risk—address gaps during Phase 1.

If you have >85% checked:
Strong foundation—execute with confidence.

Post-launch health check (6-month intervals)

Data quality metrics:

• >90% data completeness for core attributes
• <2% duplicate products
• <5% products in "junk drawer" categories
• Taxonomy health score >80%

Governance effectiveness:

• 100% of new products follow onboarding workflow
• Data stewardship roles filled and active
• Quality audits conducted on schedule
• Governance council meeting regularly

System adoption:

• >80% of target users actively using PIM
• <10% workarounds or shadow systems
• Training completion rate >90%
• User satisfaction score >70%

Business value:

• At least 2 measurable business improvements vs baseline
• ROI tracking in place with positive trend
• Executive sponsor still engaged and supportive
• Stakeholders willing to expand PIM usage

AI readiness (2025 addition):

• Attribute completeness sufficient for AI use cases
• PIM APIs accessible to AI systems
• Event-driven updates implemented
• AI systems successfully using PIM data

If multiple categories show red flags:
Urgent intervention required—governance or strategy problem.

Conclusion — PIM as Strategic AI Commerce Enabler

Product Information Management has evolved from operational necessity to strategic differentiator. In the AI commerce era, the organizations winning are those that recognize PIM isn't just about organizing product data—it's about creating the product intelligence foundation that enables:

• AI-powered product discovery and recommendations
• Automated multi-channel content generation and syndication
• Intelligent B2B configurators and guided selling
• Voice commerce and visual search
• Personalization at scale across dozens of touchpoints

The pattern is unmistakable:

Organizations with mature PIM:

• Deploy AI commerce 3-5x faster
• Achieve 30-50% higher conversion rates
• Reduce syndication costs by 70-90%
• Enable personalization that drives measurable revenue lift

Organizations without PIM foundation:

• Stuck in manual processes that don't scale
• Unable to activate AI due to poor data quality
• Losing to competitors on every digital channel
• Watching ecommerce ROI stagnate or decline

The 5 key factors—taxonomy architecture, data governance, system integration, change management, and continuous optimization—aren't independent checkboxes. They're interconnected elements of a complete product intelligence strategy.

The opportunity:
Start now with these proven factors as your blueprint. Six months from now, you can have product intelligence infrastructure that enables AI, scales across channels, and drives measurable business value—or still be trapped in spreadsheets and manual processes, watching competitors pull ahead.

The choice is clear: Build your PIM foundation right, or risk irrelevance in the AI commerce economy.

About Earley Information Science

For 30 years, Earley Information Science has been the leading authority on product information management, information architecture, and the data foundations that make digital commerce successful.

We've guided hundreds of organizations through PIM selection, implementation, and optimization—preventing expensive failures and accelerating time-to-value. Our clients don't struggle with PIM because we address the 5 factors that determine success before technology gets implemented.

Our PIM expertise includes:

• Product taxonomy and attribute design
• PIM selection and vendor evaluation
• Data governance and quality management
• System integration architecture
• AI-readiness assessment and enablement
• Change management and organizational alignment

We make product information usable, findable, and valuable—the foundation for commerce that works in the AI era.