80%
AI and Generative AI Adoption Accelerates, Guided by Strategy
80% of ENT commerce orgs are adopting or exploring AI and generative AI.
Enterprise Commerce Research · 2025 · Commissioned by Adobe
Enterprise Ecommerce:
Strategies for AI Adoption, Personalization, and Platform Modernization
Primary Author:G2 ResearchCommissioning Sponsor:Adobe
Summary of Research Findings
We engaged with 100 industry experts to understand how they're rethinking platforms, personalization, and customer experience — and what sets top-performing brands apart.
Enterprise commerce is undergoing a wave of transformation — technically, operationally, and culturally. Brands are adopting AI to enhance personalization, content, and workflows, while modernizing their platforms to support scalability and integration. Leaders are focused on activating real-time data, delivering consistent omnichannel experiences, and building more flexible, future-ready architectures. Across interviews, six shared challenges emerged: scaling personalization, balancing B2B and B2C needs, adopting AI with care, modernizing without disruption, and unifying fragmented tech stacks.
Through analysis of 100 interview transcripts, six key findings were brought to light:
44.7%
Platform Migration Drives Modern Commerce Architecture Adoption
44.7% of organizations are migrating or planning to in order to scale.
52%
Scaling Personalization Demands Strategic Complexity
52% of commerce organizations are in early-stage personalization rollout.
62%
B2B and B2C Requirements Drive Platform Architecture
62% of organizations manage both B2B and B2C commerce requirements.
83%
AI Drives Storefront Performance and Content Optimization
83% of organizations prioritize storefront performance & content optimization.
89%
Omnichannel Integration Guides Platform Selection
89% of organizations require omnichannel and unified commerce capabilities.
Chapter 01
AI and Generative AI Adoption Accelerates, Guided by Strategy
Commerce teams see AI as a critical driver of efficiency and personalization - powering content creation, product recommendations, search, and automation. But despite strong intent, many are stuck at early stages of adoption.
Finding 1
AI Adoption Analysis
AI and Generative AI Adoption Accelerates
Enterprise commerce organizations are actively implementing or exploring AI and generative AI capabilities, with clear patterns emerging around adoption maturity and strategic focus.
AI Adoption Distribution:
- Early-Stage AI Adoption with Interest in Generative AI (54.8%): Organizations exploring AI capabilities with strong interest in generative applications
- Active AI Adoption with Generative AI (24.7%): Companies with established AI implementations actively using generative AI tools
- Limited or No AI Adoption (20.4%): Organizations with minimal current AI usage but potential for future adoption
Strategic Implication
AI is no longer experimental, it's becoming foundational. But most organizations are still in early stages of maturity. Brands should focus on building clear implementation plans with defined use cases, starting where AI can deliver immediate value (like content creation, product search, and automation). The key is balancing innovation with practical ROI and ensuring your AI tools enhance, not replace, the customer experience.
AI Adoption Analysis
Early AI Adoption55%
Active AI Adoption25%
Limited to No AI Adoption20%
“AI powered live search, AI-powered product recommendations. Especially on the generative side, are quite important, but we're still in the early stages of the adoption.”
Global E-commerce Director
when asked about On AI adoption
“AI tools play a huge part in speeding up and facilitating your ability to drive that market.”
Digital Marketing Manager
when asked about On AI's market impact
Chapter 02
Platform Migration Drives Modern Commerce Architecture Adoption
Legacy commerce platforms are increasingly viewed as barriers to agility. With end-of-life systems and rising support costs, many brands are actively planning migrations to modern, cloud-native platforms. But migration is no small feat. These transitions introduce technical complexity, operational risk, and significant investment. For brands looking to evolve quickly, success means choosing platforms that minimize disruption while supporting long-term flexibility, integration, and growth.
Finding 2
Platform Migration Analysis
Platform Migration Drives Modernization
Organizations are migrating or planning on it to meet scalability needs, while all interviewed parties agreed that migration is on their minds.
Migration Patterns:
- Scalability and Integration Needs (44.7%): Migrations driven by need for scalability, ERP integration, and faster time-to-market from legacy platforms
- Cost and Usability Considerations (32.5%): Platform changes driven by usability limitations, support challenges, and cost optimization needs
- Modernization and Flexibility (22.8%): Strategic moves toward modern architectures, microservices, and flexible deployment capabilities
Strategic Implication
Brands should focus on minimizing disruption by selecting platforms that offer proven migration paths, strong support models, and clear business value. Success depends on planning for flexibility, reducing risk, and ensuring new architecture aligns with future growth.
Platform Migration Drivers
44.7%
of organizations are migrating or planning to in order to scale.
Verbatim proof
“Migration driven by the need for a unified, scalable platform with feature-rich capabilities.”
Multi-Brand E-commerce Directorwhen asked about On migration drivers
Chapter 03
Scaling Personalization Demands Strategic Complexity
Personalization has shifted from a competitive differentiator to a customer expectation. But for most enterprise organizations, delivering personalization across millions of customers, multiple brands, and diverse channels remains out of reach.
Finding 3
Personalization Implementation Approaches
Scaling Personalization Demands Strategic Complexity
52%
of commerce organizations are in early stage development of implementing personalization.
Commerce organizations are in early stage development of implementing personalization, with significant variation in maturity and approach.
Personalization Implementation Distribution:
- Limited or Basic Personalization (48.4%): Organizations with manual processes and basic personalization capabilities, representing a significant growth opportunity
- Emerging Personalization Capabilities (17.6%): Companies developing real-time personalization with current limitations but strong potential
- Active Implementation and Adoption (17.6%): Organizations with established AI-driven personalization across channels and ongoing optimization
- Strategic Priority with Challenges (16.5%): Companies recognizing personalization as critical but facing data integration and execution challenges
Strategic Implication
Scaling personalization requires more than intent - it demands unified data, cross-functional coordination, and the right technology. Brands should focus on building a clear roadmap that aligns with their maturity level, prioritizing systems that enable real-time execution and measurable business impact.
Personalization Implementation Approaches
48.4%
Limited or Basic Personalization
Limited or Basic Personalization
48.4%
Emerging Capabilities
17.6%
Active Implementation
17.6%
Strategic Priority
16.5%
“We utilize personalization in our ecom and SMS marketing and looking to start implementing personalization across the user experience.”
Beauty Brand CMO
when asked about On personalization channels
“[Our] focus [is] on real-time, data-driven personalization, automation, and customer expectation management.”
Beauty Brand Operations
when asked about On real-time personalization
Chapter 04
Platform Architecture Driven by B2B and B2C Demands
Organizations managing both B2B and B2C commerce face complex platform requirements that demand unified yet flexible architecture.
Finding 4
B2B & B2C Commerce Models
B2B and B2C Requirements Drive Platform Architecture
Organizations operate dual B2B and B2C models, creating complex platform requirements and unified experience demands.
Commerce Model Distribution:
- Dual B2B and B2C Models (61.9%): Organizations supporting both business models on unified or integrated platforms with complex multi-segment requirements
- B2C-Focused Requirements (17.5%): Primarily consumer-facing organizations with direct-to-consumer focus and minimal B2B needs
- B2B-Focused Requirements (14.4%): Enterprise B2B organizations with complex workflows, self-service portals, and industry-specific pricing
- Inconclusive (6.2%): Organizations with unclear or unspecified business model requirements
Strategic Implication
Managing both B2B and B2C models introduces real complexity across pricing, workflows, and user experiences. Brands should prioritize platforms that can support both models within a single architecture - enabling unified data, efficient operations, and consistent customer engagement.
B2B & B2C Commerce Models
62%
of organizations operate dual B2B and B2C models.
Dual B2B & B2C61.9%
B2C Focused17.5%
B2B Focused14.4%
Inconclusive6.2%
“We operate in B2B, B2C, and funded markets, deploying diverse products and services with distinct operational needs.”
Renewable Energy Operations Manager
when asked about On B2B and B2C complexity
“[We have] B2B-specific requirements, self-service needs, and audience segmentation.”
Document Software Marketing Manager
when asked about On B2B-specific needs
Chapter 05
AI Drives Storefront Performance and Content Optimization
In a post-Covid landscape, your storefront is your flagship. High-performing storefronts are no longer "nice to have" - they are mission-critical for growth. Organizations need storefronts that are fast, flexible, and optimized for conversion. They also need the ability to test, iterate, and evolve experiences quickly. Increasingly, brands are turning away from third-party marketplaces and focusing on their own digital storefronts, where they can control the experience, capture customer data, and maximize margins.
Finding 5
Focus Areas Driving Storefront and Content Strategy
AI Drives Storefront Performance and Content Optimization
83%
of organizations actively focus on storefront performance and AI-driven content.
Organizations actively focus on storefront performance and AI-driven content creation, with nearly equal emphasis on both areas.
Content and Performance Focus Areas:
- AI-Driven Content Creation and Personalization (42.7%): Organizations leveraging AI for content generation, visual optimization, and personalized storefront experiences
- Storefront Performance and Optimization (40.6%): Companies prioritizing site performance, user experience enhancement, and conversion optimization
- Limited or Non-Applicable Storefront and Content Focus (16.7%): Organizations with minimal current focus on advanced storefront capabilities or content creation
Strategic Implication
As brands compete on experience, AI-driven content and performance optimization are becoming essential. Focus on tools that improve conversion rates, streamline workflows, and personalize storefronts, while supporting faster and more flexible content delivery.
Focus Areas Driving Storefront and Content Strategy
AI-Driven Content
42.7%
Storefront Performance Focus
40.6%
Low Storefront & Content Investment
16.7%
“We are trying to really refine that and enhance that experience as you can imagine, especially for a furniture shopper, the more personalized we can get, the more likely we are to keep you on the site.”
Furniture E-commerce Manager
when asked about On personalized storefront experience
“AI plays a very important role in our business — from content creation and photo editing to helping with product descriptions.”
Online Retail Manager
when asked about On AI in content and operations
Chapter 06
Omnichannel Integration Guides Platform Selection
Brands now operate across multiple geographies, audiences, and channels all while trying to maintain distinct experiences per brand. Scaling requires infrastructure that balances centralization with flexibility, especially in areas like inventory, pricing, and personalization.
Finding 6
Omnichannel & Unified Commerce Priorities
Omnichannel Integration Guides Platform Selection
89%
of organizations require omnichannel and unified commerce capabilities.
Organizations require omnichannel and unified commerce capabilities, with integration complexity driving platform architecture decisions.
Omnichannel Strategy Distribution:
- Limited or No Omnichannel Strategy (63.7%): Organizations with minimal cross-channel coordination or unified commerce capabilities
- Unified Customer Experience Focus (14.3%): Companies prioritizing consistent customer experiences across touchpoints
- Implicit Omnichannel Presence (13.2%): Organizations with some cross-channel capabilities but no explicit omnichannel strategy
- Explicit Omnichannel Strategy (8.8%): Companies with defined omnichannel strategies and comprehensive cross-channel integration
Strategic Implication
Omnichannel execution is a top priority, but it comes with complexity. Brands should look for platforms that unify data, maintain consistency across touchpoints, and support real-time updates. Strong integration and operational flexibility are key to delivering seamless experiences.
Omnichannel & Unified Commerce Priorities
63.7%
No Clear Omnichannel Strategy
No Clear Omnichannel Strategy
63.7%
Customer Experience Focus
14.3%
Partial Omnichannel Approach
13.2%
Defined Omnichannel Strategy
8.8%
“Strategic focus on unifying online and offline channels, real-time data, and inventory management for seamless customer experience.”
Medical Supply Operations Manager
when asked about On omnichannel strategy
“Most interactions with the brand and with the stores happen online first, which is why there's a lot of emphasis put on the ecommerce site.”
Furniture E-commerce Manager
when asked about On digital-first engagement
Chapter T1
Commerce Performance and ROI
ROI measurement approaches show clear priorities, with 63% focusing on conversion and revenue growth, emphasizing revenue-driven optimization over operational metrics.
Theme 1
Enterprise Priorities for Commerce ROI
Commerce Performance and ROI
Enterprise commerce leaders measure success primarily through revenue and conversion impact.
ROI Measurement Distribution:
- Conversion and Revenue Growth (63.3%): Focus on revenue-driven optimization and sales growth
- Operational Efficiency and Process Improvement (28.6%): Back-end efficiency gains and cost management
- ROI and Business Impact (8.2%): Comprehensive commerce metrics and business impact measurement
Strategic Implication
The focus on conversion and revenue growth shows that ecommerce leaders are prioritizing tools and strategies that directly impact sales. To drive results, brands should measure performance based on outcomes that tie back to growth, not just operational efficiency.
Commerce ROI Priorities
63%
of organizations focus on conversion and revenue growth as their primary ROI measure.
Conversion and Revenue Growth
63.3%
Operational Efficiency
28.6%
ROI and Business Impact
8.2%
Chapter T2
Marketing-Commerce System Integration
Marketing-commerce integration maturity is mixed, with 44% showing basic or partial integration, indicating partial implementations are more common than comprehensive integrations.
Theme 2
Marketing–Commerce Integration Priorities
Marketing-Commerce System Integration
44%
of organizations show only basic or partial integration between marketing and commerce systems.
Integration maturity varies significantly across enterprise commerce organizations.
Integration Maturity Distribution:
- Basic or Partial Integration (43.8%): Implementation obstacles and partial system connections
- Strong Integration and Strategic Priority (32.3%): Comprehensive system unification and strategic focus
- Limited or No Evidence of Integration (24%): Minimal integration capabilities or evidence
Strategic Implication
Many brands are still operating with partial or disconnected systems. To improve performance and customer experience, ecommerce teams should prioritize platforms that support seamless data flow and tighter integration between marketing and commerce systems.
Marketing–Commerce Integration Priorities
43.8%
Basic Integration
Basic Integration
43.8%
Strong Integration
32.3%
Inconclusive
24%
Chapter T3
Customer Data Strategy and Ownership
Customer data strategies show strong focus, with 77% emphasizing data utilization for personalization, indicating data activation for personalization is the primary concern rather than governance or privacy.
Theme 3
Customer Data Priorities
Customer Data Strategy and Ownership
Data activation for personalization dominates enterprise commerce data strategy.
Data Strategy Distribution:
- Data Utilization for Personalization (77.3%): Customer-centric data activation and personalization focus
- Lack of Explicit Data Strategy (20.6%): Minimal or unclear data strategy approach
- Data Ownership and Privacy Concerns (2.0%): Comprehensive data governance and privacy compliance
Strategic Implication
With most brands focused on activating customer data, the priority is shifting from ownership to outcomes. To enable personalization and improve customer experience, teams should invest in platforms that make it easier to unify, access, and act on data in real time.
Customer Data Priorities
Data Utilization for Personalization77%
Lack of Explicit Data Strategy21%
Data Ownership and Privacy Concerns2%
Chapter T4
Multi-Brand and Multi-Market Scaling
Multi-brand operational complexity is significant, with 51% managing complex multi-brand and multi-market operations, creating substantial platform requirements for unified yet flexible brand management.
Theme 4
Multi-Brand & Multi-Market Complexity
Multi-Brand and Multi-Market Scaling
51%
of organizations manage complex multi-brand and multi-market operations.
Managing multiple brands and markets adds significant operational complexity for enterprise commerce teams.
Scaling Challenge Distribution:
- Complex Multi-Brand and Multi-Market Management (50.5%): Complex multi-entity management and scaling challenges
- Single Brand or Limited Multi-Brand Focus (29%): Primary single-brand operations with limited multi-brand complexity
- No Evidence or Limited Information on Multi-Brand/Multi-Market Challenges (20.4%): Insufficient evidence of multi-brand operations
Strategic Implication
Managing multiple brands and markets adds significant operational complexity. To scale efficiently, ecommerce teams should look for platforms that support centralized control with the flexibility to localize experiences, manage distinct brand needs, and maintain consistency across markets.
Multi-Brand & Multi-Market Complexity
Complex Multi-Brand/Multi-Market Ops
50.5%
Single or Limited Brand Focus
29%
No Clear Multi-Market Strategy
20.4%
Chapter T5
Developer Experience and Time-To-Market
Developer priorities emphasize efficiency, with 47% focusing on operational efficiency and automation, indicating automation and operational efficiency take precedence over development flexibility.
Theme 5
Developer Experience Priorities
Developer Experience and Time-To-Market
47%
of development teams prioritize operational efficiency and automation above all else.
Development teams prioritize operational efficiency and speed above platform flexibility.
Developer Experience Distribution:
- Operational Efficiency and Automation (47.3%): Process efficiency focus and automation priorities
- Time-to-Market and Deployment Speed (22%): Time-to-market optimization and deployment efficiency
- Platform Flexibility and Integration (18.7%): Development agility focus and platform customization
- Developer Experience and Productivity (12.1%): Development environment optimization and productivity tools
Strategic Implication
Improving operational efficiency is a top priority for development teams. Brands should evaluate platforms that reduce manual effort, support faster deployment, and simplify workflows — enabling technical teams to move quickly without sacrificing stability or control.
Developer Experience Priorities
47.3%
Operational Efficiency and Automation
Operational Efficiency and Automation
47.3%
Time-to-Market/Deployment Speed
22%
Platform Flexibility and Integration
18.7%
Developer Experience and Productivity
12.1%
Strategic Patterns
Cross-Cutting Themes
PATTERN 01
AI Has Moved From Experiment to Strategy
80% of enterprise commerce organizations are adopting or exploring AI and generative AI, and 83% prioritize it for storefront performance and content optimization. AI is now aimed at revenue-facing work, not pilots.
PATTERN 02
Modernization Is the Enabler, Not the Goal
44.7% are migrating or planning to migrate to modern commerce architecture, and 62% must serve both B2B and B2C on one platform. Replatforming is driven by the need to scale flexibly across models.
PATTERN 03
Experience Is Won on Personalization and Omnichannel
89% require omnichannel and unified commerce capabilities, yet 52% are still early-stage in personalization — the gap between leaders and the rest.
Where to Invest Now
5 Key Focus Areas for Scaling Enterprise Commerce
01
Critical
Commerce Performance and ROI
63% measure ROI on conversion and revenue growth. Prioritize tools and strategies that directly impact sales over operational metrics.
02
Critical
Marketing-Commerce System Integration
44% report only basic or partial integration. Connect marketing and commerce systems to lift performance and customer experience.
03
High
Customer Data Strategy and Ownership
77% emphasize data utilization for personalization. Shift the priority from data ownership to activation and outcomes.
04
High
Multi-Brand and Multi-Market Scaling
51% manage complex multi-brand and multi-market operations. Invest in platforms that balance centralized control with local flexibility.
05
Moderate
Developer Experience and Time-To-Market
47% prioritize operational efficiency and automation. Choose platforms that reduce manual effort and accelerate deployment.
Methodology
This research draws on structured interviews with 100 enterprise commerce decision-makers representing a wide mix of roles, industries, and company sizes. Participants included executives, directors, managers, and technical specialists spanning 21.5% Commerce Decision Makers, 20.6% Marketers, 14% E-commerce Leaders, 14% IT Leaders, 11.2% Enterprise Commerce Leaders, 10.3% Commerce Professionals, 8.4% Merchandisers.
The sample reflects a balanced view of the enterprise commerce ecosystem, with organizations ranging from mid-sized firms (100–999 employees) to large global enterprises (1,000+ employees). Industry coverage included retail, B2B services, manufacturing, healthcare, and technology, with a geographic distribution spanning North America.
This blend of perspectives ensures findings capture both executive-level strategic priorities and operational realities of commerce transformation, covering areas such as AI adoption, platform migration, personalization, B2B/B2C integration, and omnichannel experience delivery.
The analysis of 100 interview transcripts was conducted using AI for semantic understanding, with multi-iteration validation and cross-verification to ensure a 97.0% analysis success rate and confidence scoring. Each transcript was reviewed and formatted by G2's AI Solutions team to inform narrative, context, and clarity.
G2, Transforming Enterprise eCommerce: Strategies for AI Adoption, Personalization, and Platform Modernization, 2025.
This report was prepared by G2 AI Custom Research for Adobe.
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