What the latest research reveals about AI adoption in B2B commerce and how manufacturers and distributors can maximize their AI investments

Introduction

Artificial Intelligence has become a defining topic in B2B commerce. But how are manufacturers and distributors actually implementing AI? What results are they seeing? And what separates successful AI adopters from those still struggling to generate value?

The 2026 B2B Commerce AI Benchmark report, produced by B2B Online Insights for OroCommerce, surveyed 100 senior decision-makers at B2B manufacturers and distributors to answer these critical questions. The findings reveal an industry that has moved past experimentation, but hasn’t yet achieved the transformational results many expected.

Key Findings at a Glance

• 80% of B2B organizations have deployed AI, including 8% with full integration across operations
• 81% use AI for back-office automation and data entry, the most common application
• 48% describe AI as “somewhat effective” with positive but not transformational results
• Only 17% report significant ROI from their AI investments
• 55% cite data quality as a top barrier to deeper AI adoption
• Only 4% have comprehensive AI governance policies in place

Where AI Stands Today in B2B Commerce

The B2B industry has decisively moved past the “should we use AI?” stage. Organizations are now exploring how deep their adoption should go and where AI tools deliver the most value.

Current Implementation Status

Most respondents have deployed AI in multiple functions with measurable results (37%) or have deployed it in one or several functions with limited results (35%). Only 8% have fully integrated AI across their B2B commerce operations.

These numbers reveal an industry committed to AI but still working toward enterprise-wide deployment.

Where AI Is Being Used

The most common AI applications include:

• Back-office automation and data entry: 81%
• Customer service and support automation: 73%
• Customer-facing chatbots and virtual assistants: 68%
• Product discovery and search optimization: 62%
• Sales enablement and lead scoring: 59%

Organizations have started implementing AI where risk is lowest and ROI is most straightforward. However, emerging applications like AI-native purchasing, quote processing, and pricing optimization show strong momentum for the next 12 months.

How AI Is Being Integrated

The dominant approach is embedding AI modules within existing enterprise software (60%), followed by:

• AI capabilities in productivity suites like Microsoft Copilot (40%)
• Custom in-house developed AI tools (35%)
• Stand-alone AI point solutions (28%)

This suggests most B2B organizations prefer adding intelligence to systems they already run rather than replacing them with AI-native platforms. The most successful AI implementations tend to serve existing processes rather than impose new ones.

The Reality of AI Effectiveness

Overall Results

Nearly half (48%) say AI is somewhat effective with positive but not transformational results. Meanwhile, 17% report very effective deployments with significant ROI.

However, 15% say AI has not delivered expected value at all. Among these respondents, recurring themes include:

• Teams that don’t trust or use AI recommendations
• Use cases too narrow to drive meaningful results
• Tools requiring more manual oversight than expected
• Data foundations too weak to support reliable outputs

Top Business Outcomes

Among organizations reporting effective AI deployments, the top three outcomes are:

1. Enhanced employee productivity: 54%
2. Improved customer satisfaction scores: 51%
3. Improved data quality and accessibility: 48%

These are practical, measurable gains. Companies drawing a direct line between AI investments and time saved, customers served, and data made more usable can demonstrate clear ROI.

What Successful Companies Are Doing Differently

A deeper analysis of the 17% reporting “very effective” AI implementations reveals distinct patterns:

They Collaborate with Customers and Employees

• 47% actively co-develop and pilot AI with customers (compared to 23% overall)
• 41% report employees feel “very positive” about AI tools (compared to 8% overall)

Successful organizations design AI capabilities with customers in mind and solicit employee input to increase adoption rates.

They Establish Formal Governance

• 24% have comprehensive AI governance policies with clear approval processes
• Another 71% have basic guidelines in place

Formal governance reduces risk and enables departments to implement AI according to pre-determined rules.

They Use Embedded Vendor Capabilities

Successful companies leverage specific AI-driven capabilities within their existing systems:

• Email sentiment analysis to identify at-risk accounts
• Real-time insights from data for faster decision-making
• Personalized pricing engines based on purchase history
• Demand forecasting for inventory management
• AI assistants handling routine buyer queries

They Balance Optimism with Caution

Successful adopters express optimism for specific use cases like faster service and better data insights. However, they remain cautious about cybersecurity risks, data quality issues, and maintaining trust in customer relationships.

As one respondent noted: “I am skeptical about including too many AI features because B2B is all about building and maintaining trust with parties.”

What’s Holding AI Back

Top Barriers to Adoption

1. Legacy system or data integration: 53%
2. Concerns about data security and privacy: 46%
3. Resistance from employees or sales teams: 41%
4. Lack of executive buy-in or organizational alignment: 33%
5. Unclear use cases or business value: 33%

Organizations want AI embedded in their existing systems, but those same systems are often the primary obstacle to making it work.

The Biggest Data Gaps

Among respondents citing data quality as a barrier:

• Lack of standardized data formats across teams and systems: 67%
• Inaccurate or incomplete order history: 63%
• Outdated or siloed ERP systems: 49%
• Disconnected pricing and inventory data: 46%

These are not anomalous problems but everyday realities of B2B operations. Fixing them requires new data policies, technology changes, and robust AI governance.

The Governance Gap

Only 4% have comprehensive AI governance policies with clear approval processes. Most organizations (62%) have basic guidelines that aren’t fully formalized, while 30% are still developing policies.

This means 96% of B2B organizations are running AI without mature governance frameworks — a significant blind spot for an industry handling sensitive pricing, customer-specific contracts, and complex supply chain data.

Where AI Is Headed: Future Priorities

Greatest Expected Impact on Customer Experience (Next 24 Months)

1. Guided buying and decision support: 57%
2. Predictive maintenance and reordering: 49%
3. Dynamic pricing optimization: 48%
4. Intelligent search and discovery: 45%
5. Sales and account intelligence: 39%

These focus areas represent a shift from back-office functions toward AI that directly shapes how buyers discover, evaluate, and purchase products.

Planned AI Investments (Next 12 Months)

1. Advanced analytics and business intelligence: 55%
2. Product data management and content generation: 40%
3. Sales enablement and CRM automation: 39%
4. Supply chain and logistics optimization: 38%
5. Marketing personalization and campaign automation: 37%

Organizations are prioritizing practical, measurable AI applications over experimental ones, focusing on better visibility into existing business operations before layering on more automation.

Key Recommendations for B2B Organizations

Based on the research findings, here are actionable suggestions for manufacturers and distributors:

1. Expect Gradual Implementation, Not Revolution

While some organizations see measurable results, few have fully integrated AI. Plan for a gradual, iterative journey rather than immediate transformation.

2. Start Where AI Is Already Proven

Back-office automation and customer service automation deliver the earliest and clearest wins. Build momentum with these use cases before expanding.

3. Fix Your Data Foundation First

Data quality and legacy systems are primary barriers. Focus on standardizing data formats and ensuring order history accuracy before diving into advanced AI implementation.

4. Establish AI Governance Early

With only 4% having comprehensive policies, organizations risk exposure from unapproved tools and inconsistent oversight. Create clear approval processes and usage guidelines.

5. Take a Process-First Approach

AI should serve as an assistant augmenting employees’ work rather than replacing human expertise. Focus on practical applications that solve defined problems.

6. Invest in Education and Change Management

Adoption has always been challenging in B2B eCommerce. Education, defined limits, and clear expectations are foundational to encouraging use and improving sentiment.

Conclusion

The state of AI in B2B commerce may not match the sweeping revolution headlines promise, but genuine transformation is underway. Successful AI deployments share common threads: they target well-defined problems, are built on clean data, and match realistic expectations.

Organizations best positioned to benefit from AI resist the temptation to invest simply because of industry trends. Instead, they strengthen foundations, embed AI into existing workflows, and put people first.

AI can be disruptive, but it works best when implemented as a practical tool developed with the end-user and customer at the forefront.

About This Report

The 2026 B2B Commerce AI Benchmark is a custom research report produced by B2B Online Insights for OroCommerce. The study surveyed 100 senior decision-makers including C-suite executives, vice presidents, department heads, and directors across B2B manufacturers and distributors. Most companies represented (67%) generate at least $1 billion in annual revenue.

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Ready to optimize your B2B commerce operations with AI? Contact Ekino Vietnam to learn how OroCommerce can help you implement AI-driven solutions that deliver measurable results while maintaining the human relationships that define successful B2B commerce.