AI Adoption in the Enterprise: How B2B Leaders Extract Value

Navigating AI adoption in the enterprise today can feel incredibly isolating. Thirty-seven billion dollars flooded into the market last year – the fastest software category expansion in history. The pressure on business leaders to deploy is immense, and on the surface, it looks like everyone is succeeding.

Yet, when the door closes, and the CFO asks for the financial payoff, the room goes quiet.

Our latest survey results reveal a stark divide in how AI deployments in manufacturing and distribution are performing:

• Only 17% report achieving a significant return on investment.
• Nearly half (48%) say the technology is “somewhat effective,” meaning they see positive movement on a dashboard, but no transformational business value.
• Another 15% admit their deployments have delivered no expected value at all.

If you’re feeling the pressure to justify your recent AI investments, this article is for you. The lack of immediate ROI is not a failure of vision or effort. B2B commerce is incredibly complex, and finding the right path forward requires a different playbook than what the broader market is selling.

Where Enterprise AI Adoption Stands Today

When you look at the broader state of AI, the narrative feels contradictory. McKinsey reports that 88% of enterprises now use some form of generative AI. Yet, an MIT study of 300 deployments found that 95% of pilots deliver zero impact on the P&L.

Why the disconnect? Part of it comes down to the industry itself.

In other industries, the investment headlines make sense. Life sciences organizations are training highly specialized models for drug discovery. The public sector is deploying tools to analyze massive civic datasets.

But B2B commerce operates on a different kind of complexity. Larger companies in our space can’t just buy an algorithm to handle heavily negotiated contracts, custom pricing logic, and sprawling supply chains. Scaling these tools across complex business functions requires deep integration.

Four Stages of AI Maturity in B2B

When we map out the maturity of B2B commerce specifically, the reality of scaling AI becomes clear. 

Our AI report benchmark reveals four distinct stages of adoption:

• Fully integrated: Only 8% have deployed AI enterprise-wide.
• Scaling: 37% have deployed it in multiple functions with measurable results.
• Emerging: 35% are running tools in isolated areas and seeing limited results.
• Early stages: 19% remain stuck piloting in limited areas.

This distribution aligns closely with macroeconomic data from BCG, which shows a massive “emerging” bucket of companies experimenting, and a tiny elite building future-proof infrastructure.

The gap between these groups is widening. The leaders capturing significant value are rapidly compounding their competitive advantage. Meanwhile, two-thirds of the market remains caught in the middle, trying to extract ROI from disconnected systems.

To understand why so many initiatives stall in that messy middle, we have to look honestly at the barriers holding teams back.

What Stops AI Initiatives from Scaling

When AI initiatives fail to deliver, the immediate instinct is to blame the tech. But our findings point to a different culprit.

The barriers preventing companies from achieving long-term AI success have shifted from technological limitations to deep-rooted operational hurdles. When we asked B2B leaders to name their top roadblocks, here is what they told us:

• Legacy system integration (53%)
• Concerns about data privacy and security (46%)
• Resistance from employees or sales teams (41%)
• Unclear use cases or business value (33%)
• Lack of executive buy-in (33%)

Let’s break down exactly why these specific barriers are paralyzing so many AI investments, and why feeling stuck here is a completely normal part of the modernization journey.

The Legacy Integration Trap

This is the heaviest anchor holding B2B companies back. You likely inherited multiple ERPs through acquisitions, alongside custom-built portals, and legacy databases.

McKinsey accurately calls this the “great AI and ERP divide.” AI models require unified, context-rich data to function. If your pricing logic is trapped in a twenty-year-old backend system, the algorithm fails.

This is precisely why we advocate for the Strangler Pattern, where you use a unified commerce layer to modernize your architecture without suffering through a massive ERP rip-and-replace.

Data Security and the Shadow AI Problem

B2B commerce runs on highly sensitive information from negotiated contract terms to detailed purchase histories. Feeding this proprietary information into public AI technologies poses an enormous data privacy risk.

Compounding this issue is “shadow AI.” External research from Snowflake shows that 57% of employees use unapproved tools at work. Your sales reps might be uploading sensitive customer histories into consumer-grade chatbots today just to draft quotes faster.

Employee Resistance and Relationship Fears

B2B sales rely entirely on trust and long-term relationships. When you introduce new AI capabilities, your sales teams naturally worry that heavy automation will disrupt the human touch. But beneath that sits a deeper anxiety: is this tool here to help me, or replace me?

Our data captures this hesitation. Genuine enthusiasm is incredibly rare; only 8% of employees feel “very positive” about these tools, while 40% sit firmly in “neutral.” They’re waiting for proof that the software will eliminate their administrative drag, not their jobs or add to their workload.

If you deploy without empathy for those fears, your team will simply bypass the technology. As one executive in our survey admitted, when senior reps don’t trust the data, they ignore the algorithm entirely and just keep doing things the old-fashioned way.

Lack of “AI-Ready” Data Quality

While only 23% cited data quality as their absolute top barrier, it remains a silent killer across all business units. In our industry, “clean data” doesn’t just mean a nice, standardized product catalog. It means consistently parsing massive amounts of unstructured data and aligning it with credit limits, supplier rules, and inventory levels across disconnected systems.

The AI Governance and Compliance Blind Spot

While many leaders are hyper-focused on deployment, the legal landscape is shifting quickly.

The EU AI Act takes full effect in August 2026, introducing strict regulatory compliance rules and heavy financial penalties. Yet, our benchmark shows that only 4% of respondents have comprehensive governance policies in place.

When you deploy autonomous tools without a firm governance framework, meeting complex compliance requirements becomes almost impossible.

These hurdles explain why scaling AI feels so frustrating for the majority of the market. But acknowledging them is the first step toward fixing the foundation.

How High-Performers Extract Business Value from AI

The companies generating significant financial returns faced the exact same legacy ERPs, data silos, and skeptical sales teams as the rest of the market. The difference is how they responded.

Instead of treating artificial intelligence as a shiny new toy, the leaders treat it as a disciplined operational workflow. They’re leveraging AI to fix specific, broken business processes.

When we isolate the data from the most successful companies in our benchmark, a very clear playbook emerges. Here is what they are doing differently to extract massive efficiency gains and drive enterprise-wide EBIT

1. They deploy in a pragmatic sequence

The companies winning today build from the ground up. They secure their initial ROI by automating unglamorous backend operations long before touching complex, revenue-facing applications. Our data shows staggering implementation rates for operational use cases:

• Back-office automation: 81% implemented
• Customer service automation: 73% implemented

By automating high-friction tasks like manual order entry first, they build internal trust and secure immediate time and cost savings. Only after proving AI ROI in the back office do they move up the stack to complex areas like inventory forecasting and sales enablement.

2. They don’t choose between efficiency and customer experience

There is a persistent myth that adopting AI requires sacrificing the human touch. The successful group proves otherwise. When asked about their top outcomes, they reported enhanced employee productivity (54%) alongside improved customer satisfaction (51%).

Because the technology handles the administrative drag, their sales and support teams have more time to focus on high-value buyer interactions.

3. They buy embedded platforms instead of building from scratch

Unless your core business is software engineering, hiring an army of data scientists to build custom machine learning models is a massive financial risk. The leaders know this.

• 60% of organizations rely on AI tools that are deeply embedded within their enterprise software (like their commerce platform, ERP, or CRM).
• External MIT research backs this up: vendor-built deployments succeed twice as often as internal builds (67% vs. 33%).

By embedding intelligence directly into existing processes, these companies ensure the algorithms immediately understand their complex pricing and customer hierarchies.

However, relying on embedded AI introduces a different challenge: evaluating the platform itself. In our recent review of seven major B2B commerce platforms with AI, we found that many out-of-the-box AI tools are adapted from consumer retail models. They handle generic tasks well but struggle with wholesale-specific workflows without heavy custom configuration.

Successful organizations scrutinize whether the embedded intelligence inherently understands B2B complexity, while still prioritizing open systems for avoiding vendor lock in as the underlying language models evolve.

4. They co-develop with their buyers

Successful organizations refuse to build in a vacuum. A massive 86% of B2B companies are engaging their customers directly regarding AI features, either by validating concepts before building (63%) or actively co-developing pilots together (23%).

In a relationship-driven industry, treating your buyers as design partners ensures you only build tools they will actually use.

5. They establish basic governance immediately

You can’t deploy advanced technology without a framework for safe knowledge management. While only 4% of respondents have comprehensive governance policies, 62% have established basic, working guidelines.

That means 66% of the market has something in place to protect their proprietary data. Governance gives employees the psychological safety to use the tools without fear of exposing sensitive contract terms.

If you follow this playbook – fix the foundation, establish governance, and embed the tools – you position your organization perfectly for the next massive shift in the market – agentic AI.

Putting the Playbook into Practice: DiversiTech

Imagine trying to roll out advanced AI when your sales reps are still manually typing out faxed purchase orders. That was the daily reality for DiversiTech, North America’s largest HVAC distributor.

Through years of acquisitions, they inherited 12 different legacy ERPs. But instead of doing what most organizations do – slapping a flashy AI chatbot on top of the mess – they took a pragmatic approach to their AI use.

• Before deploying any algorithms, they implemented OroCommerce to act as a unified commerce layer. The platform sat directly between their buyers and those 12 fragmented ERPs, creating one single, clean source of truth for all their pricing logic, inventory, and customer records.
• With that clean data hub in place, they deployed OroCommerce’s native AI-powered order intake tool (see how it works here). It reads unstructured, emailed PDFs and automatically normalizes the data into draft orders, routing them to the correct legacy ERP in the background.
• As a result, the company saw an immediate 20% productivity gain, entirely avoiding the need to build a massive, custom translation system to handle their ERP consolidation.

Because they solved the architectural problem first, DiversiTech is now comfortably testing predictive sales alerts to empower better internal decision-making. They earned the right to innovate because they built the foundation to support it.

Preparing for the Next Phase

When we asked B2B leaders where they expect the greatest AI’s impact on customer experience in the near future, they pointed directly to complex, revenue-driving functions:

• Guided buying and decision support (57%)
• Predictive maintenance and reordering (49%)
• Dynamic pricing optimization (48%)
• Intelligent search and discovery (45%)

These are incredibly powerful AI capabilities. But look closely at what they require to function safely. Guided buying demands perfectly clean product data. Predictive reordering requires real-time inventory visibility. Dynamic pricing needs strict, hard-coded margin rules and approval workflows.

You can’t deploy these advanced features if you haven’t yet solved the legacy integration barriers holding your data hostage. The companies ready to deploy guided buying today are the exact same companies that spent last year automating their order entry and cleaning their catalogs.

You simply cannot skip steps.

The Agentic AI Reality Check

This brings us to the loudest buzzword in the market today: agentic AI.

Consumer retail is aggressively testing autonomous AI agents that can browse, negotiate, and purchase goods entirely on their own.

For B2B commerce, the timeline and the risks look very different. Gartner warns that upwards of 40% of agentic projects will be canceled by 2027.

In our industry, successful modernization relies entirely on human-AI collaboration. The technology is meant to act as a high-powered accelerator for your sales team, handling the heavy data lifting while human experts maintain absolute control over complex negotiations and customer relationships.

The architecture must include strict guardrails and seamless exception handling, ensuring that whenever the system encounters a highly sensitive negotiation or a pricing anomaly, it instantly routes the task to a sales rep.

The 2026 AI Outlook: Where B2B Commerce is Investing Next

The window for endless experimentation is closing. As we look to the near future, our findings suggest that enterprise AI adoption is moving into a highly disciplined phase. 

We’re seeing several trends that prove senior leaders are shifting their AI strategies away from hype and toward hardcore foundation building.

When we asked executives where they plan to direct their AI investments over the next 12 months, the priorities were:

• Advanced analytics and business intelligence: 55%
• Product data management and catalog enrichment: 40%
• Sales enablement and CRM automation: 39%
• Supply chain and logistics optimization: 38%

These investment priorities mark the end of the plugin era.

Most organizations treat AI like a widget. They bought third-party AI tools, taped them to the outside of their legacy systems, and expected them to effortlessly process massive volumes of unstructured data.

That approach is collapsing under its own weight.

Renting external, proprietary models creates an immediate operational ceiling. A standalone application doesn’t know your contracted pricing tiers or custom shipping logic.

When you force disparate tools to constantly ping a 15-year-old ERP, you introduce severe latency. In complex B2B environments, handing sensitive commercial data over to isolated external systems is a massive liability.

The Shift to Native Infrastructure

The companies pulling ahead have abandoned the bolt-on strategy. They’re shifting their AI investments directly into their core data engines. Funding eCommerce, PIM, and supply chain visibility proves that leaders now understand a fundamental rule: intelligence must live natively where the business logic lives.

When the algorithm shares the exact same database as your account hierarchies, you eliminate the integration tax. The system automatically inherits your security rules. It reads your inventory directly.

While organizations are using AI at unprecedented volumes, our key findings highlight that you can’t buy your way out of technical debt with a smarter algorithm.

To achieve true organizational readiness and extract the most value, your focus must shift from the software to the architecture.

The path to sustainable enterprise AI adoption requires unifying your data foundation long before you ever delegate authority to AI agents.