The integration of AI into B2B settings marks a seismic shift in how businesses operate, mirroring how consumer ecommerce trends have impacted business buying over the last decade.

The advancements in artificial intelligence (AI) we have seen over the last 18 months amount to the biggest technological breakthrough since the dawn of the internet.

Deploying AI within B2B settings will reshape how businesses buy and sell items in ways that are still not fully understood. Here at Bowery Capital, we specialize in backing early-stage business software startups and we are already seeing AI-driven applications being purpose built to solve some of the longstanding challenges shared by B2B buyers and sellers.

How companies manage purchasing decisions, review trade credit requests, and catalog product-level inventory data are three areas where we already see an opportunity for AI-native challengers. We are still in the early innings of this technological shift, and as in the case of the internet, rates of adoption will vary wildly across industry segments. Nevertheless, this move toward AI being baked into core B2B workflows is already well underway.

Here are four ways AI is establishing its place in B2B commerce.

1. B2B procurement will transition away from today’s catalog model toward AI-enabled recommendation engines.

Digital procurement over the last decade has relied on user experience like browsing a physical catalog. This means buyers have been left to their own devices to find the right product and price point with some limited guidance from the buying platform in the form of suggested products. This is also true of verticalized industry marketplaces, a category we have spent years researching and investing in.


With the application of large language models (LLMs), procurement platforms can provide business buyers with highly targeted, real-time product recommendations. These recommendations will be based on a combination of user inputs, prior purchasing history, and even personalized benchmarking, which helps inform buyers about the anonymized supply chain decisions of their peers.

The widespread adoption of AI-enabled buying may also give birth to a new wave of sales tech designed to make products appealing to these new AI decision makers. This is analogous to how B2B sellers ramped up their SEO efforts as Google became the new front door for buyers. We are also seeing efforts around using AI to automate not just product selection, but also the negotiations that accompany many B2B sales where the manufacturer suggested retail price (MSRP) is often just a starting point for discussion.

2. The rise of AI-driven trade credit brings new data sources and real-time tracking.

Trade credit — the extension of net 30/60/90-day terms to buyers by B2B sellers — is at the core of today’s large distribution businesses. Construction supply houses, electronics wholesalers, and food and beverage distributors are just a few examples of distribution businesses who frequently extend trade credit to their buyers.

Historically, trade credit has been based on point-in-time snapshots of creditworthiness and often relied upon sellers querying large, standardized databases like Dun & Bradstreet, which function as the equivalent of a B2B Fair Isaac Corp. (FICO) score. There is a new wave of trade credit management startups that are building software, which is centered around deploying AI to incorporate new, real-time data sets to better inform underwriting decisions, automate credit approvals, and speed up the time to a decision for vendors considering a buyer’s creditworthiness.


It remains to be seen whether Dun & Bradstreet and the other incumbents will roll out AI product expansions, or if the future of trade credit management will be captured by today’s early-stage challengers, but it is clear that AI will allow for credit management, which is faster, draws on a wider pool of data, and can proactively monitor credit risk at a level far beyond what has previously been possible.

3. The challenge of product data management for distributors.

Product data management is a pain point for every large B2B company — cleaning up, standardizing, and structuring the tens of thousands of product codes and SKUs that OEMs and distributors rely on for internal operations is no small task. As a fund that evaluates dozens of B2B marketplaces each year, this challenge is made even more difficult in the marketplace setting. A marketplace needs to manage, standardize, and group SKUs from hundreds of different vendors in a single platform. Worse, each of the brands a marketplace may be platforming will all have slightly diverse ways of tracking and describing their product lines, which exponentially complicates matters.

Recent advancements at the LLM layer have set the stage for new AI-enabled product inventory management systems (PIMs), which can take on the inventory classification challenges that plague third-party industrial distribution businesses.

This data mess distributors need to deal with is referred to within industry as the “data problem” and is caused by SKU creep and lack of standardization across OEMs in the terminology they use to classify comparable goods. A mid-sized distributor may have a product catalog of more than 1 million products, and today, many are forced to rely on armies of offshore labor provided by BPOs to clean, structure, and normalize this data.


4. AI, small language models and modern data asset management in B2B commerce

Some more tech-savvy distributors have begun experimenting with OpenAI’s GPT API to build in-house product data management tools, but most distributors lack this technical proficiency and would be better served buying something off the shelf for data asset management. Breakthroughs in small language models (these are models which are trained on smaller datasets than the well-known foundation models like ChatGPT or Claude) provide another interesting opportunity.

These small language models specialize in doing specific tasks very well. SLMs could be trained on a collection of original equipment manufacturing (OEM) product catalogs and distributor product descriptions, and then serve as autonomous assistants to help with data management and normalization. In addition to avoiding administrative headaches that accompany product data normalization, this kind of tech can help distributors arm their sales force with compelling product literature, run analytics on their product catalog to assess SKU-level performance, and offer more accurate and intuitive ecommerce experiences. We envision the next few years will see the rise of AI-native master data management platforms that provide next-gen product listing and information management capabilities.

The integration of AI into B2B settings marks a seismic shift in how businesses operate, mirroring how consumer ecommerce trends have impacted business buying over the last decade. Companies quick to adopt AI-driven strategies will not only solve persistent industry challenges but also gain a substantial competitive edge. This technological evolution promises a future where efficiency, precision, and innovation redefine B2B commerce.

About the author

Patrick McGovern is a senior associate at Bowery Capital, a venture capital firm that specializes in business software. Prior to Bowery, McGovern worked as an independent consultant advising early-stage B2B SaaS and marketplace businesses. He can be reached at [email protected].


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