How AI and Automation Are Bringing Data-Driven Decision Making to B2B Ecommerce

We’ve only scratched the surface of possible use cases with AI across industries, and particularly AI in B2B ecommerce, but here are some related features that ERP providers have started offering:

Enhancing Personalized Customer Experiences with RAG

One of the big problems that many encounter with AI is its confident incorrectness. How can you rely on the output if you’re not sure it’s actually true?

RAG, or Retrieval Augmented Generation, is an exciting application for AI that grounds results in reality and reduces hallucinations.

Think about it this way: LLMs are trained on data that becomes stagnant and outdated, especially in terms of timely news insights, and when the data you’ve provided gets an update. With the proper setup that involves access to a constantly updated knowledge base, RAG makes it possible to provide AI with a constantly updated data source.

An exciting application of RAG for B2B ecommerce brands? Creating personalized customer experiences. For example, Acumatica offers a built-in RAG solution that incorporates this technology, allowing for a clearer path to customizing customer experiences.

Here’s what that might look like in practice when integrating with an enterprise B2B ecommerce platform:

• Your ecommerce solution calls a RAG model API whenever a customer triggers a query or interaction that requires augmented intelligence.
• The RAG model processes the request using Acumatica data (including products, orders, and documents), returning a relevant answer or data payload.
• Your ecommerce solution then renders the response in the frontend user interface (UI) via search results and product information.

Precision Sales Forecasting with Predictive Analytics

AI isn’t exactly a crystal ball, but it is an expert pattern analyzer — able to achieve insights at scale efficiently. Notably, at a scale that humans, even trained data analysis experts, couldn’t dream possible without it.

As such, AI opens up opportunities for continuous sales and demand forecasting at quicker intervals than a human team could achieve without the technology.

In turn, B2B ecommerce businesses that set up AI systems and consistently use them find themselves at an advantage compared to slow adopters. They can leverage this data to make faster decisions that can have big impacts on the bottom line, rather than relying on data that may already be outdated and lagging behind what’s actually happening.

In addition to incorporating any proprietary data, they can also model situations based on market conditions. For example, by modeling market conditions in tandem with your own customer data, you can adjust forecasting for new and previously unanticipated situations in real-time.

Improving Operational Efficiency

AI and automation are a powerful combination for automating routine tasks: you can think of AI agents as automation with superpowers. Automations that incorporate advanced conditional logic make it possible to reach customer needs effectively at scale.

The drawback with only using automations without AI is that they can be too rigid. Traditional programming doesn’t account for edge cases, and you have to think through every possibility ahead of time, which is an exercise in futility for us humans. There’s always an unanticipated edge case.

Adding AI to automations makes it possible to operate effectively even when all paths aren’t perfectly defined. It creates flexibility that serves customers in useful ways.

Consider these example use cases for incorporating automation with AI for B2B ecommerce businesses, focusing on Microsoft Copilot:

• Wield the power of generative AI to repurpose content from one format to another (i.e., a webinar to a blog article) and to generate formulaic content at a high volume (i.e, meta titles and descriptions and simple product descriptions).
• Auto draft email replies for customer service and sales emails, pulling relevant data from your CRM and other connected sources.
• Use AI agents to auto-detect delivery delays and issues, trigger alerts or order changes, and communicate with suppliers to make adjustments as necessary.

Using automations with AI works best when you have clearly defined processes and standard operating procedures, using that logic to build workflows piloted by expert operators to create efficiencies.

Note that while AI agents are a way to scale decision-making efficiently and effectively, they have some notable limitations to consider and work around. AI is nondeterministic, meaning that it will not produce the same output consistently for the same input. Furthermore, reasoning isn’t its superpower — though it can be an effective thought partner.

Knowing this, don’t let AI become a crutch that you fully outsource decisions to. Rather, use it to produce a “first draft” of doing something, then have a human in the loop (HITL) to verify the outputs.

Optimizing Supply Chain Functions

There’s a big opportunity with AI that starts with inventory management and ends with the final logistics that grant users access to solutions.

By giving AI access to up-to-date data about inventory, shipping costs, and customer preferences for delivery, you can further optimize costs and delivery speed for suppliers and customers alike.

Plus, incorporating AI with customer purchasing data can also help you spot risks before they happen. For example, Acumatica lets you spot patterns that predict fraud and flag anomalies (e.g., separating true late payments from payments that are late for good reasons). These issues are difficult for humans to find on their own and can have a direct impact on business revenue.