Avoid AI pitfalls: Understanding how your business sells is key to a smooth AI deployment

Many companies struggle with where and how to implement artificial intelligence (AI) into their workflows. We suggest applying AI to the highest-value processes in your company — sales and order entry — because the return on investment (ROI) can be fast and substantial.

With AI, quote turnaround can go from 12 hours to 20 minutes, training time drops by 90%, and sales productivity goes through the roof. A simple, single-line order goes from 40 clicks to five, and 10 screens to four. It’s a compelling argument for applying AI to the customer interactions that get you paid.

At DataXstream, we do this upfront – before AI is applied – so we can create the right machine learning models tailored to your business, and then apply them to the highest-value processes in your company to drive sales. This goes beyond the lift and shift integration of data from the legacy system to the new platform. By applying AI, we go beyond the traditional ways of handling information to customize and automate your transition. Here’s how it works.

Data requirements for artificial intelligence

Traditional data mapping requires manually manipulating information to migrate it to the new platform. Typically, we see companies take digitized data and apply a standard tagging model that requires continuous adjustments as you go along. Companies may say they can give you 100% accuracy using their AI solution “right out of the gate,” but that is not entirely true. Most solutions require significant human rework to run smoothly.

The biggest differentiator of our OMS+IA platform is that we personalize our matching models based on how the customer looks for materials when building an order or a quote.

For example:

• Do they punch in the manufacturer’s number first?
• Do they always look up the material by the UPC?
• Do they have simple customer descriptions, or are they complex?

Why is this important? When it comes to document automation using machine learning technology and AI to start automating the order process, material matching models must align with how the company searches for their materials.

We look at how our customers sell, their business practices, typical product queries, the complexity of product descriptions, and more. It’s a process that sets the stage for the data migration, go-live, and ultimate success.

While data may be similar by industry segment, each distributor runs their business differently. We look for these points of differentiation. For example, we can tweak our application to prioritize how a customer query returns product data. You may want a list of brands first. Or you may want the list prioritized by what’s in stock. The data returned to a customer query can be personalized based on how the customer searches and what’s important to them.

This personalization applies across SAP SD order documents, whether a quote, an email inquiry, a return, a credit, or the order itself. We design the solution to accommodate your business. That’s unusual in an off-the-shelf product.

In addition to saving time, AI is a hedge against bad or poorly maintained data, typos, and other complicating factors in distributors’ datasets.

AI algorithms can:

1. Identify the patterns, relationships, and structures between datasets to lessen their complexity.
2. “Learn” from existing models to increase accuracy over time.
3. Spot and correct errors and inconsistencies in the data for more accurate labeling.
4. Automatically adapt to changes in the data to ensure labels remain accurate.
5. Improve the accuracy and speed of data matching, cutting time spent on manual tasks.
6. Scale as the volume of data increases.

DataXstream customers work with our data scientists to model the data, personalizing it to their business. With AI, this process is strategic, not manual. Human decision-making remains at the forefront of this approach. However, you don’t waste time on manual data mapping—AI does the heavy lifting. In this scenario, AI and automation are not replacing human expertise but elevating it.

We can achieve highly accurate output accuracy at go-live while removing many manual, time-consuming data mapping processes. Our AI model is designed for oversite to allow for human correction to further train the model with the goal of 100% accuracy.

A real-world example