A version of this article was published on Forbes.
AI is increasingly becoming an integral part of digital transformation. It’s fueling search engines, virtual assistants and eCommerce product recommendations. For B2B eCommerce businesses, AI can be a tool for greater personalization, improved decision-making ability and gaining a competitive advantage.
But, AI in eCommerce does have its challenges and limitations. It can be expensive to implement, which leads B2B business to question if there are cost-effective alternatives.
LimitationsTo AI In Commerce
AI applies a model or algorithm to process large amounts of data and predict future outcomes. Despite it being highly sophisticated, it has to be customized and tested to ensure it’s performing the way it’s intended to. AI doesn’t work in isolation but within the confines of existing tools, platforms and processes. Implementing an AI solution is a costly and somewhat uncertain endeavor. Leaders and AI advocates fail to identify valid use cases, lack necessary data, don’t have the right people or face a combination of these challenges.
Limitation #1: Identifying Realistic Use Cases
Over 65% of B2B buyers are likely to switch brands that don’t offer a personalized experience, so the most popular use cases for AI in B2B eCommerce focus on UX. Other elements best handled by AI include product recommendations, sentiment analytics and computer vision.
For example, Amazon invested heavily into its AI-powered recommendation engine to understand customers at every touchpoint. Amazon Personalize examines previous purchases to train and optimize the algorithm to offer better product recommendations. The key to Amazon’s success is their dedication to creating the most personalized customer experience.
Another example is Home Depot’s AI department, which used natural language processing (NLP) to build a model that deciphers customer reviews left on its website. It allows other shoppers to briefly see the pros and cons of each product without reading through hundreds of reviews. Their NPL processor led to more engagement and higher conversion rates.
Limitation #2: Getting Data In Order
According to an O’Reilly report, 15% to 20% of AI practitioners cite problems with missing or inconsistent data. Data isn’t a raw material. It requires analysis and preparation before it is fed into the algorithm. This can lead to human error since the parameters of the data must be defined manually before the AI processes data as it’s intended to do.
Limitation #3: Building An AI Department
AI requires a team of data professionals, engineers and BI analysts to maintain the AI properly. Unfortunately, AI professionals are not only expensive, but they’re hard to find. The talent gap in the AI industry spans all skill and experience levels and can possibly even be industry specific.
Limitation #4: Culture And Risk Management
AI is still a relatively new technology and everyone has a different idea of what it is and what it can help a company achieve. For the best chance at success, incorporate all stakeholders into your AI strategy and get them involved in setting goals. Full visibility among all stakeholders leads to the greatest chance of success with internal adoption. It also means that you’ll end up with an algorithm that is much more accurate at what it’s intended to do.
Alternatives To AI
You might not need AI to solve your problem. There are other digital alternatives that can do the job for less. For many eCommerce functions, a rule-based workflow automation engine can deliver the same results at a reduced cost. For example, a product recommendation algorithm might not need AI, and neither does a personalized quoting or checkout process.
Automated workflows and AI both involve computers to perform tasks. Once a workflow is created, the behavior remains the same. Whereas AI will teach itself and take different courses of action based on the data it reviews.
AI is the better choice for evaluating multiple unknowns such as demand fluctuations, other customers’ activity and anticipatory purchases.For example, Amazon is well-known for using algorithms to strategically place items in warehouses closest to customers before they’ve even added these items to their cart. These systems anticipate individual tastes as well as seasonal, weather and traffic conditions to deliver products as fast as possible.
Yet AI is unnecessary to simply identify the best warehouse to ship products. An automated workflow can route orders to the warehouse closest to the customer, thus creating the same experience that Amazon provides with AI. In this instance, an automated workflow can be implemented faster and cost much less. Automated workflows can be the perfect solution for tasks that are repetitive or involve handling data.
Make Sure AI Works For You
With more and more companies turning to digital solutions and eCommerce, the desire for greater operational efficiency is increasing and demand for relevant customer experiences is at an all-time high. It’s only natural that B2B sellers are seeking out trending technologies.
However, any AI solution must provide value that exceeds the costs of administering data and building a dedicated team. Otherwise, you are working for AI; it’s not working for you. AI can present a host of challenges. Make sure you have a valid use case and the resources to invest to see the project through. Consider alternatives such as automated workflows. All too often, the simplest solution is often the best.