As the pandemic lingers on, digital commerce remains at the forefront of every business. The swift change from brick and mortar to online commerce created many challenges, some of which are just emerging. For example, customer expectations have become more difficult to gauge as there is very little in-person interaction between the buyer and the business. These buyers are now invisible customers, represented by a cookied customer ID.

As businesses grow accustomed to this digital new normal, the key differentiator among digital selling organizations will be customer experience (CX). Once online, every business is starting to look alike to the consumer. The most noticeable differences that consumers can feel is how their CX changes. To deliver a positive and compelling CX that is consistent across every touchpoint, brands must have some way to see the invisible customers in order to understand their needs and meet their expectations. This is challenging due to the volatility of customer expectations. Yet businesses must deliver their product and service in a way that meets the customers’ changing expectations to gain their loyalty.

How Can Organizations Prepare to Meet Customer Expectations?

The reality is that every customer is unique and should be treated as such, especially when examining the customer through the vast amounts of behavior data with many attributes. Organizations can’t take a cookie-cutter approach to meeting customer expectations. Artificial intelligence (AI) can play a significant role in helping businesses understand their behaviors and offer a good gauge of their expectations.

Although the customers are invisible to the eyes, they often leave behind plenty of digital footprints (i.e. data) while they engage a business on digital channels. These digital footprints provide a mechanism for the business to truly know them and understand their priorities. This is not without its challenge, as this data is often messy, intermingled and in many cases provides an incomplete picture. This is where AI can help.

AI technology can analyze massive amounts of data across the different digital channels to create recommendations and decisions optimized for each customer persona. The technology can quickly create human-like decisions with speed and scale while personalizing recommendations for different buyers. While customers don’t expect to be delighted in every business engagement, customers do expect a consistent and effective experience in each sale. AI can accommodate the type of channel, product, price and context that each customer desires at the right time. It would be near impossible to truly understand the nuances and dynamic complexities of every buyer’s preferences without the use of AI.

Related Article: Choosing the Right AI for Your Business Goals

Why Isn’t Every Business Using AI to Get Ahead?

There are three overarching fears among organizations in deciding to implement AI-driven technology in their business. First, many organizations still believe that AI is a black box and don’t understand how it comes to certain decisions. This also creates a fear of losing control. They worry that putting their company into someone else’s hands — never mind the hands of an AI — will create chaos despite their current inefficient and ineffective business processes. Ultimately, organizations are afraid of giving control to an unfamiliar technology that, in their eyes, may or may not even make the right decision.

Second, AI can be cost restrictive to produce and maintain in-house. The intense infrastructure needed to operate AI at a global scale is expensive as it requires many roles and highly specialized talent. Even if companies are able to acquire in-house talents and overcome the cost barrier, they often lack patience to allow AI technology to optimize its performance over time. Besides human and financial resources, AI also needs time resources to learn and become familiar with a company’s unique data as well as feedback from the company in order to properly fine-tune its decision engine. Thus, unless a company has the resources of a large organization like Facebook or Google, it is unlikely to have specific teams dedicated to the deployment and maintenance of AI.

Learning Opportunities

Lastly, companies fear that by relying too much on AI, their CX may eventually become less differentiated from their competitors using similar technologies. Since AI and the machine learning (ML) algorithms behind the AI are invisible to the end consumers, they won’t be able to distinguish one AI system from another. Consumers can only feel the resources and efforts they spent as they progress through the multiple touchpoints of a brand. If everyone is using AI online to personalize their CX with greatly improved efficiency, how can a company differentiate from its competitors? All technologies (including AI) make a huge difference initially between those who use them and those who don’t, but when that technology is commoditized and everyone is using it, they tend to level the playing field in the long run.

Related Article: The 4 Foundations of Responsible AI

The Democratization of AI Is Vital

Implementing AI in the real world is no doubt challenging. But there is a potential solution for hesitant businesses. Many organizations today have captured huge amounts of data specific to their business operations to help inform their decision making processes. Since these data are specific to the companies, they can provide a unique lens to understand their customers. Although many companies today do manage to hire a few data scientists to help them make sense of these data internally, very few have the full spectrum of expertise required to develop them into scalable business solutions that are secured and deployable to the cloud. Consequently, traditional enterprises must rely on vendors to provide specific AI solutions to automate their business decision. However, this will ultimately lead to the loss of differentiation as AI vendors make their solutions more accessible to everyone.

What businesses need today is an additional layer of intelligence from their vendors — the ability to extend the vendor solution with their unique data asset and home-grown models. Although most companies will still rely on the vendor to provide the cloud-based AI solution due to the resource intensiveness of a full AI operation, an AI solution that is extensible will allow them to leverage their existing ML models to make real-time business decisions using company and customer specific data. Since extensible AI can ingest proprietary data assets from a business, this enables companies to leverage vendor provided AI solutions without the the eventual loss of differentiation.

An AI that takes into account a company’s unique data and existing ML models will also produce decisions that are more intuitive and less disruptive to the company’s decision makers. This makes the AI recommendations easier to understand and will increase adaption and therefore provide greater agility in a changing business environment. Extensible AI solutions will help organizations save valuable time and money by circumventing the implementation challenges while increasing adoption, differentiation and efficiency within the business.

Digital selling is dominating the commerce industry as the market is riddled by the effects of the pandemic. A more democratized approach to AI can help organizations leverage their proprietary data and ML models to better satisfy their customers’ expectations consistently, which will in turn improve the overall CX. Buyers today are rarely interacting directly with the people behind the business, which makes it crucial for them to differentiate themselves by improving CX. This requires AI to make sense of the big data generated by the customers to understand their expectations. And making AI more extensible is one way to meet their customers’ expectations while maintaining brand differentiation in an increasingly undifferentiated online world. 

fa-solid fa-hand-paper Learn how you can join our contributor community.