How to identify high-value AI use cases in Digital Commerce

High-value artificial intelligence (AI) is perfectly able to enhance digital commerce performance and improve overall customer experience. But to unlock that value, application leaders need to identify the right use cases, be able to fail fast, and build an organization-wide trust in AI.

With the help of AI, digital commerce can improve its efficiency in discovering underlying connections between disparate datasets. There will also be an improvement in clearly defined processes that would, otherwise, require manual processing.

It can manage large amounts of data with many attributes like multichannel and multi-device data, customer behavior data, complex product data, and fraud detection. Likewise, there will be more granularity regarding analysis and orchestration, especially in customer segmentation as well as personalization and sentiment analysis.

With all of these changes now in reach, there is no wonder that by 2020, AI is expected to be used by 80% digital commerce organizations, and will be among the top five priorities for CIOs. What' more, 30% of revenue growth will be attributed to AI technologies, and by 2022, early adopters will have roughly four virtual assistants that will aid them in day-to-day operations. 

The Key Challenges 

The customer experience, the operational efficiency, and commerce performance will need to improve. The competition will get fiercer, and customers will have higher expectations.

It is, thus, left to the application leaders to investigate how AI will help improve digital commerce performance by looking at various solutions in the market and determine which ones will provide the best value for their organization. 

Also, CIOs need to remember that not all employees will follow the predefined processes or take any advice from AI application, favoring, instead, their instincts. 

Recommendations for AI Use Case Identification

You need to start by documenting a pool of use cases that represent repetitive pain points or ones that involve complex decision-making processes that can be measured against business objectives.

Today's AI and machine learning technologies are best suited for three distinct categories of use cases when it comes to digital commerce. These are pattern recognition and classification, prediction, and natural language processing (NLP). 

Depending on your organization's needs, you will have to prioritize these use cases of higher value. These are those that are more complex, more frequently used, and those that are ready for commercial production. You will also have to make sure that vendors are capable of delivering. Do this by reading their case studies, talking to references, trying out demo versions, and by getting peer feedback or running pilots in Mode 2. 

When it comes to employee trust of AI, merely explain the reasoning and showcase the technology's effectiveness. Also look into ways to align employee incentives with the shifts in work patterns, as a result of AI. 

Only by identifying high-value AI use cases, makes you able to determine which types of technologies will fit best with your digital commerce organization. If you want to learn more about AI in digital commerce, feel free to contact us directly.