70% of AI Projects in Digital Commerce Successful: Survey


Most of the digital commerce organizations using AI said they are seeing double-digit improvements in the outcomes they measure.

Use of artificial intelligence (AI) in digital commerce is generally successful, a new survey has found.

Gartner, Inc., the world’s leading research and advisory company, conducted a survey of 307 digital commerce organizations that are currently using or piloting AI to understand the adoption, value, success and challenges of AI in digital commerce. About 70 percent of digital commerce organizations surveyed reported that their AI projects are very or extremely successful.

The respondents included organizations in the U.S., Canada, Brazil, France, Germany, the U.K., Australia, New Zealand, India and China.

Three-quarters of the respondents said they are seeing double-digit improvements in the outcomes they measure. The most common metrics used to measure the business impact of AI are customer satisfaction, revenue and cost reduction. For customer satisfaction, revenue and cost reduction specifically, respondents cited improvements of 19, 15 and 15 percent, respectively.

Gartner predicts that by 2020, AI will be used by at least 60 percent of digital commerce organizations and that 30 percent of digital commerce revenue growth will be attributable to AI technologies.

“Digital commerce is fertile ground for AI technologies, thanks to an abundance of multidimensional data in both customer-facing and back-office operations,” said Sandy Shen, research director at Gartner.

Top uses and challenges of AI in digital commerce

The survey, conducted from June to July 2018, also found a wide range of applications for AI in digital commerce. The top three uses being Customer segmentation, Product categorization, and Fraud detection.

Despite early success, digital commerce organizations face significant challenges implementing AI. The survey shows that a lack of quality training data (29 percent) and in-house skills (27 percent) are the top challenges in deploying AI in digital commerce. AI skills are scarce and many organizations don’t have such skills in-house and will have to hire from outside or seek help from external partners.

On average, 43 percent of the respondents chose to custom-build the solutions developed in-house or by a service provider. In comparison, 63 percent of the more successful organizations are leveraging a commercial AI solution.

“Solutions of proven performance can give you higher assurance as those have been tested in multiple deployments, and there is a dedicated team maintaining and improving the model,” said Shen.

He suggested that organizations looking to implement AI in digital commerce need to start simple. “Many have high expectations for AI and set multiple business objectives for a single project, making it too complex to deliver high performance. Many also run AI projects for more than 12 months, meaning they are unable to quickly apply lessons learned from one project to another,” he explained.

On average, respondents spent $1.3 million in development for an AI project in digital commerce. However, of the more successful organizations, 52 percent spent less than $1 million on development, 20 percent spent between $1 to 2 million, and 9 percent spent more than $5 million.

Tips for successful use of AI in digital commerce

To successfully implement AI, Gartner advises digital commerce leaders to follow the following points :-

  • Assess talent: If there is insufficient AI talent in-house to develop and maintain a high-performance solution, go with a commercial solution of proven performance.
  • Aim for under 12 months for a single AI project: Divide larger projects into phases and aim for under 12 months for the first phase, from planning, development and integration to complete launch.
  • Ensure enough funding: Allocate the majority of the budget to talent acquisition, data management and processing, as well as integration with existing infrastructure and processes. Enough funding also helps secure high-performance solutions.
  • Use the minimum viable product (MVP) approach: Break down complex business problems and develop targeted solutions to drive home business outcomes.

To achieve better result, Gartner suggests to use AI to optimize existing technologies and processes rather than to try to develop breakthrough solutions.


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