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Three-quarters of 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 per cent, respectively.
Gartner predicts that by 2020, AI will be used by at least 60 per cent of digital commerce organisations and that 30 per cent 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.
The survey found a wide range of applications for AI in digital commerce. The top three uses are:
1. Customer segmentation;
2. Product categorisation; and
3. Fraud detection.
Top challenges
Despite early success, digital commerce organisations face significant challenges implementing AI. The survey shows that a lack of quality training data (29 per cent) and in-house skills (27 per cent) are the top challenges in deploying AI in digital commerce. AI skills are scarce and many organisations don’t have such skills in-house and will have to hire from outside or seek help from external partners.
On average, 43 per cent of respondents chose to custom-build the solutions developed in-house or by a service provider. In comparison, 63 per cent of the more successful organisations 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 Ms Shen.
“Organisations looking to implement AI in digital commerce need to start simple,” said Ms Shen. “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.”
On average, respondents spent $1.3 million in development for an AI project in digital commerce. However, of the more successful organisations, 52 per cent spent less than $1 million on development, 20 per cent spent between $1 to 2 million, and 9 per cent spent more than $5 million.
To increase the likelihood of success, Gartner advises digital commerce leaders to:
· 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 approach. Break down complex business problems and develop targeted solutions to drive home business outcomes. Use AI to optimise existing technologies and processes rather than to try to develop breakthrough solutions.
