Capgemini calculates a $300 billion opportunity for those retail companies that are able to scale and expand the scope of their existing deployments. However, it is not straightforward. The report also found that just 1 percent of use cases by retailers have achieved this level of deployment today.
The study’s title is “Retail superstars: How unleashing AI across functions offers a multi-billion dollar opportunity”. It looked at 400 global retailers that are implementing AI use cases at different stages of maturity. This is a group that represents 23 percent of the global retail market by revenue. The study further included an extensive analysis of public data from the world’s largest 250 retailers, by revenue.
Comparing this data to 2017 equivalents, it delivers a series of reality checks that not only show how far AI has come in terms of concrete returns, but how much value it can deliver if retailers begin to prioritize less complex deployments, and diversify their focus.
The main insights from the report
- Over a quarter of retailers are deploying AI today. The research finds a significant increase of AI deployments from 2017 (17 percent).
- AI fuels some job creation, with negligible losses so far. 71 percent of retailers say AI is creating jobs today. Over two-thirds of the jobs are at a senior level coordinator level or above. Meanwhile, 75 percent declared that AI has not replaced any jobs in their organization so far.
- AI’s impact icludes lower customer complaints and higher sales. Retailers are now remarkably aligned on the impact AI is likely to have on customer relations and sales. While expectations have declined from 2017, nevertheless, the report finds that 98 percent of respondents using AI in customer-facing functions expect the number of customer complaints to reduce by up to 15 percent. Furthermore, 99 percent expect AI to increase sales by up to 15 percent.
Expectation versus reality
Multi-billion dollars of future savings are currently available to just a minority of retail companies. According to the report, retailers can save as much as $300 billion in the future by scaling AI deployments across the entire value chain. However, when reviewing all the active AI deployments, just 1 percent were shown to be working on either at multi-site or full-scale implementation.
There simply is a lack of focus on simple, customer-centric deployments. This lack of scalability is likely caused by retailers focusing on more complex, higher-return projects. Retailers deploying AI were 8 times more likely to be working on high-complexity projects than ‘quick win’ projects that are easier to scale.
Deployments to date have also lacked a focus on customer usability. The driving forces behind current AI implementations are cost and ROI. Customer experience and known customer pain points are significantly lower priorities.
There is enormous potential for AI in operations. Only 26 percent of AI use cases today are operations focused, but these were among the most profitable. Standout examples include using AI for procurement tasks (averaging 7.9 percent ROI), applying image detection led algorithms for detecting in-store pilferage (7.9 percent) and optimizing supply chain route plans (7.6 percent). A transformed and super-charged supply chain, for example, offers a significant operational opportunity.
Retail companies are more realistic about their level of AI preparedness
As the realities of AI have revealed themselves, companies in 2018 have adopted more realistic expectations regarding their preparedness for it. Those claiming they have the necessary skills to implement AI have dropped from 78 percent in 2017 to 53 percent today.
More than eight out of ten retailers in 2017 were confident that their data ecosystem for implementing AI was ready. Today, this figure has dropped to 55 percent. Organizations claiming to have a roadmap for AI have dropped from 81 percent in 2017 to just 36 percent.
Kees Jacobs, Capgemini, said, “For global retailers, it appears reality has kicked in regarding AI, both in terms of what the technology can achieve and what they need to do to get there. Of course, deploying and scaling will be the next big objective, but retailers should be wary not to chase ROI figures without also considering the customer experience.”
Jacobs concludes, “Our research shows a clear imbalance of organizations prioritizing cost, data and ROI when deploying AI. Only a small minority consider the customer pain points also. These two factors need equal weighting if long-term AI growth, with all of the benefits it brings, is the goal.”