Retailers moved from AI hype to reality in 2018 but yet to seize opportunities: Report

New Capgemini study looks at 400 global retailers who are implementing AI use cases

e4m by exchange4media Staff
Updated: Dec 18, 2018 12:18 PM

A new study from Capgemini Research Institute has cleared some of the mystery around AI’s value-to-retail businesses and opened up a path to tangible growth opportunities. 

Capgemini calculates a $300 billion-plus opportunity for retail companies that are able to scale and expand the scope of existing deployments. However, it is not straightforward – the report also found that just 1% of use cases by retailers have achieved this level of deployment today.

The study “Retail superstars: How unleashing AI across functions offers a multi-billion dollar opportunity” looked at 400 global retailers who are implementing AI use cases at different stages of maturity. This group represents 23% 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 include:

  • Over a quarter (28%) of retailers are deploying AI today: The research finds a significant increase of AI deployments from 2017 (17%) and a seven-fold increase from 2016 (4%). 
  • AI fuels some job creation, negligible losses so far: 71% of retailers say AI is creating jobs today with over two-thirds (68%) of the jobs being at a senior level (coordinator level or above) . Meanwhile, 75% declared that AI has not replaced any jobs in their organization so far. Those who did say jobs have been cut put the number at 25 or lower.
  • AI’s impact: Lower customer complaints, 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% of respondents using AI in customer-facing functions expect the number of customer complaints to reduce by up to 15%, while 99% expect AI to increase sales by up to 15%. This marks a significant change from 2017, where respondents gave widely contrasting expectations from zero, to more than 15%, to “don’t know”. In both business cases, zero respondents reported that they could not quantify AI’s benefit.

In order to calculate the clear opportunities for future growth, such as the benefits expected and the feasibility of implementation, the Capgemini Research Institute analyzed 43 working use cases for AI, finding:

  • 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-plus in the future by scaling AI deployments across the entire value chain. However, when reviewing all active AI deployments, just 1% were shown to be working on either at multi-site or full-scale implementation. 
  • 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 (62%) and ROI (59%), while customer experience (10%) and known customer pain points (7%) are significantly lower priorities.  
  • Enormous potential for AI in operations: Only 26% of AI use cases today are focused on operations, but these were among the most profitable in terms of cost returns. Standout examples included using AI for procurement tasks (averaging 7.9% ROI), applying image detection-led algorithms for detecting in-store pilferage (7.9%) and optimizing supply chain route plans (7.6%). A transformed and super-charged supply chain, for example, offers a significant operational opportunity as it is one area where AI can bring greater efficiency. 

Retailers 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 that they have the skills needed to implement AI have now dropped from 78% in 2017 to 53% today. More than eight out of ten retailers in 2017 were confident that their data ecosystem for implementing AI was prepared, and today this figure has dropped to 55%. Finally, those organizations claiming to have a roadmap for AI deployment have dropped from 81% in 2017 to just 36% today.

Kees Jacobs, Vice President, Global Consumer Products and Retail Sector at 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. Our research shows a clear imbalance of organizations prioritizing cost, data and ROI when deploying AI, with only a small minority considering the customer pain points also. These two factors need to be given equal weighting if long-term AI growth, with all of the benefits it brings, is to be achieved.”

Research Methodology

The Capgemini Research Institute surveyed 400 executives from retailers across the US, the UK, France, Germany, China, India, Italy, Spain, Sweden and the Netherlands in August 2018. All respondents reported that they were implementing AI use cases at different stages of maturity across a range of retail sub-sectors and countries. Capgemini then conducted an extensive secondary research in October 2018, focused on the top 250 retailers by revenues. The revenue figures have been sourced from the declared revenues of 2017 from Bloomberg and the sample represents a mix of retailers active across multiple retail sub-sectors and geographies.

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