Emerging technologies that will advance the retail industry

Emerging artificial intelligence technologies advance retail industry
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As COVID-19 continues to affect economies and industries, one industry that has experienced significant disruption is the retail sector. Shopping behaviors have certainly shifted drastically in two directions - towards massive online stores and a reinforced meaning behind purchasing items locally.

In response to this changing landscape, retailers have brought forward technology investments to accelerate their digital business transformation, particularly in artificial intelligence (AI) technologies with retailers often utilizing AI-enabled third-party vendor applications to solve immediate challenges before moving to more strategic deployments once they gain evidence of the benefits. These steps offer a significant opportunity for retail solution vendors with AI-based application suites.

We explore five emerging AI technologies - edge AI, smart robots, machine learning (ML), cloud AI developer services, and AI business and technology services – their adoption and potential impact in retail today, and the opportunity this presents to retail solution providers.

Edge AI in retail

Edge AI enables real-time operations for data acquisition and decision making where response time matters. In the retail industry, this is particularly important when looking to improve “store intelligence” through the monitoring, analysis and tracking of store activity through various endpoint technologies deployed in the store. Use cases include dynamic pricing management, mixed-reality experiences, real-time inventory management, and fraud prevention.

Retail solution providers looking to implement edge AI should:

  • Assess the target retailers’ digital maturity for localized edge deployments. Scrutinize use cases that will require high bandwidth and low latency, and evaluate current staffing deployed in the retail physical footprint that can be optimized.
  • Build out a robust product introduction plan by co-creating launch efforts with innovative early adopter retailers and network operators to cross-promote solutions and innovation labs.
  • Source talent that brings in-depth, retail-specific knowledge to your product development roadmap.

Smart robots in retail

Smart robots are electromechanical form factors that work autonomously. They learn in short-term intervals from human-supervised training and demonstrations, or by their experiences on the job.

COVID-19 has led to an acceleration of growth in this area where leading retailers are expected to increase spend over the next 3-4 years. This growth is fast-tracking the adoption of smart robots to take over lower-level repetitive tasks for greater reliability and productivity at lower costs. This will help to free up human workers for redeployment in more valuable activities.

Smart robot use cases include picking and packing inventory, handling of hazardous waste, routine cleaning, stock auditing and replenishment. Some pioneers have installed smart robots in customer-facing roles like store navigation and help desks.

Retail solution providers looking to develop smart robot solutions should:

  • Develop a robust product roadmap incorporating both short-term use cases triggered by COVID-19 and a longer-term view of additional use cases that can add value to retailers’ entire physical footprint and various formats.
  • Enable retailers to assess the ROI of deployment and lower the barriers of adoption by creating a scorecard to measure the value of each benefit a robotics solution could bring.

Machine learning

Machine learning (ML) is an AI discipline that applies mathematical models to data to solve business problems by extracting knowledge, finding patterns, and recommending actions. ML is segmented into three subdisciplines based on how it accumulates and processes data - supervised learning, unsupervised learning, and reinforcement learning. Generally, supervised learning offers answers to questions, unsupervised learning explores data and reinforcement learning provides a balance of both.

As COVID-19 increases the use of e-commerce adoption, the retail merchandising function has been ground zero for AI and ML technologies to enable intelligent automation and improve data-driven decision making. Retailers can use ML to measure and improve forecast accuracy by measuring forecast deviation using actual demand down to stock keeping unit or location level.

Retail solution providers looking to deploy ML-based applications should:

  • Engage with the retail buyer for smaller proofs of concept (POCs) in a single line of business to clearly demonstrate value created before trying to implement multiple projects.
  • Identify key resources already at work within the retailer on ML, advanced analytics and data science.
  • Be clear on what training models were used for out-of-the-box solutions and how the training was done.

Cloud AI developer services in retail

Cloud AI developer services allow for IT teams to integrate the advantages of AI and machine learning with their existing cloud computing and cloud storage technology. Services include natural language processing (NLP), sentiment analysis, image recognition and autoML model creation.

Companies that began their digital transformations early in the pandemic are paving the way forward for retail late-comers who are looking to migrate to the cloud while minimizing infrastructure downtime. Even though a share of workloads will stay in private cloud or on-premises data centers, Gartner expects cloud-based AI solutions to command the largest share of the overall AI-based applications market in retail.

Retail solution providers looking to implement cloud AI developer services into their solution set should:

  • Improve your current products’ capabilities by supporting various cloud AI services such as NLP, image recognition and autoML models as part of your roadmap.
  • Build your value proposition that addresses security risks, regulatory/privacy mandates and retailer technical maturity.
  • Create case studies by retail subsegments that demonstrate how similar customers have moved data into the cloud.

AI business and technology services in retail

AI-related business and technology services offer a continuum of services to help retail companies build and run AI-centric projects and solutions for targeted business outcomes. They include services such as AI strategy development, business readiness assessment, and independent verification and validation of cloud AI services.

Retail solution providers planning to leverage AI business and technology services should:

  • Offer services to help retailers build assessment frameworks to identify their readiness for AI projects in terms of their current infrastructure, technology, and human factors.
  • Create industry solutions by developing AI services based on retail subsegments (e.g., grocery, specialty, mass merchandise) or similarities between subsegments (e.g., apparel/footwear and luxury).
  • Ensure that AI initiatives focus on business outcomes and client needs through a use-case-based approach that considers the client’s digital maturity level and AI skills.
  • Focus on educating the retailer’s technical staff by creating knowledge repositories, training material and test environments.
  • Sandeep Unni, Senior Research Director at Gartner.

Sandeep Unni is a Sr. Director Analyst in Gartner's Retail Industry Research Practice. Mr. Unni advises clients on product solutions and go-to-market strategies through guidance on key market trends, best practices, new technologies and business models, and future scenarios.