Enterprise struggles to mature key AI capabilities as it entails a more significant level of development in all organization abilities to ensure success.
Business functions are looking for financial impact with AI, ML, deep learning and data science adoption to achieve cost and competitive advantage.
Transformation requires superior tech-stack artificial intelligence competencies, infrastructure technology, data management capabilities, forecasting tools and applications.
The organization needs a fully comprehensive strategy and infosec governance operating models to address evolving customers’ needs and to ramp up results.
Our Advice
Critical Insight
Enterprises are embracing AI and ML expertise to provide cost and competitive advantage creators across the business functions to enhance personalization, operations, and information security throughout the customer lifecycle. Businesses that embrace AI ML are likely to differentiate and gain market share.
Impact and Result
Identify how the best-in-class retailers are already reaping business benefits from adopting technology demands with artificial intelligence, etc.
Realize the effects of artificial intelligence that are being felt by all entities in the retail value chain.
Analyze the value chain to understand stakeholder pain points better and craft an AI or ML use-case around them to ramp up results.
Recognize cost and competitive opportunities across the enterprise to capitalize on the AI, ML, deep learning, and data science environments to monetize key customer insights.
Leveraging AI to Create Meaningful Insights and Visibility in Retail Research & Tools
1. Leveraging AI to Create Meaningful Insights and Visibility in Retail Storyboard - AI prominence across the enterprise value chain.
This storyboard analyzes artificial intelligence technology adoption cases across the enterprise ecosystem. Additionally, it provides awareness and readiness on how retailers can capitalize on evolving technologies to realize “Leveraging AI to create meaningful insights and visibility.”
Leveraging AI to Create Meaningful Insights and Visibility in Retail
Analyst Perspective
Artificial intelligence prominence across the enterprise value chain
Rahul Jaiswal
Principle Research Director, Retail
Info-Tech Research Group
The retail industry has been at the forefront of digital transformation for several years. It has noticeably increased speed, operation efficiency, and accuracy across every business segment due to a data surge and predictive analytics systems serving brands to make data-driven business decisions.
In retail, artificial intelligence enables businesses with high-level data and information to be leveraged into improved operating models and new business opportunities. As technology evolves, organizations increasingly seek to understand how artificial intelligence will provide value and reshape the industry ecosystem to provide contextual personalization seamlessly across channels and ensure information security measures.
Leading brands are leveraging artificial intelligence (AI) at various stages to create meaningful customer insights and provide infosec effectiveness as global ransomware costs its victims millions of dollars. Moreover, consumers report that digital trust matters; many will shop elsewhere when businesses don't deliver a secure environment.
As AI applications integrate into various business functions, envision business capability components, costs, and competitive advantage creators in harmony with AI services for retail streams.
Executive Summary
Your Challenge
Enterprises struggle to mature key AI capabilities as they entail a more significant level of development in all organization abilities to ensure success.
Business functions are looking for financial impact with AI, ML, deep learning, and data science adoption to achieve cost and competitive advantage.
Transformation requires superior tech-stack artificial intelligence competencies, infrastructure technology, data management capabilities, forecasting tools, and applications.
Need a fully comprehensive strategy and infosec governance operating models to address evolving customers’ needs and ramp up results.
Common Obstacles
Leaders have an ambiguous understanding of AI, ML and their benefits, let alone how it they impacts an organization.
Businesses are reluctant to execute artificial intelligence, which is the key to the success of the cost and competitive organization strategy.
Line of businesses (LOBs) struggle to begin and ramp-up. Business teams do not take a structured approach to execute technology in each phase and deploy technology before developing a robust strategy.
Prioritizing the demand for AI programs and governing the projects to prevent the overloading of resources.
Info-Tech's Approach
Discover how the best-in-class retailers are already reaping business benefits from adopting technology demands with artificial intelligence.
Realize the effects of artificial intelligence that are being felt on all entities of the retail value chain.
Analyze the value chain to understand stakeholder pain points better and craft an AI or ML use case around them to ramp up results.
Recognize cost and competitive opportunities across the enterprise to capitalize on the AI, ML, deep learning, and data science environments to monetize key customer insights.
Info-Tech Insight
Enterprises are embracing AI and ML expertise to provide cost and competitive advantage across business functions to enhance personalization, operations, and information security throughout the customer lifecycle. Businesses that embrace AI and ML are likely to differentiate and gain market share.
AI is a modus operandi that mimics human behavior
Artificial intelligence (AI) is a multidisciplinary research field aiming to make machines intelligent. Applied to specific tasks like computer vision, speech recognition, and language translation, AI has advanced exponentially in the last few years.
Info-Tech Insight
Scaling artificial intelligence can generate an enormous cost and competitive expansion.
As technology evolves, retail organizations increasingly seek to understand how artificial intelligence will reshape the industry
Current state
The worldwide artificial intelligence in retail was estimated at $4.84 billion USD in 2021.
Machine learning accounted for around 39.7% ($1.92 billion) of the global artificial intelligence in retail market in 2020.
Automation: Retailers are rapidly executing AI expertise to enhance intelligent automation in retail stores. The
technology empowers retail brands to drive online automation to offline services and contextual personalization,
adopting AI-based automation to increase YOY. Thus, retailers will likely invest more to understand customer preferences
and cost optimization to drive value (e.g. Amazon Go).
Future state
AI services in the retail sector are predicted to rise from $5 billion to above $31 billion by 2028 (WEF at DAVOS, 2023)
AI-as-a-service (AIaaS ) and ML-as-a-service (MLaaS) models will become prevalent because of their convenience,
cost-effectiveness, relative ease, and security of carrying out implementations.
Sustainability: Consumer retail is expected to have the most significant sustainability potential for AI-driven
improvement use cases at 45 percent emissions intensity (EEI) reduction by 2030.
Automation: Retailers are rapidly executing AI expertise to enhance intelligent automation in retail stores.
The
technology empowers retail brands to drive online automation to offline services and contextual personalization,
adopting AI-based automation to increase YOY. Thus, retailers will likely invest more to understand customer
preferences
and cost optimization to drive value (e.g. Amazon Go).
Sources: World Economic Forum, 6 Jan. 2023. Cision PR Newswire. 23 Jan 2020. Fortune Business Insights, 16 Nov. 2021.
AI and ML drivers will bring growth through personalization and infosec measures
Artificial intelligence (AI) and machine learning (ML) drivers
Artificial intelligence (AI) disruption
Generative AI and increased data access. With the upsurge of data to 200 Zettabytes (ZB) by 2025, consumer
online-to-offline hyper-personalization will become the critical cost and competitive advantage value creator, and
relevant content will become increasingly vital.
Natural language processing. NLP is becoming increasingly vital for understanding customer intent, communication, and
data analysis (e.g. customer service chatbots).
Augmented intelligence. Groups the essential qualities of both humans and technology, offering businesses the ability to
raise the productivity and efficiency of their staff.
AIoT and hardware. AI and IoT play are the leading tools businesses invest in to increase efficiency and deliver a
competitive advantage. Startups and large enterprises prefer AI technology to unleash IoT’s full potential. 75 Billion
IoT hardware devices to be in use by 2025 (Seeking Alpha, 2020).
Transparency. Insurers are increasingly declining coverage unless enterprises demonstrate that they are running
practical security training and have implemented vital security protections such as multi-factor authentication (MFA).
There is a stronger push for the deployment of AI in a visible and specified manner.
Composite AI. A new method that combines different AI technologies with more profound insights from content and data
(e.g. knowledge graphs).
Algorithmic customer-centric platform. Deployment eliminates an organization’s silos to create an enterprise-wide
opportunity to boost conversion and increase cross-sell up-sell.
Cognitive analytics. Enables data analysis in a way that humans can understand and is now becoming more readily
available in applications such as Google Analytics.
Low-code intelligent automation platforms. These no longer need expert programmers to design business solutions.
Wearable devices and online assistants. Collect consumer data that can be used by AI applications to offer consumer
preference insights. Statista reports that by 2026, consumers will download 143 billion mobile apps (“Annual global
mobile app downloads”, Statista).
Information security. Procedures will need to be created to better defend sensitive data from likely cyberattacks.
Global ransomware will cost its victims around $265 billion USD annually by 2031, according to cybersecurity ventures
(Seeking Alpha, 2020).
Retail has invested the most in artificial intelligence along with analytics to create value and impact
Awareness and readiness
Artificial intelligence and machine learning capabilities enhance processing speed and human-centric behaviours by
interpreting algorithms and data to identify patterns.
Hyper Personalization – Shoppers expect sophisticated levels of hyper-personalization. Retail brands leverage AI, ML,
and recommendation engines to predict consumer demand patterns by building on shopper online-to-offline data and
devising intelligent, insight-enabled marketing services.
Information Security – Enterprises are leveraging AI models covering many areas, from network and security architecture
to testing and auditing. Information security procedures usually focus on confidentiality, integrity, and availability
to safeguard sensitive data from potential cyberattacks.
Source: McKinsey & Company, April 17, 2018
Leading retail brands are leveraging artificial intelligence (AI) at various stages to create meaningful customer
insights and operational effectiveness
Awareness
Consideration
Transaction
Delivery
Engagement
At Walgreen, AI is empowering customers and the store as it uses data from the number of anti-viral prescriptions it fills at more than 8,000 locations to track the spread of the flu.
The online, interactive map helps customers know how bad the flu is in their area and helps Walgreens stock inventory accordingly (TechRepublic).
LowesBot supports in-store customers in finding products and sharing specialty knowledge.
In-store robots that see their surroundings and an AI ‘brain’ that senses low inventory, understand traffic patterns and proactively replenish inventory (Lowes Innovation Labs).
To enable faster checkout time, Amazon Go uses a combination of computer vision, deep learning, and sensor fusion
technology to automate the payment at checkout made through the Amazon Go app (Retail Dive).
Zara uses robots for buying online and pickup of in-store purchases to ensure it offers customers a quick, efficient
pickup process.
It has automated the pickup process by prompting customers at pickup stations to enter a code that activates an in-store robot to begin searching for the requested order. The robot then delivers it to a drop box (Forbes).
Kroger is powering personalization and brand partnerships with data science.
It has launched numerous loyalty programs focusing on data to increase targeted personalization and get products to consumers faster, with its digital coupon downloads 32% higher than last year (RIS).
Online fast-fashion brand Shein uses AI as its central engine to determine trends and predict consumer demand patterns.
It leverages the combined scale of influencers and key opinion leaders on YouTube, Instagram, and TikTok to run
marketing campaigns and drive sales.
Walmart automated negotiations of its 100,000+ suppliers with artificial intelligence-powered software comprising a
text-based interface (chatbot).
Pactum AI’s machine learning algorithm automatically negotiates better agreements with human suppliers on behalf of
Walmart (“How Walmart Automated”).
Nordstrom partnered with Forter, which provides an AI-powered model to help improve real-time, identity-based decisions
on whether an incident is a fraudulent transaction and would result in a chargeback (Forter).
U.K. Retailer Ocado is working with British startup Wayve for autonomous last-mile operations in London. It has put an
investment of over $13 million into A.I. delivery.
This partnership will focus on Ocado’s U.K. operations, and international customers such as the U.S. grocery chain
Kroger and Coles Group in Australia (Reuters).
Starbucks simplifies customers’ morning pick-me-up with its AI-enabled voice ordering.
Customers can chat with the My Starbucks Barista app to place their orders by voice or text. When the customer gets to
their local Starbucks, the order will be waiting and they can skip the line (Geek Wire).
Marketing and sales automation stand to gain the most revenue from embracing AI technologies
Awareness and readiness
AI pursues customers in real-time from the moment they are inside a store or online. They:
Attain platform listings and information on products and services.
Receive hyper-personalized offers, dynamic prices, information etc.
Can optimize search for information on specific outcomes of interest with AI voice recognition technology.
Are aided by contextual AI ML tools in-store or virtual personal assistants (VPAs) online.
Filter product information, translations, facts, in-store stock levels, availability, etc.
Get product recommendations, compare prices online, and check customer reviews.
Self-scan the products in their shopping basket and seamlessly check out.
Pay by mobile or through near-field communication (NFC) card.
Receive automated email or SMS receipts, vouchers, post-purchase re-engagement contests, loyalty surveys, etc.
Revenue increases from adopting (AI) in organizations worldwide.
Source(s): Statista Survey, Worldwide; May 18 to June 29, 2021.
Service operations and risk management optimize value the most from embracing AI technologies for cost reduction
Awareness and readiness
According to a global AI survey, service operations benefit the most from adopting artificial intelligence technologies
in terms of cost reduction.
Fifty-one percent of respondents said that service operations functions in their organizations witnessed cost decreases
greater than twenty percent.
Enterprises have leveraged the power of AI for many service operations, such as
Customer care
Voice-enabled ordering
Inventory management
In-store robotics
Check-out-free stores
Last-mile operations
Loyalty management
Information security
AloT HVAC monitoring
Network operations
Network planning and deployment
Cost Decrease from AI adoption in global companies worldwide by function
Source(s): Statista Survey, Worldwide; May 18 to June 29, 2021
AI applications will integrate into numerous lines of business (LOBs)
Source: Info-Tech Research Group Analysis
AI is the fastest-growing category for new spending
AI as a service (AIaaS), and ML as a service (MLaaS) models will lead the next wave of services growth
Overarching Insights
AI services in the retail sector are predicted to rise from $5 billion to above $31 billion by 2028 (World Economic
Forum).
As technology evolves, retail organizations will increasingly seek to understand how artificial intelligence reshapes
the industry.
Automation will play a substantial role in mechanizing many tasks. Overall, this development will increase efficiency
and help to improve the customer experience and supply chain resiliency.
Sustainability – AI forecasting tools will help enterprises achieve carbon neutrality by observing emission rates and
promoting recycling. In addition, they can help execute consumer subscription models, reduce environmental strain, and
minimize waste materials sent to landfills.
Managed Services
With the expanding AIaaS market, managed services need to become the focus of many companies as they opt for AI services specific to a particular function, process, and application. For example, third parties could recommend AI-based contract interpretation services for legal ventures. Top technology companies provide end-to-end managed services to increase automation and provide better customer and enterprise value.
Extension in Microservices
As AI enters, most enterprises (small or big) anticipate getting AI microservices. Microservices enable AI delivery as a package of separately deployable services customized to specific business needs. For example, solution design allows flexibility around designing solutions as individual AI microservice can have substantial complexity with the requirement for monitoring, retraining ML models, and maintenance. It may speed up AI capabilities and consequently take care of AI’s ethical use.
Improve In-House Capabilities
AIaaS demands orderly coordination between the AI service provider and the subscriber enterprise to avoid the compromise of sensitive data. These synchronized systems undergo frequent maintenance and updates to keep vulnerabilities (internal and external risks) in safety check. Therefore, enterprises must train people with sensitive systems to keep them cyber-safe. It will become essential for all working workforce to know, understand, and engage in security practices to collaborate with AIaaS seamlessly.
Outsource AI ML Components
A third-party service provider has a vital role to play in AIaaS. Businesses can use this by outsourcing their AI components (ML, complicated and out-of-the-box algorithms, end-to-end AI services, creating virtual assistants, and conversational AI) to service providers. Enterprises need not worry about the required setup, maintenance, or improvements. With such an AIaaS provision, businesses can invest their time in critical projects that need care.
Businesses (big or small) will need to leverage the AIaaS community-based ecosystem comprising various stakeholders
Awareness and readiness
AIaaS are cloud-based systems providing on-demand services to businesses and enterprises to install, develop, train, and manage AI models. AIaaS stack may be distributed into a layer, organized as follows:
AI software services that are ready-to-use AI applications and building blocks (SaaS Cloud Layer).
AI function services to quickly develop/ test critical functions without the pains associated with in-house infrastructure management (FaaS Cloud Layer).
AI data services opportunities for enterprises to monetize their data through various third-party DaaS offerings.
AI developer services tools for assisting developers in implementing code to bring out AI capabilities (PaaS Cloud layer)
AI storage and infrastructure services comprising raw computational power for building and training AI algorithms, network and storage capacities to store and share data (STaaS & IaaS layer).
Most IT departments should aim to drive better efficiency and cost-saving outcomes
The maturity level skews lower. Slightly more than half of CIOs using Info-Tech's CEO-CIO Alignment Program rated themselves at a Support level of maturity in 2022. That aligns with IT professionals' view of their organizations from our Tech Trends and Priorities Survey, where organizations are rated at the Support level on average. At this level, IT departments can provide reliable infrastructure and support a responsive IT service desk that reasonably satisfies stakeholders. It is concerning that nearly one quarter consider their maturity as Struggling.
CIOs want to be transformative. In the future, CIOs aspire to attain the Transform level of maturity. Nearly half of the CIOs select this future state in our diagnostic, indicating a desire to deliver reliable innovation and lead the organization to become a technology-driven firm. However, fewer CxOs aspire to that level of maturity in IT. CxOs are more likely than CIOs to say that IT should aim for the Optimize level of maturity. At this level, IT will help other departments become more efficient which will lower costs across the organization.
Most CIOs should move toward short-term optimization. IT maturity is achieved one step at a time. Aiming for outcomes at the Optimize level will be a realistic goal for most CIOs in 2023 and will satisfy many stakeholders.
Current and future state of IT maturity
Source: Info-Tech CIO Priorities 2023
RETAIL INDUSTRY REFERENCE ARCHITECTURE
Envision business capability components, costs, and competitive advantage creators in unison to introduce AI into retail value streams
CASE STUDY
Walmart deploys AI-automated negotiations to engage with 100,000-plus suppliers
Walmart Inc. is an American multinational retailer corporation with a chain of hypermarkets, discount department stores, and grocery stores in North America, headquartered in Bentonville Arkansas.
Walmart wanted to automate the age-old procurement problem, as corporate buyers lacked time to negotiate fully with all vendors. Usually, this left untapped opportunities for suppliers eager to be part of the Walmart merchandise assortments as it was often not optimal to engage with 100,000+ suppliers.
Solution
Walmart solved this problem via artificial intelligence software that involved a text-based interface (i.e. chatbots) that negotiates with Walmart suppliers directly on behalf of Walmart. The concept started in Walmart Canada in 2021 and leveraged supplier feedback into the system.
Walmart started piloting this innovative supplier management application with vendors who supplied “merchandise goods not for resale.” It then focused on pre-approved suppliers so the need to authenticate new suppliers would not delay the pilot program.
The initial pilot program involved suppliers that sold “merchandise goods not for resale,” such as fleet services, carts, and other materials used in retail stores. The management team saw substantial opportunities to rally payment terms and secure extra discounts.
Results
Walmart reached agreements with 64% of participating suppliers, which exceeded the 20% goal. In addition, the average turnaround time for a deal was 11 days, and the company accomplished 1.5% savings and increased payment terms to 35 days on average. Currently, the chatbot runs 2,000 concurrent negotiations and learns from every transaction.
AI-Profitable production pilots have aided Walmart in selling the supplier management solution to other parts of its business. After the pilot in Canada, deployments in the United States, Chile, South Africa, Mexico, Central America, and China are on the horizon.
Leading organizations require business reference architecture techniques such as strategy maps, value streams, and capability maps to design accurate blueprints of retail operations.
Assess your initiatives and priorities to determine if you are investing in the right capabilities. Conduct capability
assessments to identify opportunities and prioritize projects.
Design a strategy that applies innovation to your business model, streamlines and transforms processes, and uses technology to enhance interactions with customers and employees.
Use digital for transforming non-routine cognitive activities and derisking vital value chain elements. Create a
balanced roadmap that improves digital maturity and prepares you for long-term success in a digital economy.
This blueprint assists organizations with assessing, planning, building and rolling out their AI ML initiatives.
Using the architecture building blocks will speed up the architecture decisions phase. The success rate of AI
initiatives is tightly coupled with data management capabilities and sound architecture.
Envision a plan to deploy AI capabilities to improve your IT operations.
Achieve a current state assessment to identify which areas within your operations management are least mature and cause
the most grief. Then identify which functional areas within operations management need to be prioritized for
improvement.
Bibliography
Anderson. George. “Zara Is 'Throwing Robots' At Its Click-And-Collect Problem.” Forbes, 14 Mar. 2018. Web.
“Artificial-intelligence-ai-in-retail-market-101968.” Fortune Business Insights, Nov 16. 2021. Web.
“Autonomous delivery services increasing.” Super Market News NZ, 16 Aug. 2022. Web.
Bishop, Todd. “Coffee from a chatbot: Starbucks unveils ‘My Starbucks Barista’ AI technology for mobile orders.” Geek Wire, 7 Dec. 2016. Web.
Braue, David. “Global Ransomware Damage Costs Predicted To Exceed $265 Billion By 2031.” Cyber Crime Magazine, 2 June. 2020. Web.
Carey, Nick. “UK's Ocado invests in Wayve for autonomous grocery deliveries.” Reuters, 6 Oct 2021. Web
Ceci, L. “Annual global mobile app downloads 2021-2026, by store.” Statista, 5 July 2022.
Chui, Michael, James Manyika, et al. “Notes from the AI frontier: Applications and value of deep learning.” McKinsey & Company, McKinsey Global Institute. 17 Apr. 2018. Web.
Davos 2023. “Here's how artificial intelligence can benefit the retail sector.” World Economic Forum. Jan 6, 2023. Press Release.
Dilmegani, Cem. “AI Use Cases & Applications: In-Depth Guide for 2023.” AI Multiple, 10 Jan. 2023. Web.
Dominguez, Liz. “Kroger Powers Personalization and Brand Partnerships With Data Science.” RIS, 12 June. 2022. Web.
Mejia, Niccolo. “Artificial Intelligence in Payment Processing – Current Applications.” Emero, 8 Sep. 2019. Web.
Morgan, Steve. “The World Will Store 200 Zettabytes Of Data By 2025.” Cyber Crime Magazine, 8 June. 2020. Web.
Pav, Nicole. “2023 emerging AI and Machine Learning trends.” Data Science Dojo, 22 Nov 2022. Web.
Statista Survey. “Marketing and sales automation stand to gain the most revenue from embracing AI technologies.” Statista Worldwide. May 18 to June 29, 2021.
Thomas, Lauren. “Walmart is quietly preparing to enter the metaverse.” CNBC, 16 Jan. 2022. Web.
Thormundsson, Bergur. “Cost Decrease from AI adoption in global companies worldwide by function.” Statista Worldwide. 19 April 2022. Web
Van Hoek, Remko, Michael DeWitt, Mary Lacity, Travis Johnson. “How Walmart Automated Supplier Negotiations.” HBR, 08 Nov 2022. Web.
Vertiv Holding Co. “A Solid Play On The Data Explosion Trend.” Seeking Alpha, 26 Jun. 2020. Web.
Vinugayathri. “AI and IoT Blended - What It Is and Why It Matters?” Clarion Technologies, Jan. 2023. Web.
AI prominence across the enterprise value chain
Email Infographic
About Info-Tech
Info-Tech Research Group is the world’s fastest-growing information technology research and advisory company, proudly serving over 30,000 IT professionals.
We produce unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. We partner closely with IT teams to provide everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.
What Is a Blueprint?
A blueprint is designed to be a roadmap, containing a methodology and the tools and templates you need to solve your IT problems.
Each blueprint can be accompanied by a Guided Implementation that provides you access to our world-class analysts to help you get through the project.
Our analyst calls are focused on helping our members use the research we produce, and our experts will guide you to successful project completion.
Book an Analyst Call on This Topic
You can start as early as tomorrow morning. Our analysts will explain the process during your first call.
Get Advice From a Subject Matter Expert
Each call will focus on explaining the material and helping you to plan your project, interpret and analyze the results of each project step, and set the direction for your next project step.
