Generative AI for Retail: Enhancing Experiences and Driving Innovation

Did you know that 2023 is Generative AI’s breakout year, according to McKinsey?

AI is the big differentiator today. Generative AI and foundational models are reimagining how we live and reinventing how businesses unlock competitive advantage. The disruptive potential of AI frameworks is all-encompassing, and the retail industry is no exception and is at the cusp of an exciting evolution. The focus is on creating engaging omnichannel experiences that turn discerning customers into diehard evangelists.

Here’s what prominent leaders have to say about the integration of generative AI within the retail sector:

“Our approach to new tools like generative AI is to focus on making shopping easier and more convenient for our customers and members and helping our associates enjoy more satisfying and productive work.” – Walmart CEO Doug McMillon

“For Etsy, more than most, [AI has] the opportunity to unlock really incredible gains, given that there’s 115 million things for sale on Etsy and none of them map to a catalog. So the ability for AI to really help to organize the world for us, I think, is a huge opportunity.” – Etsy CEO Josh Silverman

Read this blog to explore how GenAI is creating new avenues for innovation, customer experience transformation, and sustainable growth for the retail industry ecosystem. Also, discover Hexaware’s proven Decode AI workshop approach that helps businesses create a strategic roadmap for their generative AI journey.

GenAI’s Impact on Data Creation and Analysis: A Paradigm Shift

Generative AI has transformed how we analyze and create new content, including text, images, audio, video, music, voice synthesis, style transformation, multilingual, data synthesis, code analysis, and new code generation.

It has redefined creativity and innovation and has become a force multiplier in helping the retail and manufacturing industry.

• Generate content: Sales, marketing, contracts
• Respond to queries: New insights, self-service, product details, status checks, inventory level
• Create design: New product designs, new patterns, uncover new combinations
• Optimize business processes: Gain insights, patterns analysis, automate repeated tasks
• Provide data insights: Establishing new data correlation and insights

User Experience in the Age of Generative AI

Generative Artificial Intelligence (AI) is enabling user engagement to become more humanlike: free flow, practical, insightful, and creative engagement as compared to software-based restrictive information flow. It is changing the behavior and interactions to how the customer, employees, and all stakeholders want it to be without being limited by software design.

Some of the examples of enhancing the buyers’ journey are:

• Selecting the products based on:
  • Individual preferences
  • Physical and emotional attributes
• Finding product substitutes
• Customizing services
• Comparing the products and prices
• Automating repeated tasks:
  • Marketing campaigns
  • Geofencing
  • Promotions

To know how generative AI can be used in marketing, click here.

How GenAI Empowers Consumer-Centric Innovation

Generative AI has redefined customer and employee journeys and engagements besides optimizing operations and supply chain processes.

Here are three key aspects in which Generative AI will transform and innovate businesses:

• Business Growth: Generative AI can gain insights from data, potentially leading to new product designs and offerings, resulting in new revenue, micro-personalization for cross-sell and up-sell, increased customer engagement, and higher conversions.
• Business Transformation: It can enable new customer experiences, and employee decision-making processes across the channels and value chain. It can understand the data relationship and patterns across suppliers, procurement, manufacturing, and supply chain to find ways to enhance it. For example, customer sentiment analysis and buying patterns, customer product feedback (integrated into the value chain), inventory planning, supplier performance analysis, optimization, and contract creation.
• Increased Productivity and Cost Optimization: It can automate self-service for customers, employees, and suppliers, sales and marketing, finance and audits, and supply chain efficiencies. The increased transparency and collaboration will improve the ecosystem across downstream and upstream processes. Also, it can automate repeated tasks and processes, applications, infrastructure, networks, and security to improve productivity and decrease cost.

Besides changing businesses, generative AI is also changing the way in which IT and Infrastructure teams function:

• It can be leveraged to:
  • Increase productivity: Project design, coding, debugging, and documentation
  • Provide support: Apps, databases, infrastructure, network, and security
• It can accelerate autonomous testing to improve code coverage in test automation:
  • Automated test data synthesis
  • Test case generation
  • Anomaly detection
  • Bug detection

What Are the Limitations of Generative AI?

Here are some of the critical limitations of Generative AI for which it is essential to take proper measures during design and development.

Setting up an AI Governance Model is critical to ensure the model gives the desired outcome and meets ethical and regulatory requirements.

• Data Privacy: It is vital to avoid breaches of sensitive, proprietary, and unauthorized access to data.
• Trust: It includes critical factors like data sufficiency, accuracy, consistency, fairness, and verified data sources to avoid biases, misleading information, and legal and copyright issues.
• Alignment: It is important to align models to the business domain. Fine-tuning Generative AI models for business scenarios and constant monitoring are essential for the right outcomes.
• Safety: Cybersecurity, data moderation, and vulnerability mitigation are critical to avoid data misuse. For example, breaches of critical data, prompt injections, and malicious code generation.
• Environmental, social, and governance (ESG): Being one of the essential regulations, it is mandatory to manage environmental, social, and governance factors such as carbon emissions, fairness, transparency, traceability, explainability, regulations, and compliance.
• Cost: Limited expertise and computational resources can make the adoption of generative AI cost-intrinsic if not governed well.

How Does Hexaware Address These Generative AI Limitations?

With tools, services, and actionable frameworks to mitigate data- and security-related risks, for robust governance of nascent technology design and implementations, and to align the outcome with business goals and objectives, Hexaware also takes the following factors into consideration for the generative AI implementation:

• Sustainability: ChatGPT was trained on large language models (LLMs) with billions of parameters, leaving a high carbon footprint. For Enterprise Generative AI models, Hexaware carefully minimizes the size of models while maximizing accuracy by training on large CRM data, which requires less computation and hence reduces carbon footprint. In fact, Generative AI is expected overall to reduce carbon footprint with advancements in computing technology and reduced need for repeated manual analysis that otherwise consumes much higher resources.
• Data Governance: AI is only as good as the data it is trained on. Hexaware helps companies review accuracy and recency of datasets and documents that will be used to train models to ensure data accuracy and safety.
• Models Moderation: Like any other AI model, generative AI needs to be monitored on a regular basis. Hexaware’s proprietary automation tool for Generative AI is specialized to moderate and govern generative AI framework to constantly review data collection, checks-and-balances, and standard mitigations for specific risks.

Now, let’s delve into Hexaware’s strategy for implementing generative AI within the retail sector.

Revolutionizing Retail with Generative AI: Hexaware’s Use Cases and Approach

Hexaware has a step-by-step, structured approach to using Generative AI for retail to launch new ideas into fully operational products and businesses using its solutions ecosystem, covering our clients’ specialized service offerings and benefits.

Here are some of the popular high-impact generative AI use cases among our retail industry clients.

Reimagine Customer Experience to Enhance Engagement and Conversions

• Customer Service: Customer inquiries and support for products, services, visual searches, orders, returns, membership accounts, reward points, etc.
• Product Details: Engaging product descriptions, substitutes, comparisons, and recommendations based on customer preference
• Personalized Recommendations: Based on customer preference, browsing, purchase history, and other factors
• Payments: Renewals, reminders, processes, queries, and facilitation
• Returns, Exchanges, and Refunds: Process support and status updates
• Technical Support: Frequently Asked Questions (FAQs), Do It Yourself (DIY), and troubleshooting support in multiple languages
• Feedback and Reviews: User feedback and sentiment analysis
• Customer Segmentation: Customer clustering based on common attributes

Enable Employees with AI Assistant for Creative Decision Making

• Training and Onboarding: Interactive simulations for company policies, procedures, and best practices
• IT Support: Troubleshooting common issues with software or hardware, resetting passwords, checking Wi-Fi connectivity, and automating ticket creation based on event analysis
• Call Logs and User Reviews Analysis: Analyze text data – such as customer support chat logs or social media posts – to detect signs of potential fraud or scams, and gain insights into products and services
• HR Support: HR-related queries such as benefits, payroll, and company policies
• Project Management: Status updates, meeting schedules, and task assignments
• Internal Communications: Centralized platform to access company and project updates, resources, collaboration, and knowledge base
• Knowledge Management: Standard operating procedures (SOPs), rules, instructions, guidelines, and processes

Reinventing Enterprise Operations and Supply Chain

• Sales and Marketing: Market research, targeted campaigns, content creation, sales inquiries, and product information
• Smart Inventory Management: Inventory levels, demand forecasting, dynamic pricing, stock taking, shelf planning, and various store apps
• Warehouse and Shipping Logistics: Processes, paperwork, delivery times, and shipping status
• Scenario Planning: Simulate the effects of changes in price, marketing campaigns, or external factors based on demand and pricing
• Supply Chain Optimization: Demand levels, potential bottlenecks, vendor performance, and optimal shipping routes
• Contracts: Supplier, shipping, and other vital documents
• Image and Video Analysis: Objects and text recognition, pattern analysis and alerts
• Fraud Detection: Detect fraudulent patterns and transactions
• Virtual Assistants: 24×7 multilingual customer support

Automate IT Operations including Infrastructure and Security

• Network Monitoring: Monitor network traffic, detect issues, and provide alerts.
• System Administration: Manage system configurations, monitor system health, and automate routine tasks such as backups and updates.
• Apps Monitoring: Identify patterns, detect anomalies, and predict potential issues in an application’s behavior.
• Security: Identify security threats, such as malware or unauthorized access attempts.
• Data Privacy: Avoid breach of sensitive, proprietary, and unauthorized access of data.
• Helpdesk Support: Troubleshooting issues
• Performance: Performance monitoring, patterns analysis, and alerts.
• Analytics: Analyze system logs and performance metrics, identify areas for improvement, and generate reports.
• Automation: Automate repeated tasks and incidents resolution.
• Coordination: With internal and third-party teams to analysis and resolve issues.

Can Generative AI Be Fully Automated?

Yes. Hexaware is a firm believer in AI-and-Automation-First services to bring maximum benefits to clients to transform and optimize cost.

There are business cases where it is best to automate everything (such as operational tasks) altogether, but then there are business-sensitive scenarios, such as audits and contracts, where it is best to use AI as an assistant in a supporting role and keep the humans in the decision-making process.

In either case, the model outcome should be transparent and accessible to stakeholders.

Is Generative AI Ready to Use?

Generative AI is real! It’s a groundbreaking technology with a ready-to-use framework. It is here to solve business problems and will change the way businesses operate. Using generative AI in the manufacturing and retail industries would make businesses reimagine the journey of customer and employee engagement, and the entire operations and supply chain.

Generative AI is not an entirely new technology!

Did you know that it has footprints from the early 1960s?

Since then, various factors such as progress in neural network techniques, access to large sets of multimodal data, advanced LLMs and transformers, high-performance cloud computing, specialized Graphical Processing Unit (GPU)/Tensor Processing Unit (TPU), 5G, and other advancements in technology to access and process data have significantly expanded the Generative AI capabilities.

Can Generative AI Work with Other Technologies?

Generative AI can be leveraged standalone or in conjunction with business analytics tools, conversational AI, Augmented reality (AR)/Virtual Reality (VR), Custom Vision, etc., to create meaningful, practical, and high-impact business use cases. Just to illustrate, here are some practical scenarios:

• Conversational AI (chatbot/voicebot), in combination with Generative AI, can help create self-service applications that can be integrated across channels to create a uniform experience for customers, employees, and suppliers.
• AR/VR leveraging Generative AI can be highly effective for product demos, workshops, training, virtual tours, brochures, gamification, etc.
• Custom Vision combined with Generative AI can create great applications for monitoring warehouses, store floors, secured areas, packaging items, etc., where it can detect, analyze, and summarize objects and text to resolve complex business challenges.

How to Build a Generative AI Model?

It is important to qualify the right business use cases, which includes identifying the Generative AI use cases and ranking them by feasibility, relevance, and return on investment (ROI). An organization roadmap should define clarity on Embedded AI versus separate AI platforms. Selection of technology, foundation model, and integration of business knowledge to build fine-tuned models are essential steps to build any Generative AI applications.

Prompt Engineering is another critical area involving systematic approach to design the inputs for Generative AI to get optimal output. Hexaware has a large workforce trained in Generative AI model fine-tuning and prompt engineering integrated with deep domain knowledge in retail, consumer, and manufacturing industries, which enables us to provide maximum benefits to our clients.

Here is the high-level depiction of the design approach and business integration for generative AI models.

Navigating the Future: The Next Phase of Generative AI for Retail

Generative AI (GenAI) is a proven and practical technology, which is already in use and has high business potential.

According to a recent survey, generative AI could add up to $4.4 trillion annually to global economy.

Here are a few ways to get started on this journey:

• Attend DecodeAI, an innovative workshop designed by Hexaware to deconstruct GenAI opportunities and use cases and to make strategic decisions on the choice of architecture, tools, and platforms.
• Leverage the expertise of Hexaware retail, consumer, and manufacturing business domains to prioritize high-impact practical use cases.
• Implement robust risk management and governance model to leverage Generative AI safely and confidently for transformation and innovation. Define processes and guardrails to ensure trust and explainability in model outcomes and safeguard it from misuse.
• Evaluate Tensai – a comprehensive platform designed to speed up GenAI adoption through a portfolio of solutions – for knowledge base services, infrastructure and applications support, coding and testing, data security and privacy, testing, etc., to catalyze digital transformation.

That’s it! You are all set to get started with your Generative AI journey to reinvent customer and employee experiences, enhance operations, and drive growth!

Hexaware is focused on creating a suite of platforms and tools that allow our customers to adapt, innovate, and thrive in this AI-first era. Every tool, every platform we create, responds to a unique business need. They’re all geared toward the same goal – empowering our clients to lead in their respective industries with AI’s untapped potential. Hexaware’s blueprint on generative AI acknowledges transformative potential and can help unlock the full potential of generative AI for our clients while managing the risks and governance towards a future of innovation and efficiency.

If you are looking to implement generative AI, we will be happy to help you with customized use cases that fit your business. It is critical to prioritize the use cases based on their feasibility, relevance, and ROI. Hexaware has a proven DecodeAI innovative workshop approach designed to deconstruct AI and Gen AI use cases by closely working with our clients to discover practical high-impact use cases that add immediate value to client business.