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How ambitious and resource-intensive does your retail business need to be to start leveraging generative AI? Why is generative AI better than standard bots for enhancing the customer experience in the retail industry?
According to a 2024 McKinsey assessment, generative AI is expected to unlock between $240 billion and $390 billion in economic value for retailers, representing an industry-wide margin increase of 1.2 to 1.9 percentage points.
In January 2025, at the international CES exhibition, a panel discussion addressed the use of artificial intelligence (AI) in the retail sector. Panelists highlighted that retailers are at various stages in their AI adoption journeys—from those just beginning to explore the technology to those with well-established AI implementations.
"AI is really at the core of everything that we do… from our personalization recommendations and the tools we provide to our stylists to how we plan our inventory—it's all aimed at delivering exceptional client outcomes,"
Noah Zamansky, VP of Client Experiences at Stitch Fix.
Our research team has investigated how retail companies are utilizing generative AI, the results they are achieving, and the costs involved.
Glossary
Generative AI - a type of artificial intelligence that generates new content, such as text, images, or recommendations, based on patterns learned from data.
Chatbot - an AI-driven virtual assistant designed to communicate with users and provide information or assistance through text or voice.
Conversational AI - AI technology that enables machines to understand, process, and respond to human language in a natural and conversational manner.
API (Application Programming Interface)- a set of tools and protocols allowing different software systems to communicate and integrate functionalities seamlessly.
LLM (Large Language Model) - a type of AI model trained on vast amounts of text data to generate human-like text and perform complex language-based tasks.
Why is generative AI better for the retail user experience?
Retailers constantly strive to improve the customer experience online and in stores by leveraging generative AI technologies. Here are some key ways this technology is transforming the shopping journey.
1. Purchase Recommendations
Generative AI agents analyze customer preferences to provide accurate shopping suggestions. For example, implementing a retail chatbot offering personalized recommendations can significantly boost conversion rates. These bots also suggest products aligned with current demand trends, helping solve inventory management challenges.
Example: A chatbot recommending products based on customer style or current trends enhances customer loyalty.
2. Search and Selection
Generative AI enables smart navigation through large product catalogs, helping customers find items that match their purchase history and preferences perfectly. It can also perform intelligent product comparisons by analyzing unstructured data, such as styles, customer reviews, and market trends.
Example: AI can recommend products by comparing reviews and market ratings.
3. Virtual Try-Ons
AI-powered virtual fitting rooms allow customers to visualize how products like clothing, glasses, or makeup will look on them, without needing to visit a store. This improves online shopping experiences and reduces the likelihood of returns.
Example: Pictofit is a virtual try-on plugin that allows customers to try on clothing virtually before purchasing. It is trusted by Shopify, Microsoft, and Hugo Boss, and integrates seamlessly with major e-commerce platforms, like Shopify, WooCommerce, and Wix.
4. Effortless Checkout
AI-driven checkout solutions streamline the purchasing process by minimizing friction. This includes auto-filled payment details, voice-assisted checkouts, and fast, seamless transactions.
Example: AI-powered Checkout by AiFi for convenience stores Żabka in Poland. To maximize efficiency, they installed AiFi's AI-powered frictionless checkout platform in retail locations.
5. 24/7 Support with a Virtual Assistant
Generative AI ensures customers receive round-the-clock support, regardless of time zones. Virtual assistants can handle common inquiries, provide personalized assistance, and resolve technical issues instantly, ensuring uninterrupted service and customer satisfaction.
Example: Zendesk AI, built on billions of real customer service interactions, can understand and resolve the most sophisticated interactions from end to end.
By implementing these generative AI-driven solutions, retailers can offer a more personalized, efficient, and engaging customer experience, increasing loyalty and driving business growth.
Generative AI for internal retail operations
Generative AI is transforming retail internal operations and optimizing processes across all stages of the retail value chain.
1. Inventory Optimization
Retailers can leverage AI technologies, such as computer vision, to optimize inventory levels, manage seasonal fluctuations, and enhance customer experiences by improving inventory tracking and streamlining warehouse management. For e-commerce businesses, AI can automate fulfillment processes, efficiently handle diverse product catalogs, and boost order accuracy by analyzing social media trends and customer feedback.
Example: Blue Yonder uses AI-powered demand forecasting to optimize inventory levels and ensure supply matches demand.
2. Procurement
Chatbots conduct initial negotiations with suppliers, gathering essential data for decision-making. AI models then compare supplier data to determine the optimal partner.
Example: AI negotiation tools like Nibble boost conversion rates through built-in A/B testing while protecting profit margins by offering targeted discounts instead of blanket discounting. These tools enable dynamic pricing to efficiently clear slow-moving stock, reducing warehousing costs without compromising brand perception. Additionally, their interactive and conversational nature fosters stronger customer relationships, enhancing loyalty and engagement while maintaining a natural, on-brand shopping experience.
3. Distribution
AI-powered systems monitor logistics in real-time, managing communication with third-party logistics providers, including handling issues like returns and delays.
Retail businesses benefit from fewer delays and smoother supply chain operations.
Example: Project44 provides real-time tracking and visibility across the supply chain to reduce delays and manage logistics more effectively.
4. In-Store Operations
AI assistants handle customer queries about product prices, in-store locations, and stock levels, improving operational efficiency and customer satisfaction.
5. E-Commerce
AI tools generate product profiles and descriptions within minutes, significantly reducing the time required for content creation while maintaining quality.
Example: Intelligent product descriptions at The Very Group
The leading multi-brand online retailer in the UK, The Very Group (TVG), has implemented a generative AI system for intelligent product analysis and descriptive writing. The system leverages Amazon Bedrock, large language models (LLMs), and multimodal models to analyze products and generate content for copywriters. This has significantly reduced preparation time while improving the quality of product descriptions.
6. Marketing
AI assists in creating marketing strategies, generating and optimizing creatives, and engaging with community members. Retailers achieve higher engagement through timely and accurate marketing campaigns.
Example: Jasper AI assists in creating high-quality marketing copy for campaigns, social media posts, and email newsletters.
7. Back Office Operations
Generative AI supports tasks such as HR processes, financial reporting, and custom software creation, reducing manual workload and improving efficiency.
Example: UiPath automates routine administrative tasks such as payroll, invoice processing, and compliance checks.
8. Commercial Operations
Manual data analysis is replaced by AI tools that handle complex analytical tasks "under the hood," providing faster and more accurate insights.
Example: Tableau combines AI with advanced analytics to provide actionable insights for better decision-making in sales and operations.
By integrating generative AI at every stage of the retail value chain, retailers streamline operations, reduce costs, and improve decision-making, ultimately driving better business outcomes.
How Much Does AI in Retail Cost?
The Price of AI in Retail
In short, the cost of AI in retail can range from $0 to millions of dollars.
And a cost of $0 doesn't mean that implementation won't bring you revenue growth. On the contrary, I want to inspire you to try out ready-made solutions (links provided above). Almost all of them offer a free trial period during which you can test and compare your strengths and the needs of your business.
To start, you'll find the Google AI in Retail course useful.
Gen AI services typically use value-based pricing. Another good piece of news - many services charge clients only when the AI function successfully performs its task (success-based pricing). Whether it's the number of transactions, conversations, or customer interactions processed by AI, this model ensures that companies pay only for the value they receive. For example, Salesforce announced its new Agentforce platform, which charges $2 per conversation instead of per user or seat.
Additionally, the cost of many LLM APIs has significantly dropped over the past year (e.g., comparing the cost of input tokens, GPT-4o released in May 2024 is half the operational cost of GPT-4 Turbo, released a year earlier). AI experts believe that the cost of LLM APIs will continue to decline significantly, with some estimates suggesting it could drop by 80% over the next two to three years.
If you find the idea promising but lack the time to develop it, you can hire agencies specializing in setting up and customizing ready-made solutions. They often create additional integrations and services to fully match your case. Such services can cost from $3,000 to $10,000.
Developing a custom AI solution can range from $20,000 to over $500,000, depending on the complexity of the project and specific requirements.
The cost of developing AI in retail depends on several factors, including the specific use case, the scale of implementation, the type of AI solution, and whether it is developed in-house or through a third-party platform.
However, beyond direct development costs, integrating AI into workflows and customer paths involves not only development but also integration, training, and ongoing maintenance.
Integration
Integrating AI into existing retail systems and workflows may require additional investment. This process can be complex and time-consuming, so you should be prepared for possible disruptions during the transition period.
Support
After implementation, retailers should budget for ongoing support, including system updates, performance monitoring, and addressing any issues.
Training
Implementing AI in retail always involves a steep learning curve for employees, customers, and brands. You must invest in training and building trust among employees and consumers.
How to calculate ROI from AI in retail
While the initial costs can be substantial, AI has the potential to optimize operations, enhance customer experiences, and increase sales, leading to significant long-term benefits. For example, AI-driven supply chain management automation has led to 20-30% cost reductions for major retailers.
That sounds great, but as with all innovations, the main challenge is to accurately calculate AI's benefits, which include increased efficiency, cost savings, and improved decision-making capabilities.
Each business needs to select and prioritize its own set of metrics. I'll share some examples we used in our client's cases.
1. Operational efficiency: inventory turnover rate, order accuracy rate, time savings, automation uptime
2. Customer experience: customer satisfaction score, net promoter score, average resolution time, personalization effectiveness
3. Financial performance: sales growth, cost savings, ROI of AI
4. Marketing and engagement: customer retention rate, conversion rate, engagement rate
5. Employee productivity: employee satisfaction, task completion time
6. Error reduction: error rate in forecasting, fraud detection rate
The previously mentioned Agentforce from Salesforce offers a calculator to estimate the benefits of implementing AI.
How to successfully implement AI in retail
By following these steps, distributors can take a structured and strategic approach to AI in their operations. With the right strategy, AI can transform all aspects of the supply chain, enhance the organization's overall resilience, and create a competitive advantage.
1. Identify Specific Use Cases
- Define the problem areas or opportunities where AI can add the most immediate value (e.g., inventory management, personalized marketing, or customer service).
- Evaluate which areas align with your business goals and where AI could drive measurable impact.
- Assess your current AI capabilities in operations, pinpoint pain points across different operational areas, and prioritize one or two low-risk, high-impact use cases that can be implemented within three to four months to secure buy-in. Many organizations face resource constraints or limited leadership support, making it challenging to roll out multiple use cases simultaneously. By beginning with targeted experimentation, you can avoid overextending your efforts.
- Start with manageable projects and prioritize ROI: focus on simple, high-value use cases that deliver measurable results.
2. Assess Data Readiness
- Ensure you have access to high-quality, structured data, as AI relies heavily on data for training and accuracy.
- Invest in consolidating and cleaning data from multiple sources (e.g., sales, CRM, supply chain).
- Set up a scalable data infrastructure to handle future AI applications.
3. Choose the Right AI Tools or Partners
- Decide whether to build custom AI solutions or use off-the-shelf tools.
- Evaluate platforms specifically designed for retail, such as Salesforce Einstein, Google Cloud Retail AI, or AWS Machine Learning.
- If in-house expertise is limited, partner with AI development firms or no-code/low-code AI platforms to accelerate implementation.
4. Start Small and Scale Gradually
- Begin with a pilot project in a focused area, such as optimizing product recommendations on your website or automating customer service.
- Monitor performance, measure ROI, and gather feedback to refine the solution.
- Once the pilot is successful, expand AI integration across other business areas.
- Develop a structured roadmap. Create a 1-2 year roadmap focused on high-value, targeted operations to prioritize future initiatives effectively. Include a timeline with measurable impacts to ensure a positive return on investment throughout the implementation process. At the same time, a data and technology strategy should be crafted to support these initiatives rather than attempting to fix everything everywhere.
5. Invest in Training and Change Management
- Train employees on how to use and interact with AI tools effectively.
- Build trust in AI by educating staff and customers on its benefits and addressing concerns about transparency and reliability.
- Foster a culture of innovation to encourage adoption and maximize the value of AI.
- Encourage collaboration and experimentation: Foster a culture of innovation and promote cross-functional teamwork.
- Collaborate with experts: Leverage the expertise of technology partners, consultants, and software providers.
- Tell a compelling story: Craft a clear vision of how AI will transform the customer experience and enhance business outcomes.
Bonus Advice:
Make AI self-funding and self-sustaining. Reinvest the revenue generated from initial use cases into the roadmap's next set of AI initiatives. This self-funding approach allows you to start small while enhancing data and technology capabilities, upskilling the workforce (including digital talent for implementation and frontline talent for utilizing new tools), and gaining leadership support as use cases begin to deliver measurable improvements.
Future of AI in Retail
The future of artificial intelligence in the retail industry is poised to revolutionize every aspect of the customer journey and internal operations. As AI technologies evolve, retailers can expect significant advancements that will enhance customer engagement and operational efficiency. Here are the key trends shaping the future of AI in retail.
- Hyper-Personalization: AI will deliver tailored, one-to-one shopping experiences by analyzing real-time customer data. This will enhance satisfaction, loyalty, and engagement across channels.
- Immersive Experiences: AI-powered tools like augmented and virtual reality will allow customers to try products virtually, explore store layouts, and enjoy interactive shopping, bridging the gap between online and in-store experiences.
- Predictive Analytics and Automation: AI will optimize operations through demand forecasting, dynamic pricing, and process automation, helping retailers manage inventory, improve supply chains, and make data-driven decisions.
These innovations will redefine retail, drive efficiency, enhance customer experiences, and foster sustainable growth.
Thinking about automating parts of your retail operations but not sure where to start? The Sommo team can help you set up AI solutions that fit your business without the hassle.
Here’s what we do:
- Work with you to figure out what AI tools make the most sense for your goals.
- Customize the setup so it works with what you already have.
- Make the whole process smooth and easy to manage.
If you’re ready to explore how AI could help with things like inventory, customer support, or personalized recommendations, let’s chat. It’s easier than you think to get started!
