The retail sector is always changing due to supply networks that are becoming increasingly complicated, disruptive technology, and changing consumer expectations. Staying ahead requires more than just incremental improvements; it demands a fundamental shift in how retailers operate. Agentic AI, with its ability to autonomously act and learn, offers a transformative solution.
A study by McKinsey shows that more than half of the activities in retail can be automated using current, at-scale technology. But how does one ensure efficient implementation of AI within their business? 

This article explores the key considerations for implementing Agentic AI in retail, examining how these intelligent agents can revolutionize everything from personalized shopping experiences to optimised inventory management. Let us take a look.

Agentic AI refers to artificial intelligence systems that possess a degree of autonomy and can make decisions or take actions on behalf of human operators. In the retail context, this translates to AI-powered solutions that can analyze vast amounts of data, identify patterns, and execute complex tasks with minimal human intervention.

The importance of Agentic AI in retail cannot be overstated. As the industry grapples with unprecedented challenges – from supply chain disruptions to evolving consumer behaviors – the need for intelligent, adaptive systems has never been more critical. Agentic AI offers retailers the ability to:

• Anticipate and respond to market trends in real-time: Agentic AI can analyze real-time data from various sources (e.g., social media, sales data, weather patterns) to identify emerging trends and automatically adjust inventory, pricing, and marketing strategies. This requires data accessibility and calibration and control of AI features.
• Optimize inventory management and pricing strategies: Autonomous agents can optimize stock levels, predict demand fluctuations, and adjust pricing to maximize revenue and minimize waste. This contributes to speed and efficiency and financial and OPEX improvements.
• Deliver hyper-personalized customer experiences across channels: Agentic AI can power personalized recommendations, targeted promotions, and proactive customer service across all touchpoints, creating a seamless and engaging customer journey. 
• Streamline operations and reduce costs through predictive maintenance and process automation: Agentic AI can automate routine tasks, predict equipment failures, and optimize workflows, leading to significant cost savings and improved operational efficiency. 
• Enhance decision-making through advanced analytics and prescriptive insights: Agentic AI can provide real-time insights and data-driven predictions, allowing retailers to make faster and more informed decisions regarding pricing, promotions, resource allocation, and overall business strategy. 

Current market applications of Agentic AI in retail keep evolving and leading retailers are already utilising this technology to revolutionize their operations. For instance, Amazon’s anticipatory shipping model uses AI to predict customer orders and preemptively move products to nearby fulfillment centers.

Agentic AI offers significant opportunities for retailers to differentiate themselves, improve efficiency, and drive growth, provided they address the inherent implementation challenges.  These opportunities align with the need for a holistic approach and industry capability plan, moving beyond limited scope implementations to achieve broader organizational impact.

1. Enhanced Customer Experiences through Personalized Interactions:

Agentic AI empowers retailers to create truly unique and personalized customer experiences that go beyond simple recommendations. 

• Develop AI-powered personal shopping assistants: These intelligent agents can provide tailored recommendations across all channels, proactively anticipate customer needs, and even manage entire shopping journeys autonomously. This directly addresses the need for differentiation and creates a competitive customer treatment.
• Offer proactive and personalized customer service: Agentic AI can power chatbots and virtual assistants that not only answer questions but also proactively offer assistance, resolve issues, and personalize interactions in real-time.
• Create dynamic pricing and promotions: Autonomous agents can adjust pricing and promotions in real-time based on market conditions, competitor pricing, and even individual customer behavior, maximizing revenue and profitability. 

2. Optimized Operations and Streamlined Supply Chains:

Agentic AI significantly enhances operational efficiency and accelerates the realization of business impact by automating routine tasks and providing real-time insights. This aligns with the speed and efficiency opportunity and addresses the challenge of slow implementation.

• Implement intelligent inventory management: Agentic AI can optimize stock levels in real-time, reducing waste, improving product availability, and minimizing storage costs. 
• Automate supply chain optimization: Autonomous agents can manage logistics, predict disruptions, and optimize delivery routes, leading to significant cost savings and improved efficiency.
• Improve predictive maintenance: AI-powered systems can predict equipment failures and schedule maintenance proactively, minimizing downtime and reducing maintenance costs.

3. Data-Driven Decision-Making and Enhanced Insights:

Agentic AI empowers retailers with real-time insights and data-driven predictions, enabling them to make faster and more informed decisions across all areas of the business.

• Enhance decision-making speed and accuracy: Real-time insights and data-driven predictions enable retailers to make faster and more informed decisions regarding pricing, promotions, and resource allocation. 
• Gain deeper customer insights: Agentic AI can analyze vast amounts of customer data to identify trends, preferences, and behaviors, providing valuable insights for targeted marketing and product development.

4. Increased Efficiency and Productivity through Automation:

Agentic AI automates routine tasks, freeing up human employees to focus on higher-value activities that require creativity, strategic thinking, and human interaction.

• Reduce operational costs: Automating tasks like customer service, order fulfillment, and supply chain management can significantly reduce labor costs and improve profit margins. This directly contributes to financial and OPEX improvements.
• Improve employee productivity: Automating repetitive tasks with AI, allows employees to focus on more strategic and creative work, leading to increased productivity and job satisfaction.

5.  Building a Data-Driven Culture and Improved Data Accessibility:

Implementing Agentic AI provides a unique opportunity to overhaul data management practices and create a more accessible, unified data ecosystem. 

• Implement data mesh solutions: Decentralize data ownership and improve accessibility through data mesh architectures, empowering business units to utilise data more effectively. This directly addresses data accessibility opportunities and supports wider impacts across the organization.
• Develop AI-driven data quality management tools: Ensure consistent, high-quality data across the organization using AI-powered tools that automate data cleansing, validation, and enrichment.
• Create intuitive, AI-powered analytics interfaces: Democratize data access and foster a data-driven culture by providing user-friendly interfaces that allow business users to easily access and analyze data.

While the potential of Agentic AI is substantial, retailers must proactively address several key challenges to ensure successful implementation and avoid the pitfalls of slow implementation, limited scope,and the snowball effect of disparate AI solutions. The following strategies are crucial:

1. Developing a Holistic Enterprise AI Strategy for Retail:

• Move beyond isolated use cases in retail: A comprehensive strategy is essential to avoid the trap of limited scope implementations that address only narrow retail functions (e.g., just customer service or just inventory). Focus on a holistic approach that integrates AI across all relevant retail business functions, from merchandising and marketing to supply chain and store operations.
• Establish a clear vision and roadmap for retail AI: Define specific, measurable business objectives for AI initiatives within the retail context. For example, instead of improve efficiency, aim for reduce inventory holding costs. Develop a phased roadmap with short-term wins (e.g., AI-powered product recommendations) and long-term goals (e.g., fully autonomous store operations). .
• Secure executive sponsorship and cross-functional alignment in retail: Gain buy-in from C-suite leaders responsible for retail operations and foster collaboration between IT, retail business units, and data science teams. This is crucial for aligning retail initiatives, securing resources, and overcoming organizational silos within the retail organization.

2. Building a Robust Data Foundation for Retail:

• Address data quality issues: Implement data governance frameworks that address the unique data challenges in retail, such as inconsistent product data, fragmented customer data across channels, and the high volume of transactional data. Invest in data cleansing and enrichment tools tailored for retail data and prioritize data quality management. 
• Modernize data infrastructure for retail needs: Invest in modern data warehouses, data lakes, and data hubs designed to handle the scale and complexity of retail data. This modern data foundation is essential for supporting AI and Agentic AI models in retail applications, such as personalized recommendations, inventory optimization, and dynamic pricing.
• Implement data mesh solutions for retail data: Decentralize data ownership and improve accessibility through data mesh architectures, empowering retail business units to utilise data more effectively for specific needs, such as marketing analytics or store performance analysis.

3. Modernizing Legacy Systems for Retail AI Strategically:

• Prioritize modernization efforts relevant to retail AI: Focus on modernizing legacy systems that are critical for AI integration and retail business value. For example, modernize the point-of-sale (POS) system to integrate with AI-powered customer loyalty programs or the inventory management system to enable AI-driven forecasting. Avoid a rip and replace approach; consider re-platforming, re-architecting, or refactoring as appropriate.
• Embrace APIs and microservices for retail systems: Make use of APIs and microservices to enable seamless integration between legacy retail systems and new AI solutions. This allows for a more agile and flexible approach to modernization, crucial for keeping pace with evolving retail technologies.
• Balance modernization with ongoing retail operations: Carefully plan and execute modernization projects to minimize disruption to ongoing retail business operations, especially during peak seasons.

4. Bridging the Skills Gap and Building Internal Expertise in Retail AI:

• Invest in upskilling and reskilling for retail AI roles: Provide training and development opportunities for existing employees to enhance their AI literacy and develop specialized skills in data science, machine learning, and AI engineering with a focus on retail applications.
• Establish an AI Center of Excellence for Retail: Create a centralized hub for AI expertise within the retail organization, fostering collaboration, driving innovation, and disseminating best practices for retail AI applications.

5. Establishing AI Governance and Ethical Frameworks for Retail:

• Develop ethical guidelines for retail AI: Define clear ethical principles for AI development and deployment in retail, addressing issues like bias in customer profiling, fairness in pricing algorithms, and data privacy related to customer information.
• Implement governance policies for retail AI: Establish clear roles, responsibilities, and processes for managing AI initiatives within the retail organization, ensuring compliance with retail-specific regulations and ethical standards.
• Prioritize explainable AI (XAI) in retail: Use XAI techniques to ensure transparency in AI decision-making for retail applications, building trust with customers and addressing concerns about the black box effect in areas like personalized recommendations or credit scoring.

66degrees specializes in offering retailers the right tools and strategies for AI adoption and legacy system transformation. Our solutions are designed to create tangible business value while fostering innovation:

1. AI Strategy and Roadmap Development

We work closely with retailers to develop comprehensive AI strategies aligned with their business objectives. This includes:

• Assessing current capabilities and identifying AI opportunities
• Creating a phased implementation roadmap

2. Data and AI Factory Methodology

Our unique approach ensures rapid value delivery while maintaining long-term innovation:

• Iterative 30-day value delivery cycles
• Continuous refinement and scaling of AI initiatives

3. Legacy System Modernization

We help retailers bridge the gap between legacy infrastructure and modern AI capabilities:

• Developing custom integration solutions
•  Implementing data mesh architectures for improved accessibility
• Gradual migration to cloud-based, AI-ready platforms

A leading American retailer faced challenges in delivering personalized experiences to its vast customer base. According to McKinsey, retailers who personalize shopping experiences see an average 6-10% lift in revenue. With millions of customers and billions in annual revenue, the company struggled to utilise its extensive data effectively to enhance customer engagement and drive sales growth.

Challenges

• Inability to process and analyze large volumes of customer data in real-time
• Lack of scalable infrastructure to support advanced analytics and machine learning
• Difficulty in creating and deploying personalized marketing campaigns efficiently
• Inconsistent customer experiences across various touchpoints

Solution

66degrees implemented a comprehensive Google Cloud solution to address these challenges:

• Utilised BigQuery for efficient data storage and analysis
• Utilized Dataflow for real-time data processing
• Implemented AI Platform for advanced machine learning capabilities
• Deployed Recommendations AI to deliver personalized product recommendations
• Used Cloud Composer for workflow orchestration

This case study demonstrates the transformative power of AI in retail, showcasing how legacy systems can be modernized to drive growth and improve customer experiences. With the help of cloud-based AI solutions, retailers can unlock the full potential of their data, enabling them to compete effectively in an increasingly digital marketplace.

While the challenges of transforming legacy systems in retail are significant, the potential rewards are too substantial to ignore. 66degrees can help address the challenges of integrating AI with existing infrastructure. We help retailers bridge the gap between legacy systems and modern AI capabilities by developing custom integration solutions, implementing data mesh architectures for improved data accessibility, and guiding them through a gradual migration to cloud-based, AI-ready platforms. 

At 66degrees, we empower organizations to make the right choices when it comes to cloud modernization and setting the stage for AI integration. Our strategic Google Cloud consulting services help align your technology needs with your business objectives, ensuring a robust, future-proof AI infrastructure. By making use of our deep expertise in cloud, data and AI engineering, we guide you through every step of your transformation journey. Connect with us to learn how we can help.