How AI is transforming the retail and consumer goods industry

Key takeways

• AI is shifting from generative to agent-based: The next frontier in retail AI is agent-based systems, which move beyond content generation to autonomous task execution, enabling semi-automated retail operations
• “Connected Retail” drives efficiency: SymphonyAI’s Connected Retail model integrates supply chain, merchandising, forecasting, and store ops, creating a closed-loop system that boosts efficiency and decision-making
• AI enhances (not replaces) jobs: AI automates repetitive tasks, allowing employees to focus on strategic, creative, and human-centric roles—increasing productivity by up to 40%
• Practical AI deployment is a differentiator: Unlike many companies still in pilot phases, SymphonyAI’s AI is fully operational in over 300,000 stores, handling billions of transactions and delivering real-world ROI
• Adoption challenges require trust and training: Barriers like data quality, institutional resistance, and trust in AI must be addressed through tools like the Eureka platform, executive bootcamps, and transparent ROI

Manish Choudhary speaks at Thinkdata Podcast

SymphonyAI Retail-CPG division president Manish Choudhary stopped by the Thinkdata Podcast to discuss what’s really going on with AI and why some retailers and CPGs may not know they are falling behind. 

The transformative effects of AI are being felt throughout the retail industry, but more profound change is ahead. To explore what’s next and why some companies are winning with AI, SymphonyAI Retail-CPG division president Manish Choudhary joined the Thinkdata Podcast hosted by Dataworks co-founder Alex Hutchings.

Podcast conversation summary

Alex Hutchings: Let’s start with an overview of SymphonyAI and your role within the company? 

Manish Choudhary: SymphonyAI is a leading enterprise software company harnessing predictive, generative and agentic AI technologies to serve retailers and consumer packaged goods (CPG) companies globally. Our AI solutions are deployed across approximately 300,000 stores worldwide, and we have strong partnerships with top-tier retailers and CPGs. We aim to revolutionize the industry by transitioning from predictive AI to generative AI, and now towards agent-based AI frameworks.

I am a technologist at heart, with a passion for coding and developing applications using large language models (LLMs). Prior to SymphonyAI, I served as Executive Vice President at Diebold Nixdorf and worked at organizations like Pitney Bowes following several successful company exits. I’ve also authored books on AI, and the rapid evolution of generative AI suggests it might soon be time for another book.

AH: How does SymphonyAI differentiate itself from other enterprise software companies in the retail sector?

MC: Our approach is about fundamentally changing how the retail industry operates through our unique “Connected Retail” methodology. SymphonyAI’s distinction lies in seamlessly integrating AI technologies across various retail functions, ensuring optimal performance and efficiency. Unlike some competitors that are still in the proof-of-concept stage, our technology is fully operational and has processed billions of grocery transactions. This practical application is what sets us apart in the marketplace.

AH: Could you elaborate on the concept of “Connected Retail”?

MC: Connected Retail involves the integration of four critical components essential for any retailer’s success: supply chain management, merchandising, demand forecasting, and store operations. SymphonyAI creates AI-driven solutions to enhance demand planning, optimize supply chains, and refine pricing and promotions. We offer a series of platforms that connect supply chain and merchandising processes, helping category planners use data-driven insights effectively.

For example, our AI capabilities handle tasks like planogram automation and space planning, enabling retailers to optimize their product assortment and store layouts. Furthermore, our cutting-edge computer vision technology can scan shelves to predict stock outages and ensure compliance with planograms and pricing strategies. By bringing these elements together, we create a seamless, closed-loop system that drives retail efficiency. Our flagship platform, CINDE, is at the forefront of this technology, representing a $120 million investment in R&D.

AH: What were your initial priorities when you joined SymphonyAI to lead the retail and CPG division?

MC: When joining any organization, my focus has always been on two elements: the product and the client. Achieving a strong product-market fit requires an understanding of market and technology transitions. When I started at SymphonyAI, generative and predictive AI were the major technology transitions impacting our industry. It was crucial to quickly transform our product offerings to align with these advancements and deliver real ROI for our clients. Addressing client pain points and ensuring the technology transition aligned with market needs were key to our strategy.

AH: How has your technical background in machine learning and AI contributed to your role?

MC: Having a solid grounding in AI and machine learning is crucial in today’s rapidly evolving tech landscape. Leaders must be authentic in their understanding of technology and its implementation. This knowledge facilitates a clearer understanding of client problems, enabling us to provide practical solutions. At SymphonyAI, our leadership team, from the chairman to the CEO and beyond, consists of technically proficient individuals who actively contribute to our product development. This hands-on approach is rare and fosters an environment where we are not only discussing technology but actively developing and deploying it.

AH: How do you see AI evolving in the retail industry over the next 6-12 months?

MC: The shift from generative AI to agent-based AI will be significant. While generative AI has provided unprecedented access to knowledge, agent-based AI promises more autonomy in operations. Organizations will benefit as repetitive tasks become automated, freeing up resources for strategic decision-making. In the future, AI-driven automation will lead to semi-automated workforces that run efficiently, revolutionizing processes from category management to store operations. Hyper-personalization will gain importance as shopping becomes increasingly omnichannel, driven by the need for more efficient customer engagement and operational performance.

AH: What challenges do companies face when adopting AI in retail?

MC: Successful AI adoption requires overcoming hurdles related to trust, data management, and institutional change. Building trust in AI systems is crucial, ensuring reliability and specificity in their outputs. SymphonyAI leverages its decades of experience in retail to provide tailored AI solutions that clients can depend on. Our proprietary Eureka platform facilitates data management, ensuring clean, reliable data for AI applications. Moreover, we invest in training and educating leaders through bootcamps and workshops, promoting a top-down understanding of AI’s benefits and ROI potential. Combined, these efforts reduce barriers to AI adoption and ensure a seamless integration into business processes.

AH: How does AI affect job roles within the retail industry? 

MC: AI primarily automates tasks rather than replacing jobs. It excels at performing repetitive tasks such as demand forecasting and pricing simulations, allowing humans to focus on creative and strategic aspects like relationship building and decision-making. Retailing remains a human-centric business, and AI enhances the workforce by increasing productivity and profitability. By adopting AI, retailers not only keep pace with industry changes but also gain a competitive edge in a rapidly evolving landscape.

AH: How do you address concerns about AI replacing jobs?

MC: It’s crucial to understand that AI is designed to replace tasks, not people. It handles repetitive functions such as forecasting demand, flagging anomalies, or running pricing scenarios, which allows human workers to focus on areas that require intuition, creativity, and relationship-building skills. This approach actually amplifies human potential, making workers more effective and productive.

The time AI frees up enables people to engage in higher-order tasks and strategic thinking. In retail, which remains a human-centric industry, AI can act as a co-pilot, aiding category managers by providing unobvious insights they couldn’t access before due to time constraints. The ultimate goal is to enhance productivity by up to 40%, leading to better pricing decisions and improved organizational efficiency. For instance, AI can optimize holiday promotions, potentially halving costs while boosting ROI. It’s a matter of survival in today’s competitive landscape; using AI isn’t optional, as competitors will likely harness its capabilities.

AH: What’s next? How do you envision AI and retail evolving?

MC: The journey from generative AI to agent-based AI is just beginning. Agent technologies will enable more autonomous decision-making within organizations, automating repetitive tasks and freeing up considerable capacity for more strategic functions. In a retail context, this means envisioning a workforce that operates semi-autonomously with AI agents handling specific functions like category management and store operations. 

In the future, retail operations will transform to be more intelligent, reducing guesswork and enhancing personalization. The role of AI will extend from back-office processes to front-end customer interactions, reshaping how stores are designed and operated. This evolution promises to redefine retail experiences, making them more personalized, efficient, and enjoyable for consumers.

By addressing these challenges and harnessing AI’s potential, SymphonyAI’s customers are at the forefront of revolutionizing the retail and consumer goods industry. As AI becomes an essential component of retail strategy, its role will continue to expand, offering new opportunities for innovation and growth in a dynamic market landscape.