Senthil Nathan Rajendran, Co-founder & CEO, Tenzai Systems

Senthil is the Founder & CEO of Tenzai Systems, an award-winning AI and Data Science consulting firm that is focused on democratizing AI and making it accessible, scalable, and responsible for enterprises. He is a highly accomplished thought leader, innovator, and technologist with over 17 years of experience in the fields of Artificial Intelligence and Data Science. He has consulted large Fortune 1000 clients across multiple verticals, including Retail, Consumer Goods, Travel, Manufacturing, and Financial Services. In addition to his consulting work, Senthil has filed 7 patents in areas such as Augmented Analytics, Generative AI, Natural Language Processing, and AutoML. He is the founder of Tenzai Systems, an award-winning AI and Data Science consulting firm that is focused on democratizing AI and making it accessible, scalable, and responsible for enterprises.


Generative AI is rapidly gaining attention in the business world, particularly in the retail and consumer sectors. In November 2022, the launch of ChatGPT caused a sensation, reaching 100 million users within two months and becoming the fastest consumer app in history. Microsoft also made waves by integrating ChatGPT into its popular product offerings such as Bing, GitHub, Dynamics, Teams, Azure, Power Platform, and Office 365. This move prompted enterprises to take note of the potential of generative AI in enhancing productivity and transforming their business models.

Recently, consulting firms BCG and Bain signed separate agreements with Open AI to accelerate enterprise adoption of generative AI, indicating the growing interest in this technology. Leading consumer brands, including Coca-Cola, Levi’s, and Tommy Hilfiger, have announced that they are exploring the use of generative AI in various areas such as marketing, e-commerce, merchandising, and product development.

With its ability to reduce costs, improve productivity, and create personalized customer experiences, generative AI holds great promise for the retail and consumer industries. In this article, we will explore some of the high-impact use cases of generative AI for these sectors.

Product Research:

Consumer brands can enhance their research quality and scale by aggregating diverse research reports and leveraging generative AI to help product teams analyze unstructured data using natural language queries. It can also help them create automated summaries of key insights on product trends. By monitoring social media posts, generative AI can spot trends and understand emerging designs, products, and consumer preferences. For instance, Axe launched Axe AI, a special edition body spray for men, and employed AI to analyze 46 TB of data, 6000 ingredients, and 3.5 million potential combinations to identify the right formulation for the fragrance.

E-commerce & Merchandizing:

Generative AI can help merchandizers create engaging product descriptions across SKUs at scale. Brands can also use generative AI to develop 3D catalogs at a fraction of the cost and time compared to conventional approaches. Merchandizers can share text or 2D images as prompts or inputs to generate high-quality and interactive 3D product catalogs. 

Another interesting area is photoshoots, where brands can create virtual models using generative AI. This helps them to accelerate the entire process of photoshoots and reduce costs by a significant margin. Recently, Levi’s announced that it will start testing AI-generated models on its website. This initiative will help Levi’s to showcase clothing items on a variety of models with different body types, sizes, ages, and skin tones. However, this move has also been criticized for its potential impact on models from marginalized and minority backgrounds


According to Gartner, AI-generated content is expected to account for 30% of all marketing content by 2025. Generative AI can help marketers create various forms of content such as blogs, website content, email copies, and ad scripts. Popular tools like, Jasper, Writesonic, and Copysmith are used to create blogs, emails, and other marketing copies. It can also help create images for landing pages, stock images, digital ads, and virtual models. Additionally, marketers can use tools like Synthesia,, Lexica, and Playground to generate video posts and commercials. For example, Mondelez recently used generative AI featuring Shah Rukh to develop personalized videos for over 500 stores across India, resulting in a 29% increase in sales for its Celebrations brand during Diwali.

Customer Experience:

Brands can employ generative AI to create personalized experiences for customers at scale throughout their entire journey. Organizations can use generative AI to create different landing page designs, content, and layouts for each customer based on their target personas. Chatbots and virtual avatars powered by generative AI can also act as personal concierges for customers, guiding them through the purchase journey, from product discovery to shipment tracking and support. By leveraging generative AI, brands can enhance their customer engagement and create better customer experiences.

Operations & Supply Chain

Generative AI has great potential in procurement and sourcing within operations. Procurement teams conduct extensive research to identify insights across macroeconomic factors, weather, political, and supplier risk. Generative AI can act as a virtual research assistant to help procurement teams query and analyze vast amounts of data, creating automated reports. Procurement teams can also use generative AI to draft vendor contracts. Recently, Microsoft Ventures invested in Evisort, a tech firm whose generative AI platform can draft and redline contracts based on preferred language and existing contracts unique to each organization.

Enterprise & Other Support Functions

Retail & Consumer brands can explore generative AI in other areas like 

Decision Intelligence – According to McKinsey, business users, and knowledge workers spend an average of 2 hours per day searching for data or information. Generative AI can help business users get receive real-time insights across the diverse enterprise and SaaS applications. Generative AI can help translate natural language queries from business users into SQL, allowing users across the enterprise to make informed and timely decisions backed by data. Generative AI can also create automated summaries from enterprise applications and financial documents like balance sheets, P&L data, and earnings call transcripts.

Training – Text-to-video generation has great potential in employee training. Organizations can create employee training videos simply by feeding in content like training manuals and presentations. Startups like Rephrase are exploring some interesting applications in these areas.

Recruitment – Generative AI can play a significant role across the recruitment lifecycle. To start, text generation models can help create impactful job descriptions, recruitment posts, and ads. It can also help recruiters search, analyze, and identify the right profiles from thousands of documents. It can automate candidate engagement through automated emails and virtual assistants.

Getting started with Generative AI

To get started with generative AI, it’s important to identify the right use cases across functions and shortlist based on impact, complexity, and risk. It’s recommended to start with use cases that are likely to be consumed within the organization, such as employee virtual assistants, content creation, and documentation, to avoid potential reputational risks. Start small with proofs-of-concept, document learnings, and then scale up to production. It’s also crucial to understand the risks associated with the underlying LLMs, models, and platforms, and develop guardrails accordingly. Lastly, initiate AI governance and responsible practices within your organization to monitor the use of generative AI applications. By following these steps, organizations can effectively implement generative AI while minimizing risks and maximizing impact. 

Generative AI is changing the way retail & consumer industries approach product management and design. Since it has the potential to enhance customer experiences, lower marketing expenses, and boost overall efficiency, brands should closely consider the benefits of this technology. consumer brands that embrace generative AI will have a distinct advantage in the highly dynamic retail & consumer brands space.

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