Overview / CareersAI Careers in Retail
The retail industry is wide-reaching, as it spans the sale of goods and services to consumers. These sales take place across channels, from brick-and-mortar stores to online e-commerce platforms. Careers in retail extend far beyond a person at a cash register or a salesperson. Working in retail could mean you're focused on customer service, digital transformation initiatives, inventory management, marketing, merchandising, pricing strategies, or supply chain logistics.
AI is set to fundamentally reshape the retail industry by driving hyper-personalized, unified, and frictionless omnichannel customer experiences. AI systems could anticipate consumer needs before they arise, automate supply chain and inventory decisions with near-perfect accuracy, and deliver real-time personalized promotions. Innovations like cashier-less stores, virtual try-ons, and AI-curated shopping assistants could redefine convenience and customer engagement.
What would an AI professional contribute to the retail industry?
An AI professional working in retail would typically work on the corporate side of the business and focus on developing and deploying intelligent systems and services that enhance customer experience and engagement, optimize operations, build brand equity, and drive sales. These systems and services include recommendation engines for personalized shopping and targeted marketing, inventory forecasting and management, optimizing logistics and supply chain, pricing, customer sentiment analysis from reviews and social media, and other AI-powered tasks such as computer vision for in-store analytics. AI is also used in fraud detection and chatbot-based customer service. Retail AI roles often require a strong understanding of consumer behavior, real-time decision-making, and business acumen.
What are the biggest opportunities and challenges for AI in the industry?
AI offers powerful tools to drive personalization at scale, streamline inventory and supply chain operations, predict trends, and optimize pricing strategies, all in close to real time. AI can also enable visual search, fraud and theft detection, and hyperlocal marketing, helping retailers differentiate themselves in a competitive marketplace and elevate customer satisfaction.
One of the biggest challenges deals with data quality. Retailers collect vast amounts of customer and transaction data, but ensuring the data is accurate, unbiased, and ethically sourced and used remains a hurdle. Privacy regulations also add complexity, especially for personalized experiences. Integrating AI systems with legacy systems and coordinating across online and offline operations can also be technically demanding.
How does MSAI prepare students to lead in the retail space?
The AI education and training that MSAI students receive span several aspects for what AI in retail involves. A key application of AI discussed in the core courses taken by the students is recommendation engines, which play a critical role in retail by suggesting products and services to consumers via store websites, mobile apps, and targeted marketing. Such recommendation engines need to understand their target customers very well, and that requires, among other things, sentiment analysis, which is another important application of AI, typically via natural language processing, which students get a lot of exposure to. Then there is the crucial and contemporary widespread use of AI in retail: chatbots, which aim to deliver customer service that does away with phone wait times. MSAI students have the opportunity to design and develop chatbots as part of several endeavors — the half-quarter hands-on training in the Winter on developing generative AI solutions in Azure, the Spring practicum project, and the Fall capstone project that sees some companies explore various aspects of chatbots and agentic AI systems.
Where have MSAI students and alumni interned or worked?
- Target
- The Home Depot
- Walmart Global Tech
- Chewy
- Transformco
- Cencosud S.A.
- Cstore Master
Featured Alumni
Kartikeya Vats
MSAI '23, Senior AI and Data Scientist for TargetKartikeya Vats was six years into his data science career, working at IBM, when he decided to further his education. Now he's applying the lessons he learned in MSAI to build predictive models for Target that identify if items are out of stock on the sales floor despite being listed as available in the store's ledger system.
His mission is to create happy, loyal shoppers while simultaneously strengthening the company’s supply chain.
“If you believe an item is present on the shelf of the store but it’s not, this misinformation creates confusion,” Vats said. “It won’t trigger replenishment from our distribution centers or from the back room. That impacts the customer experience.”
Akshay Kumar, Bodhisatta Maiti, and Dhruv Narayan
MSAI '22, Home DepotAs MSAI students, Akshay Kumar, Bodhisatta Maiti, and Dhruv Narayanall worked on a practicum project with Home Depot to use machine learning and computer vision techniques to identify the types and number of products customers had with them during check out. The three said Home Depot was happy with their work, and they were given the opportunity to expand on it as summer interns. After that, they became part-time workers with the company while they finished their MSAI capstone project.
Today, all three of them work for Home Depot. Maiti is a staff machine learning engineer, while Kumar and Narayan are senior machine learning engineers.
"All three of us were interested in the Home Depot project because this was a computer vision project and we'd be doing this at the industrial scale," Maiti said. "We were certainly excited to see how we can apply AI techniques in the retail domain."
