CQuotient, a software startup founded by Rama Ramakrishnan, offers personalized recommendations for retailers by diligently noting down customer interactions. The software has been adopted by Salesforce. Ramakrishnan, now a professor at MIT Sloan, teaches students how to apply AI technologies pragmatically. He also guides senior executives in using pre-trained AI models and understanding different categories of AI, such as machine learning and generative AI. Ramakrishnan advises caution in using generative AI, as it can produce plausible but possibly inaccurate or offensive outputs. He suggests considering costs and consequences when applying AI software and conducting pilot projects before full implementation. Common corporate uses for generative AI include software development, content generation, and chatbots for internal document search. Ramakrishnan helps companies leverage AI to enhance their products, services, employee productivity, and operational efficiency.
Revolutionizing Retail with AI: Practical Solutions for Middle Managers
Back in 2010, Rama Ramakrishnan realized that personalized recommendations in retail systems were not providing significant benefits for retailers. He also noticed that many customers only shopped once or twice a year, leaving retailers with limited knowledge about them. To address these challenges, Ramakrishnan founded CQuotient, a startup that has now become the foundation for Salesforce’s AI e-commerce platform.
Today, CQuotient’s technology interacts with over a billion shoppers on Black Friday alone. Ramakrishnan, now a professor at MIT Sloan School of Management, teaches students how to practically apply these amazing technologies in the real world. He also shares his expertise with senior executives through MIT executive education.
Understanding AI Categories
AI aims to give computers the ability to perform cognitive tasks that were traditionally done by humans. The traditional approach to AI, based on if/then rules, had limitations. Machine learning, on the other hand, allows computers to learn from examples and has been successful in tasks like credit scoring and demand forecasting. Deep learning, which emerged around 2010, enabled working with unstructured data like images and audio.
Generative AI, a recent development within deep learning, can create unstructured outputs such as human-like text and realistic images. However, it’s important to note that generative AI may produce content that is factually inaccurate, biased, or offensive.
Finding the Right Corporate Roles for LLMs
LLMs, trained on vast amounts of internet text, can predict the next most likely word and generate complex outputs like poetry and reasoning. However, users need to ensure the output is correct and relevant for their needs. LLMs can be applied to tasks like software development, content generation, and enterprise document search. It’s crucial to have a human in the loop to verify and correct the output.
Evolve Your Company with AI
If you want to leverage AI to stay competitive and redefine your way of work, consider the following steps:
- Identify Automation Opportunities: Locate customer interaction points that can benefit from AI.
- Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes.
- Select an AI Solution: Choose tools that align with your needs and provide customization.
- Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.
For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com. Explore our AI Sales Bot at itinai.com/aisalesbot to automate customer engagement and manage interactions across all customer journey stages.
List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Generating opportunities with generative AI
- MIT News – Artificial intelligence
- Twitter – @itinaicom
