Mr. Sant Kumar Rai is a Account Technical Leader at IBM.
Q: What are some of the most common AI challenges that your clients face?
Common challenges in the field of AI include a shortage of talent and expertise, making it challenging to find skilled professionals. Training and optimizing AI models also demand access to the correct data and substantial computational resources. Many businesses also struggle with a lack of understanding and a clear AI strategy, often relying entirely on external consultants. Another obstacle is ensuring regulatory compliance, particularly regarding data efficacy and AI insights. Moreover, maintaining trust in AI systems is vital, as issues related to transparency and responsible governance can inadvertently perpetuate societal biases present in the training data.
Q: How do you help your clients identify and implement AI solutions that address their specific needs?
We leverage design thinking workshops with customers to understand their business needs and problems. During this workshop, we identify a business use case where AI can potentially be of help. We then narrow down the list of problems to focus on the ones with the highest priority. Each team chooses a problem to concentrate on. After this exercise, we assist the customer in creating a prototype (MVP). Upon successful completion of the MVP, the customer gains the necessary support for production deployment.
Q: What are some of the most successful AI projects you've worked on with your clients?
We delivered AI Powered Revenue Growth to one of India's largest Banks. This helped the bank in the following areas:
- Personalized Marketing & Campaigns: Email, nudges, creatives, digital, etc.
- New Product Development, Competitor Analysis
- Engagement & Retention, Churn Prediction
- Marketing Optimization, MRoI, Brand Insights, Influencer Analysis
- Product Advisory, AI Investment Advisors, Asset Allocation
- Personalized Next Best Action (offer, product, reminder, etc.)
- Customer Acquisition, Lead Prioritization
- Cross-sell & upsell
- Customer Segmentation, Life Events
- Sales RM/Agent Performance, Branch & Channel Performance
Q: What are some of the most promising AI technologies that you're seeing emerge today?
IBM Watsonx is our new integrated data and AI platform. It consists of three primary components:
- Watsonx.data: This is our massive, curated data repository ready to be tapped to train and fine-tune models, with a state-of-the-art data management system.
- Watsonx.ai: An enterprise studio to train, validate, tune, and deploy traditional machine learning and foundation models that provide generative capabilities. It includes Foundation model libraries, Prompt lab, and Prompt Tuning studio.
- Watsonx.governance: A powerful set of tools to ensure AI is executed responsibly. These components work together seamlessly throughout the entire lifecycle of foundation models, built on top of Red Hat OpenShift.
Q: What advice would you give to businesses that are considering adopting AI?
Businesses should recognize that AI technologies have a critical transformative effect on their operations, sales processes, and customer experiences. This is due to several reasons, including modernizing business processes, automating workflows, predicting and influencing the marketplace, cutting costs, increasing efficiency, and improving customer experience. Embracing AI is crucial for enterprises to stay competitive, provide exceptional customer experiences, and navigate the complexities of the evolving economic landscape.
Q: How do you see AI transforming the way businesses operate in the next 5-10 years?
Artificial intelligence (AI) has transitioned from a world where companies consider using AI to enhance their business to a world where leading companies are becoming AI-first. This decision will shape how they operate, collaborate with employees, and engage with customers and suppliers. LLM/Generative AI models have opened new horizons across industries by enabling data-driven decisions, enhancing customer experiences, and driving innovation. We can anticipate even more transformative changes as these models evolve, revolutionizing how enterprises leverage AI for insights, efficiency, and personalized services.