Aditya Birla Group | AI in R&D-test 1

About the Client

Aditya Birla Group is one of India’s largest and most diversified conglomerates, with a global presence across metals, cement, chemicals, textiles, and advanced materials. Operating in over 40 countries with a workforce of more than 140,000 employees, the Group is a leader in sectors such as aluminium, cement, carbon black, and specialty chemicals. Its strong focus on innovation, sustainability, and research-driven growth has positioned it as a global benchmark in industrial excellence and scientific advancement.

Workshop Objective

The objective of this program was to enable R&D professionals to leverage Artificial Intelligence for enhancing scientific productivity, accelerating experimentation, and improving decision-making in research environments. The training aimed to help participants:

• Understand practical applications of AI in scientific research and experimentation workflows
• Leverage AI for process optimization across chemistry and material science domains
• Reduce experiment cycle time through rapid iterations and data-driven insights
• Enhance hypothesis generation and validation using AI-assisted approaches
• Build awareness of AI tools that can support research efficiency and innovation

Workshop Summary

This intensive, instructor-led program was delivered on-site for lead scientists and research associates, focusing on practical AI applications within R&D environments. The workshop was designed to move beyond conceptual understanding and enable participants to directly apply AI tools and approaches to their ongoing research challenges. The sessions emphasized scientific rigor, domain relevance, and real-world applicability across chemistry and material science contexts.

Key highlights included:

• Exploration of AI applications for improving scientist productivity and research efficiency
• Practical use cases on process optimization across chemical and material workflows
• Application of AI in understanding process chemistry and identifying optimization levers
• Techniques for accelerating experimentation through rapid iteration and reduced trial cycles
• Hands-on exposure to AI-assisted analysis for hypothesis testing and decision-making
• Discussion on integrating AI into existing R&D workflows without disrupting scientific integrity
• Scenario-based exercises reflecting real challenges faced by research teams
• Focus on translating AI insights into actionable improvements in research outcomes

The session concluded with participants identifying high-impact use cases within their respective domains, reinforcing the role of AI as a catalyst for faster, more efficient scientific innovation.

Workshop Details

• Mode: On-Site
• Audience: Lead Scientists and Research Associates
• Batch Size: 10 – 15 Participants
• Duration: 2-day Workshop
• Customized Training Modules
• Certificates for all participants

Trainer Profile

• Industry expert in Industrial AI, Digital Transformation, and advanced analytics applications
• Extensive experience in deploying AI solutions across manufacturing and process-driven industries
• Strong background in applying AI for process optimization, predictive analytics, and operational efficiency
• Worked with leading organizations in metals, chemicals, and manufacturing sectors
• Specializes in translating AI capabilities into domain-specific applications for engineering and R&D teams
• Known for delivering highly practical, implementation-focused sessions with real-world use cases
• Expertise in bridging the gap between data science and domain science, especially in process industries
• Proven track record of enabling organizations to adopt AI for measurable productivity and performance improvements