How This IT Firm Built a Formidable Moat in Gen AI

In conversation with Padmashree Shagrithaya, executive VP & MD – Insights and Data GBL, at Capgemini, on how the company is betting big on AI
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In today’s tech landscape, numerous IT companies are pouring substantial investments into Generative AI. TCS, a prominent player in the IT domain, is diligently working on developing its own alternative to GitHub Copilot to revamp enterprise code generation. Simultaneously, Accenture unveiled a new study titled ‘A New Era of Generative AI for Everyone’ exploring how generative AI and LLM serve as a compass. 

Another key player, Tech Mahindra, is taking a distinctive approach with its Generative AI Studio, empowering businesses with a user-friendly interface that facilitates a myriad of customisation options for their content. As the competitive landscape unfolds, these industry giants spearhead the charge towards AI innovation, each with their unique contributions and immense impact. 

Amidst this wave exists a notable IT giant that is leaving a profound imprint on the industry — Capgemini. Headquartered in Paris, the global IT services and consulting company offers AI-powered solutions for businesses to improve their operations, make better decisions, and gain a competitive advantage with over 360,000 employees in over 50 countries. The company’s clientele includes some of the world’s largest corporations, such as BMW, L’Oreal and more. Capgemini operates across three playing fields: customer first, intelligent industry, and enterprise management. 

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“Our innovation approach is called hybrid intelligence which involves combining engineering intelligence with data science to develop a more efficient and robust solution,” Padmashree Shagrithaya, executive vice president and MD – Insights and Data GBL, at Capgemini, told AIM.

Read more: Gen AI is All Over the IT World, Except on Ground

Tech Solutions at Disposal

Capgemini employs pre-built models and frameworks such as TensorFlow and PyTorch, which can be fine-tuned for specific use cases, as well as in-house models like AI Class Box, which is used on top of other platforms to speed up the MLOps process. 

Another in-house platform is IDEA (Industrialised Data and AI Engineering Acceleration), which aims to help clients move to the cloud and ensure their total cost of operation is minimal. The company also focuses on AI-powered automation of ML processes using various platforms and partners, including open-source solutions. Containerisation is also another method to make models reusable across different domains.

Quantum Computing for the Win

IBM, one of the leaders in quantum computing plans to build a 1,000-qubit machine this year and a 4,000-qubit one next year. However, despite this progress, quantum-based products are still in the research and development stages, and quantum companies are seeking ways to bring the technology to the market. One of the companies actively working with IBM in this space is Capgemini, which has a dedicated quantum lab (QLab).  

“Our work in innovation primarily involves proof-of-concept projects with clients and promises to discuss this further as the conversation progresses,” said Shagrithaya.

Capgemini’s QLab aims to manage research initiatives that aim to create client solutions based on business objectives and cater to sectors that are expected to experience significant benefits from quantum technologies in the near future, such as life sciences, financial services, automotive, and aerospace. It is also responsible for facilitating initial experiments with clients in their pursuit of quantum advancements and enhancing in-house expertise and capabilities in this area.

Another major application of quantum computing lies in life sciences. Capgemini’s acquisition of Altran, a global leader in engineering and R&D services, which has strong capabilities in the Life Sciences space, further strengthens the company’s capabilities in the data-driven drug discovery space. However, there are several limitations of traditional methods in leveraging nature and the genome compared to quantum computing for drug discovery. It works with life sciences organisations to use quantum-based machine learning on IBM hardware.

AI Reshapes Fashion and Cosmetics

The global market for AI in the fashion industry witnessed substantial growth from $0.65 billion in 2022 to $0.91 billion in 2023 and is expected to be worth of $3.72 billion by 2027. 

In the field of fashion and cosmetics, AI is to cater to the increasing demand for transparency, safety, and sustainability. In the cosmetics industry, Capgemini is working with large cosmetic brands like Tarte that use a QR code and AI to provide consumers with comprehensive information about the product, ingredients, and how it matches the consumer’s skin type and cultural background. The information includes the comparison of the ingredients and their impact on the skin, enabling consumers to make informed decisions when purchasing a cosmetic product.  

Moreover, when it comes to designing, the popular clothing brand Levi’s already uses AI to automate and improve its denim designing process. Similarly, Capgemini is also working with popular

“AI has a significant role to play in meeting consumer demands for transparency, safety, and sustainability in both the fashion and cosmetics sectors,” Shagrithaya emphasised. “By providing detailed information to consumers and assisting companies in making sustainable choices, AI can contribute to the industry’s development and help create a more responsible and sustainable future,” she concluded. 
Read more: This Company is Paving the Way for Generative AI Services

Shritama Saha
Shritama Saha is a technology journalist who is keen to learn about AI and analytics play. A graduate in mass communication, she is passionate to explore the influence of data science on fashion, drug development, films, and art.

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