AIdea report 2024

AIdea India report 2024 

EY
EY India

Multidisciplinary professional services organization

10 minute read 04 Nov 2024
Related topics
AI
Consulting
Technology
Digital

Generative AI (Gen AI) has been at the forefront of technological innovation, revolutionizing various industries, transforming value chains and reimagining business intelligence. AI agents are transforming the nature of work, and a new general computing paradigm is emerging, signifying a fundamental shift in computational processes and capabilities. After years of deep learning advancements, recent breakthroughs in generative AI and foundation models have dramatically expanded what is possible. These models, enhanced by advanced algorithms, vast datasets, and powerful computing, can now generalize beyond their training tasks, impacting both consumers and businesses. This surge in capability has led to a wave of technological optimism. It is being conjectured that AI will become more and more human-like in its ability to interpret and reason, further evolving the way humans interact with machines, and transforming business across the value chain. Sam Altman, the founder, and CEO of OpenAI, said in a statement at the World Economic Forum 2024:

I can’t look in your brain to understand why you’re thinking what you’re thinking. But I can ask you to explain your reasoning and decide if that sounds reasonable to me or not. I think our AI systems will also be able to do the same thing. They’ll be able to explain to us in natural language the steps from A to B, and we can decide whether we think those are good steps, even if we’re not looking into it to see each connection.

About the authors

Photographic portrait of Mahesh Makhija
Mahesh Makhija
Leader, Technology Consulting, EY India
photographic portrait of Rohit Pandharkar
Rohit Pandharkar
Partner, Technology Consulting, EY India
Mahesh is the Leader for Digital and Emerging Technologies at EY in India. With experience in technology innovation, Mahesh helps clients accelerate their digital transformation journey across a variety of areas.
Rohit Pandharkar is a Partner in Technology Consulting at EY, with over 15 years of experience. Rohit has been instrumental in guiding organizations through the transformative waves of Generative AI (GenAI).
AI-ML in manufacturing

The better the question

Decoding the Generative AI magic trick

Generative AI (Gen AI) has been at the forefront of technological innovation, revolutionizing various industries, transforming value chains and reimagining business intelligence.

1

The Future of Gen AI Powered Business Intelligence 

As generative AI continues to evolve, its impact on business intelligence is becoming increasingly profound. This section explores the future trajectory of Gen AI and its potential to revolutionize enterprise strategies.

Re-thinking Technology Strategy – Hybrid Ecosystems with Gen AI Based Intelligence across the value chain

Enterprises will need to rethink their technology strategies to fully harness the potential of Gen AI. Hybrid ecosystems, where traditional IT infrastructure is augmented with Gen AI-based intelligence and orchestration layers, will become the norm. These systems will enable more dynamic and adaptive responses to changing business environments, integrating data and technology into a cohesive data fabric that supports innovation and resilience.

By leveraging LLMs, businesses can overcome previous data limitations, as these models can synthesize information from diverse and unstructured data sources, providing comprehensive and actionable insights. For instance, GPT-4 can process customer feedback from various platforms to improve product development, while Google’s Gemini can assist in complex financial modelling, offering more accurate and timely business forecasts. Modern agent orchestration frameworks such as LangChain and LangSmith, and the ease of accessing unstructured data from LLMs through data platforms like LlamaIndex are paving the way for creating data from business ideas – and enabling a connected intelligence network across the business value chain from the leadership to the workforce and the customer. For example, one of the leading Spanish multinational telecommunications companies is using Gen AI generated synthetic customer data for analytics.

A global bank is using synthetic data sandbox to speed up data intensive POCs with third party vendors. A European financial institution has used synthetic test data to develop a successful mobile banking app. These advancements highlight how Gen AI can drive intelligence across the value chain, transforming business operations and strategic planning.

A snapshot of key metrics

Driving audit quality

Our PCAOB inspection findings
AI-ML in manufacturing

The better the question

The Future of Gen AI Powered Business Intelligence

Generative AI that will dictate the future areas of growth as industries race to adopt the new technology.

2

As generative AI continues to evolve, its impact on business intelligence is becoming increasingly profound. This section explores the future trajectory of Gen AI and its potential to revolutionize enterprise strategies.

generative-ai

This podcast series aims to explore the fascinating world of Generative AI unplugged, its applications, and its impact on various industries. 

Learn more

Enterprises will need to rethink their technology strategies to fully harness the potential of Gen AI. Hybrid ecosystems, where traditional IT infrastructure is augmented with Gen AI-based intelligence and orchestration layers, will become the norm. These systems will enable more dynamic and adaptive responses to changing business environments, integrating data and technology into a cohesive data fabric that supports innovation and resilience. By leveraging LLMs, businesses can overcome previous data limitations, as these models can synthesize information from diverse and unstructured data sources, providing comprehensive and actionable insights. For instance, GPT-4 can process customer feedback from various platforms to improve product development, while Google’s Gemini can assist in complex financial modelling, offering more accurate and timely business forecasts.

 

Modern agent orchestration frameworks such as LangChain and LangSmith, and the ease of accessing unstructured data from LLMs through data platforms like LlamaIndex are paving the way for creating data from business ideas – and enabling a connected intelligence network across the business value chain from the leadership to the workforce and the customer. For example, one of the leading Spanish multinational telecommunications companies is using Gen AI generated synthetic customer data for analytics.

 

A global bank is using synthetic data sandbox to speed up data intensive POCs with third party vendors. A European financial institution has used synthetic test data to develop a successful mobile banking app. These advancements highlight how Gen AI can drive intelligence across the value chain, transforming business operations and strategic planning.





Figure - 1 : Comparing classical ML and Gen AI products

Comparing classical ML and Gen AI products

Figure - 2 : Comparing classical ML and Gen AI products

Comparing classical ML and Gen AI products

Figure - 3 : ML and Gen AI products

Comparing classical ML and Gen AI products

Figure - 4 : ML and Gen AI products

Comparing classical ML and Gen AI products
AI-ML in manufacturing

The better the question

Comparing classical ML and Gen AI products

Generative AI solutions with multimodal capabilities can eliminate the need for standalone AI applications for each task, helping businesses reduce IT costs and better integrate technology systems.

3

Small Language Models vs. Large Language Models

While LLMs like GPT-4 can automate complex tasks, their broad training on diverse datasets may result in a lack of customization for specific enterprise needs. This generality can lead to gaps in handling industry-specific terminology and nuances. In contrast, SLMs are trained on focused datasets tailored to individual enterprises, minimizing inaccuracies and irrelevant outputs. Fine-tuned SLMs can achieve close language understanding to LLMs, crucial for applications requiring deep contextual comprehension. SLMs also offer advantages in cost-effectiveness, lower latency, and enhanced security. They are easier to manage and more adaptable, making them ideal for real-time applications such as chatbots. Moreover, SLMs can be deployed on-premises or in private cloud environments, reducing data leakage risks.

The future is already here – it’s just not evenly distributed.

William Gibson


Figure - 5 : Comparing classical ML and Gen AI products

Comparing classical ML and Gen AI products

Figure - 6 : Comparing classical ML and Gen AI products

Comparing classical ML and Gen AI products

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Summary

Generative AI (Gen AI) has been at the forefront of technological innovation, revolutionizing various industries, transforming value chains and reimagining business intelligence. AI agents are transforming the nature of work, and a new general computing paradigm is emerging, signifying a fundamental shift in computational processes and capabilities. After years of deep learning advancements, recent breakthroughs in generative AI and foundation models have dramatically expanded what is possible. These models, enhanced by advanced algorithms, vast datasets, and powerful computing, can now generalize beyond their training tasks, impacting both consumers and businesses. This surge in capability has led to a wave of technological optimism. It is being conjectured that AI will become more and more human-like in its ability to interpret and reason, further evolving the way humans interact with machines, and transforming business across the value chain. Sam Altman, the founder, and CEO of OpenAI, said in a statement at the World Economic Forum 2024:

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