Harnessing the Power of Generative AI: Insights from Manoj Madhusudanan

In the rapidly evolving world of technology, generative AI is emerging as a game-changer with the potential to revolutionize various sectors. Manoj Madhusudanan, Senior Vice President and Head of India and APAC for analytics at EXL, recently shared his insights on this transformative technology at MachineCon India 2023, held on 23rd June in Bangalore.

The Economic Impact of Generative AI

Madhusudanan began his talk by referencing a recent report by McKinsey, which highlighted the staggering economic value that generative AI could bring. The report estimated that the annual economic value of generative AI use cases could range from 2.6 to 4.4 trillion US dollars. To put this in perspective, the GDP of the UK is 3.1 trillion US dollars. The potential of generative AI is immense, and it’s not a question of whether it will change the world, but how.

The Potential and Risks of Generative AI

Generative AI has the potential to revolutionize a wide range of sectors, from digital content in the metaverse and NFT space to song and music production. It can even be used in drug discovery and the creation of new materials. The technology could fundamentally alter the nature of creativity and productivity, leading to significant changes in industries such as online education and game development.

However, Madhusudanan also highlighted the risks associated with generative AI. If used irresponsibly, it could lead to impersonation, malicious hacking, and the creation of fake social media photos. Therefore, organizations need to approach this technology with caution and a strong focus on ethical considerations.

The Business Case for Generative AI

Madhusudanan cited a report by IDC, which revealed that 70% of enterprise intelligence service providers are actively considering or working on generative AI use cases. While chat GPT is popular at an individual level, the biggest use case that companies are focusing on is not conversational application. Instead, they are prioritizing code generation, knowledge management, product design, and engineering.

When looking for organizations to help them with generative AI, companies prioritize business outcomes. They want to see a proven track record of delivering results. Data management is also a key consideration, as the quality of the data used to train AI models can significantly impact their effectiveness.

Choosing and Evaluating Generative AI Use Cases

With the vast potential of generative AI, companies may be overwhelmed by the multitude of possible use cases. However, it’s crucial to prioritize and choose the most suitable applications. Generative AI is expensive, and it’s not feasible to implement all use cases simultaneously.

Madhusudanan suggested a useful approach is to start with internal applications, where the cost of error is low. For instance, code generation is an interesting application where English commands can be given, and the system will write the code. This can significantly increase the productivity of software engineers within organizations.

When evaluating potential generative AI use cases, companies should consider several factors. These include the amount of unstructured data involved, the potential for significant cost or revenue impact, and the risk of non-compliance with regulatory requirements. By considering these factors, companies can prioritize their generative AI initiatives and ensure they are investing their resources wisely.

Implementing Generative AI

Implementing generative AI requires a structured approach. Companies need to consider their infrastructure and cloud strategy, the development of their AI models, and the integration of their generative AI applications with their business applications. It’s also crucial to have a human in the loop to ensure the system improves over time.

Before embarking on a generative AI journey, companies need to have a solid data strategy inplace, Madhusudanan emphasized. The quality of the data used to train AI models is crucial, and without a well-structured data management system, the investment in generative AI could be wasted.

Generative AI in Action: Transforming Customer Service

Madhusudanan provided a practical example of how generative AI can be applied in customer service. Traditionally, customer service calls can be frustrating for both customers and agents. Customers often have to repeat information multiple times, and agents may struggle to retrieve information from various systems quickly.

Generative AI can transform this process by acting as a coach for customer service agents. For instance, during a call, the AI can provide real-time guidance to the agent, helping them to respond more effectively to the customer’s needs. This can significantly reduce the call handling time, improve the customer experience, and increase the competency of the agents.

In a recent implementation for an insurance provider, the use of generative AI led to a 3x improvement in execution and a 30-40% reduction in the overall call time by the agents. This is just one example of how generative AI can deliver tangible business benefits.

The Future of Generative AI

Madhusudanan concluded his talk by emphasizing that generative AI is just at the beginning of its journey. As the technology continues to evolve, it will open up even more possibilities for businesses. However, it’s essential for companies to approach this technology with a strategic mindset, focusing on the most valuable use cases and ensuring they have the right data and infrastructure in place.

In conclusion, Madhusudanan believes that generative AI is having its “iPhone moment.” Just as the introduction of the smartphone revolutionized the way we communicate, generative AI has the potential to transform the way we work and do business. The key to harnessing this potential is to approach it with a clear strategy, a focus on business outcomes, and a commitment to ethical and responsible use.