In the rapidly evolving landscape of artificial intelligence, few voices resonate as powerfully as that of Shvetal Desai, co-founder of Nividous. Recently featured as one of the key speakers at the well-organized World AI Show in Mumbai, hosted by Trescon, Shvetal brings a wealth of knowledge and experience in AI automation. Business Review Live had the distinct privilege of engaging in a thought-provoking conversation with him, exploring not only the innovative strides Nividous is making but also delving into the deeper implications of AI technology in today’s business world. Join us as we uncover Shvetal’s insights on harnessing automation to drive efficiency and transform industries, shedding light on the future of AI.
As a co-founder of Nividous, how do you see the role of intelligent automation evolving over the next 5 years, especially with the growing integration of AI and machine learning in traditional business processes?
It’s insightful that you mentioned traditional business processes because processes are constantly changing. What we consider “traditional” today may evolve within a year, driven by businesses’ desire to stay competitive. Companies continuously enhance their processes to keep up, and automation plays a critical role here.
When companies automate their processes, they achieve automation across the entire enterprise. Embracing intelligent automation across an organization will be essential. This area is receiving significant attention and investment, as many companies now recognize that without intelligent automation, they risk falling behind their competitors and losing relevance.
Nividous focus is on hyper-automation. Can you share a specific use case where hyper-automation transformed an industry or a company, and what critical lessons were learned from the implementation?
In fact, we have several use cases. One relevant example is a large-scale transformation for India’s most significant automotive company. This company successfully scaled automation across its operations, including a nationwide rollout involving 5,000 dealerships. Through this initiative, loyal customers could receive cashback bonuses when they traded in an old vehicle for a new one. Previously, this process was manual and time-consuming; documentation had to be submitted to the back-end team, who verified each claim manually. This approach often took weeks or even months to complete.
Our solution introduced efficiency by allowing dealers to upload information directly through a portal. This move enabled extended enterprise involvement, where dealerships could now validate customer data upfront, eliminating manual checks by the company’s internal team. In addition to streamlining in-house processes, we automated data extraction with AI, even for older documents, enhancing speed and accuracy.
Robotic Process Automation (RPA) bots played a key role in accessing various sites and databases to verify insurance and identity information. They conducted intelligent “fuzzy matching” to handle variations in customer data across documents—such as different name formats on IDs. Finally, a minimal human review confirmed the data, significantly reducing the time and resources needed.
The success of this transformation for one division led to further adoption across other divisions, including rural, institutional, and commercial areas. This use case highlights how automating a single process can lay the groundwork for enterprise-wide automation on a unified platform.
AI and automation often raise concerns about workplace displacement. How does Nividous help companies balance automation while fostering a collaborative environment that upskills employees?
In manufacturing, especially, change management is often challenging. When employees hear about new automation initiatives, they may worry about job security, fearing that their roles could become obsolete.
To address these concerns, managers and those introducing the technology must clearly communicate the growth vision. It’s essential to convey that these changes aim to upskill employees, enabling them to take on more fulfilling work. This message highlights that automation isn’t about job cuts but about creating opportunities for employees to leverage their skills in more impactful ways.
Setting the stage with these positive messages can lead to cooperation rather than resistance. For expanding companies, automation and technology are crucial for handling increased demand and customer volume. In such cases, automation allows the business to grow while maintaining the same team size, which can reduce costs without risking job losses.
When employees understand this strategy, they recognize that automation supports the company’s growth—and their role within it. This shared understanding strengthens their connection to the company’s goals, fostering a sense of inclusion in the company’s forward movement.
Nividous emphasizes the holistic approach to process automation. Could you elaborate on the role of AI in not just automating repetitive tasks but also enhancing overall business decision-making and strategy?
Nividous offers a platform with RPA (Robotic Process Automation) bots that go beyond simply automating repetitive tasks; it actively drives better decision-making and strategic outcomes for businesses. Traditionally, RPA has been seen as a “band-aid” solution—a way to move data between systems without much critical impact. If an error occurs, it can easily be corrected in the backend.
However, Nividous engages in RPA applications that focus on the front-end, revenue-generating activities. Here, bots perform essential tasks that directly impact business success. These bots are further enhanced with artificial intelligence to increase their efficiency and reliability.
With AI, these bots function more like humans, adding cognitive abilities that enable deeper understanding rather than just basic data transfer. When combined with human oversight, the process becomes highly robust and error-free. This comprehensive approach lowers operational costs, reduces SLA (Service Level Agreement) times, and delivers consistent accuracy in performance.
With AI solutions being customized for different industries, how does Nividous ensure its platform remains adaptable and scalable across sectors with diverse needs, from finance to manufacturing?
That’s an excellent question. Today, many people view AI as a single tool that can handle everything. For instance, ChatGPT and OpenAI are widely recognized. However, these tools are trained on vast amounts of information from around the world, which may not be specific to your organization.
When you ask a question, these models relate their responses to the general knowledge they possess. Currently, many enterprises need tailored solutions. For example, consider your organization’s policy documents. When you inquire about them, you want a co-pilot who can analyze these specific documents and provide relevant information.
Unfortunately, a generic OpenAI model cannot fulfill this need. Instead, you require a custom-built model designed for your specific requirements. Whether you have sufficient in-house data to create this model or opt for generative AI, you can develop a solution that works for you. With proper prompt engineering, you can build effective solutions internally. Ultimately, every industry will need specialized models.
This is where native large language models (LLMs) demonstrate their actual value. We have developed our own LLM, customized for particular use cases that align closely with our customers’ needs.
As AI governance and ethics become increasingly crucial nowadays, how does Nividous ensure responsible AI practices are embedded within its hyper-automation solutions, especially regarding data, privacy, and decision transparency?
This focus is essential because personally identifiable information (PII) is under significant scrutiny from governments. While this has been a long-standing issue in other countries, it is gaining traction here as well. Relying on third-party services like OpenAI for public cloud solutions poses a risk because it exposes sensitive data externally.
Regardless of existing contracts, sending data outside your organization always presents a risk. Therefore, adopting native private cloud-based or on-premises large language models is crucial for all businesses. Customized deployments are now essential.
For instance, we recently automated an interactive voice response (IVR) system for a major company in North America. This company had agents who made payments to insurers on behalf of customers. Agents navigated complex IVR systems, often going through each insurer’s process to produce a credit card and complete payments for customers.
We streamlined this process by implementing a robotic process automation (RPA) bot on a private cloud. As a result, all data remained secure, and no PII left the system.
Achieving over 80% accuracy in straight-through processing was a significant accomplishment. This automation significantly transformed the enterprise, fundamentally changing how it conducted its operations. This use case demonstrates how automation can revolutionize business practices and enhance efficiency.
