Enterprises need data, tools, and systems for AI success: C5i CEO Ashwin Mittal

C5i, formerly Course5 Intelligence, has shifted from traditional data strategies to an artificial intelligence (AI)-first approach. In a recent TechCircle interview, CEO Ashwin Mittal discussed the company's rebranding, its focus on advanced technologies, and the impact of acquiring Analytic Edge, enhancing their expertise in marketing analytics and AI. C5i plans to expand into HR, finance, and risk management.

How is your company leveraging technology, and how has your recent rebranding shifted your focus and offerings?

We leverage data—both internal and external—and apply engineering, data science, and management expertise to help companies optimise decisions, improving sales, profitability, costs, and customer experience. Our rebranding reflects our evolution from a data-driven firm using AI where applicable to an AI-first company using data as a crucial element. This transition is more than a simple change in services; it signifies a major shift in our core approach and strategic direction, positioning us as an AI-driven entity.

How has acquiring Analytic Edge impacted your business?

We recently acquired Analytic Edge, which enhances our ability to optimize marketing spend and improve decision-making across various functions, including sales, marketing, operations, finance, and HR. Analytic Edge's expertise complements our existing strengths in customer analytics, AI, digital strategies, and omnichannel solutions, allowing us to scale our offerings further. The acquisition is a natural fit, with strong cultural alignment between our organisations.

How has your journey with Generative AI been so far?

Our investment in Generative AI began years ago, focusing on natural language generation before the term became widely known. Today, our platform, Discovery, functions like a chat GPT for businesses, analyzing internal data to answer operational questions and suggest actions. This technology continues to evolve, offering sophisticated solutions to our clients.

Do you think there's a data scarcity issue in India, especially with the rise of generative AI?

Our approach to discovery was initially different because it focused solely on internal organizational data assets. At that time, users could only ask questions based on the data their organization had captured, not on external information. Today, however, we've started integrating external data assets using advanced technologies developed by big tech companies.

Regarding the scarcity of data, it’s true that in developed countries, technology has led to the recording of almost everything, resulting in rich and reliable data. In contrast, in a large country like ours, where technology hasn't yet permeated all areas, not everything is being recorded. This is true across various sectors, such as commerce, customer service, retail, and supply chain management. While there is still a significant amount of data available, it's not as abundant as we would like, so we need to be cautious when applying these technologies.

This caution applies not just in India but globally, especially when using large language models (LLMs) in business contexts. Issues such as accuracy, relevance, IP infringement, social acceptability, and data security must be considered carefully. In India, for instance, some use cases will have sufficient data, particularly in more developed segments where information is more thoroughly recorded. However, for questions related to rural areas or less commonly discussed topics, LLMs may not be as effective.

Therefore, identifying the right use cases is crucial. If you can generate the necessary data set for your specific application, you can train these systems and use third-party technologies with an adapter to achieve better results. But it's essential to verify these factors before getting started.

How do you see AI's role evolving in enterprise technology, especially in data analytics and decision-making?

Honestly, we've only just begun to explore the potential here. Over the last 30 years, the biggest driver of productivity in the business world has been information technology IT products, platforms, and services that corporations have adopted to make their business processes more efficient.

Now, with the digital transformation that's taken place, we have vast amounts of data and advanced AI technology. We also have systems within organizations that allow us to deploy AI models effectively. For AI models to succeed, three things are essential: available data, the right scientific tools, and systems to deploy these models so that the right people get the right insights at the right time, whether it's a CEO making monthly decisions or someone on the factory floor making hourly ones.

With mature technology, abundant data, and effective deployment systems, we've seen a boom in this area over the past couple of years, but we're only scratching the surface. I believe that in the next 10 years, this will be the single greatest driver of value across the corporate world.

What are your long-term growth strategies, and how do you plan to achieve them?

We continue to invest in organic growth, focusing on sales, capability development, and AI innovation. We're also strategically acquiring assets like Analytic Edge to support growth, with plans for another acquisition within the next six to eight months. We're expanding our focus into manufacturing and financial services, sectors with significant growth potential.

Are there other technologies besides generative AI that you're planning to invest in?

We're expanding our portfolio to include more enterprise-class solutions in HR, finance, and risk management, alongside our existing focus areas of sales, marketing, consumer, digital, and supply chain. This expansion will make our offerings more comprehensive and user-friendly