Ashwin Kumar is a Partner at MathCo, who leads the Solutioning and Growth teams. Responsible for driving growth for the organization, he and his team are at the forefront of client conversations. They identify global Fortune 500 enterprises and understand their needs and problems to be solved, enabling MathCo to offer the best-suited solution, tailored to the clients’ unique requirements.
In this interview, Ashwin talks about trends in business intelligence and MathCo’s outlook for 2024. The following is the edited text from the original LinkedIn Live interview hosted in January 2024.
If you had to summarize MathCo’s outlook for this year in one word, what would that be?
Ashwin: These one-word summarizations are always tricky. But what I would choose for this case is self-sufficiency.
Let me expand on that. So, if you look at the last 18 to 24 months, the level and the severity of disruptions that enterprises have faced is truly phenomenal. The disruptions have come from multiple factors. One from technology; technologies like Generative AI have changed the landscape. There have been disruptions from consumers’ buying patterns. There have been disruptions around geopolitical events.
These kinds of disruptions have made enterprises realize that they cannot be dependent on solutions that drive their key decisions that are not controlled or owned by them. So, these solutions then quickly become redundant; their shelf life becomes shorter.
And so, enterprises who did not have these capabilities were not able to thrive in this disruptive environment. And this is one of the reasons why as part of our recent rebranding strategy that we did at MathCo, we wanted to reemphasize our commitment to enterprises around this. In fact, our tagline is own your intelligence, which truly synthesizes what we want to bring to enterprises and what enterprises should be focused on as well.
If you had to pick a few trends or predictions that you have for this year—in terms of how the Data Analytics and Enterprise AI industry would change or what large global enterprises would be looking at this year, what would they be?
Ashwin: There are quite a few trends that we have observed in 2023. There are indicators of these trends continuing into the new year as well. I can share a few top ones today.
The first one is the need for enterprises to create tangible impact on the investments that they make. This is very important in a disruptive environment where the competition is very high. So the need for enterprises to realize the impact as quick as possible, as sure as possible, is going to be very important. And that would drive different decisions around investment prioritizations, etc.
The second trend is around incorporating technology and innovation into the way enterprises operate. One example—obviously in the last year, and what would continue into several years forward—is around Generative AI. It’s not just about developing a proof of concept or an experiment in one corner of the organization that becomes a theoretical or innovative exercise, but how can these technologies be integrated into the code action plan decision-making process as well. That is going to be really important.
The third aspect is going to be around personalization. As I mentioned, customers—the way they are purchasing, and the way they are making their decisions has completely transformed. And enterprises today have more insights about customers’ behavior, and about the other data that is being captured. And it is especially important to translate that data into a truly personalized experience for customers. So, a lot of initiatives would be around this trend.
And lastly, as I mentioned, enterprises are going to make a lot of investments to build their internal capabilities. The conventional models of a license-based set up to outsource their capabilities or be dependent on other partners for their core analytical needs is something that is going to be disrupted further during the coming year.
In 2023, there was a lot of hype around Generative AI, and towards the end of the year we were talking about how businesses can actually start making use of this and in what ways. What do you think would be the cutting-edge Generative AI trends that you would see in this year?
Ashwin: I think the primary trend, when it comes to Generative AI is that there are going to be multiple versions, multiple enhancements of the core underlying large language models, which is going to be particularly important for enterprises to adopt into. For example, ChatGPT 5 release is around the corner; it’s being trained on trillions of parameters and the next version would be bigger. Adopting these newer models is going to be very crucial. They will offer new features, intelligent automations, being able to even create automated images or videos. So how can enterprises—and MathCo—take advantage of this is going to be very crucial.
Secondly, if you look at the potential of Generative AI today, it could be split into two categories. One category is: use-cases or problems that may not have been solved at all without Generative AI. For example, capabilities around reading through hundreds of thousands of documents and being able to infer the context is something that could be possible only through Generative AI. The older technologies around NLP may not be sufficient to solve the problem. So, there’s going to be an increased focus on use cases that can be solved only through Generative AI.
The second category of the potential is related to all the conventional solutions and decisions that are being made in enterprises. These decisions have been made for several years. For example, if you look at retailers—they have made decisions on forecasting their demand and managing their supply chain all along. But now, if you can integrate the power of Generative AI into these conventional solutions, it could be making them more dynamic; it could help with presenting an automated insights for the stakeholders; it could be giving them a heat map on where they should focus on, et cetera. Generative AI becoming a co-pilot in these conventional use cases is also an area that is going to really explode in the coming years. Because that is going to truly change the way enterprises operate, and the way they go about making their decisions.
What would we be betting big on this year?
Ashwin: Keeping these trends, the disruptions, and the opportunities that are in play, our longer term vision and investment is always going to be: developing multiple intelligent solutions within an enterprise that could solve a variety of problems, across multiple functions. And not just solving those specific problems, but can they be integrated with each other? Can they talk to each other? In the sense that the outputs of one could be fed into—as an input—for another solution. So, building these connected systems of applications is where we are going to place the bet on.
Now, to make this possible, it’s not just about one particular factor. You will need to bring in Generative AI here, because that would make the solutions more intelligent. We also need to bring any efficiency, which we do through our NucliOS platform, which has also been integrated with Gen AI.
So, these combinations of decisions that we make, to build this connected system of applications is where we are going to be focusing on as a key investment and as a strategy, not just for 2024, but also beyond.
Could you explain what NucliOS is and what it does?
Ashwin: Absolutely. I think, for any good solution to be built, you need to components. You need the expertise of the industry, of the function, to be brought in. And second, you need the technology—the algorithms, the pipelines, etc.—to be combined with the expertise for the solution to be truly beneficial; to be truly useful and effective. Now the expertise, (generally) in the industry, comes in the form of SMEs and other people-related concepts (which we also continue to invest on). But as a scalable model, that may not be the only strategy that works. So that’s where NucliOS comes in.
NucliOS is our playground for building intelligent best-in-class applications. What it has been enabled with are a series of accelerators—thousands of them—that have been mapped to specific industries, functions, and the problems they solve. These accelerators could be a piece of code; they could be KPI definitions; they could be frontend templates on how the final visualizations could happen. And in the reason versions of NucliOS, we have an enabled Generative AI across all the modules: whether it is for data management, whether it is for algorithm development, anomaly detections, insights, summarizations, etc.
So, when we start building the applications, you do not have to reinvent something that has already been done, nor do you have to depend on few individuals for the expertise. These have been encoded into NucliOS and its core tenets. And this offering is what we use to build solutions faster and also without compromising quality, as we pursue efficiency and best-in-class performance.
What advice would you have for freshers or early career professionals who are joining the space?
Ashwin: I think there has never been a more exciting time to join this industry. When several of us started in the space, the focus was always on becoming an expert in a particular technology or in a set of technologies. That is not going to be the way forward, as we have been talking about.
Uh, so anybody who is coming into the industry should obviously learn certain technologies, but they should also understand how to embrace newer technologies. What really happens when a new technology comes in? And how can they learn the art of going through the change and becoming very adept and comfortable with it? That would be the one advice I would offer to anybody who is coming into the industry for them to be successful and handle the disruptions well.