CODELESS – The Future of No-Code AI brought together leaders in the no-code and finance industries to discuss how no-code AI are changing the way finance professionals work, conduct research, and make investment decisions.

In a panel moderated by Accel’s Ben Quazzo, hear from Alex Schmelkin (CMO & Global Head of Partnerships, unqork), Juergen Weber (Head of Business and Operational Transformation, Allianz), Ben Taylor (Chief AI Evangelist, DataRobot), and Kumesh Aroomoogan (CEO and Co-Founder, Accern) on how no-code is accelerating digital transformation especially within the financial service industry and how organizations can prepare for and implement these changes.


Ben Quazzo: From my perspective, no-code has been around for decades. One could argue that in the 80s, Microsoft Excel pioneered the beginnings of no-code and that in the 90s, Dreamweaver and then WordPress pushed web app development to some degree. So why are we still talking about no-code now? 

Kumesh Aroomoogan: One thing that we’re seeing right now in the space is that every organization needs to stay competitive. And to do that, they need to build a lot of these solutions internally that their business teams are requesting. One of the biggest issues we’re seeing within large organizations is the shortage of talent that can actually build AI use cases to automate processes, automate operational efficiency, and help make better decisions at the end of the day.

And if they don’t have these sophisticated data science and technical skillsets, they’re going to be lagging in innovation. And so with the rise of no-code, it’ll basically empower a lot of the business users to take ownership in terms of all their use cases that they want to build to really enhance their day-to-day operations and workflow.

Ben Quazzo: Ben, I’m going to turn it over to you. Talk us through how Kumesh’s answer relates to AI specifically.

Ben Taylor: Well, I think he’s completely right that we have a massive talent shortage, especially when it comes to experienced talent. Because that’s really what you want in the industry. You want people that have made mistakes. They’re not going to be bright-eyed, bushy-tailed and optimistic. You want people to understand the reality of what it takes to build something and deploy something useful.

I like the kind of the Web analogy that you lead with. That look, we’ve got notecard developments that have already been happening in the Web world. You’ve got the Twitter clone. You’ve got WordPress. Why would you argue to build everything from scratch when you can do things in WordPress very quickly? And I think that’s completely true when you think of AI technology.

Why would you build something from scratch if you can get it in a few hours or a few minutes? What’s the justification? There are some people that will still fight to build it from scratch. And I think that’s funny.

Ben Quazzo: Juergen, as someone on the consumer side, what has enterprise AI and no-code AI adoption been like within the asset management space? Does it feel like a true moment of inflection for this category? 

Juergen Weber: I would say we are on a journey, right, and we haven’t reached the end yet. If you look at it from a transformation perspective, I think you need to understand that many large companies in the asset management area who have legacy are not fully on the cloud and have not fully harmonized data sets. There are many people who have invented something and want to keep that. So you need to watch infrastructure and company emotions. And I think the key challenge is, how do you overcome these hurdles? Our learnings have been very clear here in terms of building a very positive environment of people who are intrinsically motivated on those platforms dealing with this stuff, learning from it.

I think the second key point is education. You need to educate the entire organization and don’t just start with highly enthused colleagues but also top management and middle management.

Ben Quazzo: Alex, I’d love to have you weigh in here—whose job is it to educate the user? 

Alex Schmelkin: I’d actually love for it to be the IT teams themselves. We actually don’t think there’s a shortage of talent, we just think people have the wrong tools. A developer doing things the old-fashioned way, writing code, they only have a certain amount of throughput, even a very experienced developer who has been doing this for decades. And when they pass their code on to the next person, they’re going to start from scratch because code inherently is difficult for people to maintain.

We think the way to evangelize the best is actually get IT involved very quickly in a number of these use cases that Juergen was talking about. Have them immediately see the benefits, and realize that they’re more productive. Oh, and by the way, you don’t have to worry about your business users just running off in Excel anymore because we’ve provided great AI tools, and great software-building tools in order for them to participate in the software development process. Training is key. They’ve got to be able to get into the platform quickly and start building and opening it up to the large organizations where we’ve seen success.

Ben Quazzo: And in response to Alex’s view, what challenges do you see in implementing these changes at scale? Whose responsibility is it to work through these challenges?

Ben Taylor: What we see is some organizations will have a lot of siloed AI efforts and they’ll fail. And so what do they do? They hire a business leader to come in. And I think for someone that truly understands the business, they understand time to value and the urgency to build it yourself is gone. And so this conflict exists. Sometimes you have technophiles coming out of academia who like to build things themselves. They like to learn. And sometimes that is not aligned with what the business needs. Because the business has to justify its existence, right? And so a laser focus on value is what we see more on the business leaders side. So they should be educated because I think they would appreciate it.

Ben Quazzo: Looking at the latest McKinsey report, we’ll see that data scientists and devs and engineering is scaling linearly while software development is scaling exponentially. And so there’s going to ultimately be a disconnect. Does low-code, no-code, or some kind of these—I hate using this term—but ‘citizen developer’ type tools help bridge that gap?

Juergen Weber: I would say so. What we have learned is if you want to use AI or no-code AI, it needs to be really integrated horizontally across the firm. I think it starts with the investment management function. But you need to have IT on board, and you need to be pretty careful with that, because you get IT folks who deal a lot with legacy, who are still proud of the old tools, as Ben rightfully said. So getting it from the business side and molding IT makes sense. We would also love to have more data scientists. The big question is always: where should they sit? And they definitely should not sit in a silo. They should have a super strong, close proximity to the business. In our case, to the portfolio managers. And I think then using the IT tools. For example, thinking about using Kaggle as a platform to scale up thinking about no-code AI tools, I think helps in overcoming it. But I would strongly prefer to see a proximity to portfolio management as opposed to IT.

Ben Quazzo: What has been the pathway to get buy-in on the customer side? Who are the right stakeholders to help get to that end scenario Juergen just mentioned of kind of horizontal end-to-end deployment?

Kumesh Aroomoogan: Our approach is a unique one. We went vertical and focused on the top financial service use cases these enterprises need. The business owners are the ones that we sell to and get buy-in from. And from there, we work in parallel with the IT team to make sure that things can actually be integrated and scalable within the organization itself.

So our approach is basically looking at: what is the core business problem within each of the business functions? How can we bring AI to solve those business problems? And then how can we work with the technology team to really scale out the solution enterprise wide?

One very important component that we’ve seen with a lot of customers is customizability. So every time an AI use case is being deployed and a business user is now getting value out of that, we want to make it easier for customers to actually personalize this use case with their own domain of  expertise. They shouldn’t have to go to IT for each of these little requests—they should be able to personalize it themselves. And with that, it creates a network effect for the entire organization, for the software vendors themselves, if every single business user is able to create personalized versions of the software and models themselves.


The discussion around the future role of no-code AI in the financial service industry continues to evolve as companies seek out more accurate and accessible data to enhance business operations and decisions.

It is critical that AI adopters understand the capabilities of these tools to unlock digital transformation within their workforce. As Accern Co-Founder and CTO Anshul Pandey reminded us at the end of the event:

With more AI education in the space, there has been a shift from point solutions to AI platforms. And that is driven by the underlying desire to have more control on the AI workflows that enterprises are deploying in-house. So we are seeing this shift toward democratization within enterprises where people no longer want the data science teams to do all the skunk work on data ingestion and cleaning and storage and so on, but rather focus on what’s most important.”

Listen to the full discussion and learn how no-code AI is positively disrupting the financial services industry here.

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