Meeting the challenges of enterprise development [Q&A]

The rate of change in both technology and economic conditions can make it hard for CIOs to both innovate and satisfy the needs of the business.

We spoke to Ioan Iacob, founder and CEO of financial application specialist FlowX, to discuss the challenges of developing apps in the enterprise and how they can be addressed.

BN: What are the biggest hurdles enterprises go through when building applications?

II: The people developing technology in the enterprise world have an incredibly hard life today, especially in banking. I bring up banking because that's a sector we're especially familiar with and focused on. Many banks are not even aware of how difficult it is because they haven't seen an alternative way to do things that actually works.

At the last company we founded, we worked with large organizations, mainly banks, helping them build digital platforms, so we got to see this firsthand. We saw the enormity of the budgets and the lengthy time to market required. At the end of the day, the outputs were not that remarkable. This is not because the engineers were not brilliant -- they are -- but because of the complexity of the systems they had to work with, the massive amounts of data, and the stringent compliance and regulatory requirements unique to the industry. For someone who does not have exposure to the industry, it’s hard to wrap their head around the necessary scalability, security, robustness, and compliance that has to be built in.

If we look at the top 10 largest banks in the world, this complexity is costing them over $100 billion a year in IT costs and growing. Expanding that view to the top 1,000 banks, we are looking at costs approaching a trillion dollars. It's an issue they know they need to solve, but the cost is growing because they are constantly adopting new technologies that promise to be a magic bullet for this technical debt they are building. However, these technologies do not deliver on their promise and instead end up adding to it. They find themselves with Frankenstein tech stacks that are not only expensive but also unstable and difficult to maintain, all in the name of solving that very problem.

They deserve better. They deserve to be on the cutting edge of technological innovation, both from a tech perspective and from an ROI perspective on that tech investment, and AI is going to be the answer. AI needs to allow them to build complex systems simply and eliminate these cobbled-together systems that don't play nicely with each other. From what we have seen, AI is up to the task.

BN: Why is low code / no code not a good option for enterprises looking to increase developer productivity?

II: Building scalable, complex applications with Low Code/No Code (LCNC) alone is like building skyscrapers with wood and plaster. Don't get me wrong, LCNC tools are a great shortcut for small-sized apps, but they do not provide the 'steel and concrete' needed to sustain the structural framework for scalable, complex applications. They require a strong architecture to achieve this, and that's something they don't have yet. These tools would need a lot of manual hacking to fit with the custom code across the system, which is neither operationally sustainable nor scalable.

BN: Is a hybrid approach recommended?

II: Both No Code and Low Code are helpful concepts, but they do not stand tall in the enterprise ecosystem. Enterprises have sophisticated requirements that need the full power and control of programming languages. This is why we believe it is essential to bring AI, no-code, and full-code together for the first proper application of AI to banking modernization. This approach accelerates any coding language and addresses enterprise complexity.

We believe engineers and business analysts are essential and will continue to be so in banks and financial institutions. However, it's a pity that their skills are wasted on redundant, mindless work with low ROI, which they dislike. What we do with the help of FlowX AI is free smart people from having to do dumb work.

BN: Automated code generation is a trend these days? What are the challenges there?

II: Code generation is the software equivalent of optimizing bricklaying in construction. But if you think of software engineering in terms of civil engineering, a bank would have the same complexity as an entire city. So, while automated code generation is a good start, it's just optimizing the bricklaying in building an entire city. You're still left with the infrastructure, the roads and bridges, the pipelines, the access to services and experiences, the safety, and so forth.

The great thing about AI is that we can apply it to higher-level layers. For example, you can use AI to optimize the architecture of the system, the process flow, or the user experience. We believe in using AI to improve how every level of the system operates, ensuring that nothing is left behind or becomes too burdensome to update and transform.

BN: Are there any concerns around automated code generation one day replacing engineers?

II: Writing code is one thing; proper engineering is a very different thing. Anybody can write code, but not everyone can be an engineer. Engineering involves applying systemic thinking in context: How are people going to use what I'm building? How could it break? What requirements does it need to satisfy, beyond the algorithmic or functional requirements? It's about integrating information that might not exist in a formalized way.

Returning to the civil engineering analogy, building an enterprise piece of software requires the same engineering discipline needed to build a bridge. The equivalent of writing code is just laying the bricks. Engineering takes into account site preparation, foundation work, substructure and superstructure construction, safety measures, roadway or surface finishings, and environmental considerations. Every build has a massive context and many implied requirements. AI alone cannot deliver on that level of complexity today -- but it can be an accelerator. At FlowX.AI, we envision a future where engineers are empowered by AI to build extremely complex systems elegantly, quickly, and with ease.

BN: What does FlowX AI do and how does it address these challenges?

II: FlowX AI is the first scalable application of AI for banking modernization that is safe for banks and financial institutions to use. It represents over a decade of highly specialized investment. By bringing AI, no-code, and full-code together, FlowX AI enables the faster development of better banking software.

FlowX AI can integrate any type of existing technology system -- from mainframe to APIs -- into a consolidated data model. This forms the first major layer of simplification. On top of this data model, banks gain a modern orchestration and application development environment that makes it easy to build robust, modern, business-critical applications.

As a result, our customers in banking can quickly go to market with modern platforms for lending, mortgages, investment management, budgeting, financial planning, and more. They can also transition into a new paradigm where adding new features or making regulatory changes takes weeks rather than years. All of this is now possible with an overall maintenance effort reduced to less than one percent of what it would be with any alternatives.

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