M2P Fintech
Fintech is evolving every day. That's why you need our newsletter! Get the latest fintech news, views, insights, directly to your inbox every fortnight for FREE!
Most developers build apps to solve problems. But what if your app could anticipate them?
That’s the promise of AI-first development. While the idea may seem daunting, getting started can be as straightforward as experimenting with proof of concept, one that challenges conventional software-building approaches.
Let’s explore how this transformative approach works.
A Simple Proof of Concept Changes Everything
A recent development effort has sparked a profound shift in perspective on modern application development. The outcome is a simple banking application serving as proof of Concept (POC) not a production-ready system, but one that operates in a fundamentally different way from traditional applications.
Instead of navigating through complex menus just to check transactions, users can simply ask for what they need. Instead of filling out multiple fields to transfer money, they just express their intent naturally. This app goes beyond merely responding; it autonomously performs tasks, navigates interfaces, updates settings, and formats data exactly as requested.
This level of responsiveness is powered by a compact, 1-billion parameter Large Language Model (LLM) running entirely on the device. No cloud dependency. No latency. Just real-time intelligence embedded within a minimal Flutter app.
The Convergence Driving AI Adoption
Multiple industry forces are reshaping mobile experiences. Breakthroughs in device hardware, rising demand for hyper-personalization, and platform-level AI integration are converging to accelerate the shift toward AI-first development.
Hardware Reality: Mobile device power is exponentially increasing. Running small, efficient AI models locally is quickly becoming standard practice, not a luxury. The computational power that previously required cloud infrastructure now fits in a user’s pocket.
Business Reality: Premium AI inference subscriptions are increasingly common. Users have shown a willingness to pay for intelligent experiences, making AI capabilities an inevitable necessity for competitive applications.
Platform Reality: Major mobile operating systems (OS) are moving toward unified, OS-level AI assistants. Imagine asking the system assistant to "order food from Zomato" or "book a cab to the airport," and the action executes seamlessly by integrating with installed applications.
However, this future relies on one crucial prerequisite: individual applications must expose their capabilities to the OS-level model through standardized interfaces. This POC demonstrates this principle, not as a complete product, but as a glimpse of the inevitable future.
The Paradigm Shift: From Features to Foundation
The industry is at an inflection point. Years of mastering fundamental architectural patterns are reaching saturation:
MVC, MVVM, clean architecture
State management patterns
Responsive UI frameworks
API integration
These are well established, thoroughly documented, and widely understood domains with limited scope for innovation. The next revolutionary wave lies in AI-first development, the new and largely untapped frontier. This is not merely about ‘adding a chatbot’ or ‘AI features.’ It means designing an application from the outset to be intelligent, adaptive, and exposable to external AI systems.
Lessons from Minimal POC
In the minimal banking application, AI is not a secondary feature, it is the primary interface:
Traditional Approach | AI-First Approach (POC) |
User opens app $\to $ Navigates to Transactions $\to$ Filters by date $\to$ Scrolls through list | User: "Show last month’s grocery spending" $\to$ App: Executes query, generates visualization, displays result |
The difference is clear: five manual steps are replaced by one natural language sentence. The real learning, however, is deeper:
Tool Exposure: Most features were exposed as callable tools. The AI can check balances, transfer money, update settings, and navigate screens, basically everything a human can do through the UI, the AI can execute through intent. The pattern for clear capability exposure is established.
Dynamic UI Generation: The interface is not static. A request for data as a ‘colorful timeline’ or a ‘compact list’ and the AI generates the appropriate layout on the fly. Same data, infinite presentations.
Personalized Adaptation: The basic POC demonstrates the ability to adapt information presentation based on context. Scaled up, this means the application literally becomes tailored to individual preferences.
Technical Reality: On-Device Intelligence
The most remarkable technical insight is the execution environment. The POC runs on a 1 billion parameter model (Llama 3.2) locally, and not in the cloud. This provides complete privacy and low latency, achieved within a simple Flutter POC developed over a single weekend.
This capability exists today with
$1$B models (demonstrated locally)
$7$B models (already capable to run on high-end devices)
Specialized models fine-tuned for specific domains
The breakthrough is not in the specific tech stack, but in the possibilities that emerge when development transitions from treating AI as a feature to embracing it as the core foundation. A simple POC is sufficient to reveal this future.
OS-Level Future and App Tooling
A clear prediction based on this experiment is that within 3 to 5 years, OS vendors will establish standard protocols for application-AI integration.
Scenario: OS assistant: ‘Book a cab to the airport’
Application Response: Installed cab apps expose their booking tools to the OS-level AI
Result: The transaction executes seamlessly without the need to open the app
For this orchestration to function, applications must expose their core capabilities as callable tools. The banking app POC demonstrates this by exposing transactions, transfers, settings, and navigation as functions the LLM can invoke. This is not about replacing applications but about creating a unified experience where user intent drives OS-orchestrated execution across multiple applications. Applications that fail to expose intelligent interfaces will feel as dated as non-touch interfaces felt a decade ago.
Why is AI-First No Longer Optional?
Every evolved technology platform eventually reaches saturation. Web, mobile, and backend architecture have largely plateaued in foundational innovation. Implementing traditional patterns is now easy, well-documented, and battle tested.
AI-centric development represents the next untapped frontier, one that’s rich with opportunity rather than hype. Investing effort in this direction is not about following trends but about strategically positioning development teams at the forefront of the next major platform transformation. Just like how ‘mobile-first’ defined a generation of digital innovation in 2010, ‘AI-first' will be the defining standard of 2025.
The goal is a user experience revolution:
Respecting Time: One natural request replaces multiple taps
Reducing Cognitive Load: Users state what they want, not how to achieve it
Enabling Accessibility: Voice and text interfaces support any language or ability level
Personalizing Experience: Interfaces dynamically adapt to individual preferences
It's about creating software that feels less like a tool and more like a helpful, intuitive colleague. Even a minimal POC confirms this immediate difference.
Behind the Scenes: The Core Mechanism
The simple POC operates through three core concepts:
Natural Language Understanding: The AI comprehends the user's intent
Tool Integration: The AI can perform actions (it doesn't just talk about them)
Dynamic UI Generation: Interfaces are created based on context and user preferences
All this is achieved with a small Llama 3.2 1B model running locally. If such a tiny model can demonstrate this potential in a weekend POC, the possibilities with larger, production-grade models are genuinely transformative.
Start Now: Minimal Requirements
Exploring this frontier does not require massive datasets or cutting-edge hardware. It requires:
A clear mental model of AI-first architecture
Familiarity with tool-calling frameworks (e.g., those like MCP)
A willingness to experiment with local LLMs
A focus on user intent over predefined UI navigation
The most important step is simply to start.
Build a POC.
Because the barrier to entry has never been lower: local models are free, frameworks are maturing, and the development community is collaborative.
Final Thoughts
Every transformative platform shifts initially seemed like ‘overkill’
Why do we need apps? Mobile web works fine’
Why touch screens? Keyboards work
Why voice assistants? We have to search
AI-first development is rapidly becoming the foundation on which the next generation of financial applications will be built. By adopting this approach early, development teams not only enhance user experiences but also drive competitive advantages and sustainable innovation.
The future belongs to those who lead the AI transformation rather than follow it. Taking this proactive stance will define the banking landscape for years to come.
This thought leader article was authored by Srinivasan R, Senior Development Engineer, M2P.
Follow us on LinkedIn and Twitter for insightful fintech bytes curated for curious minds like you.
