A Major shift in how we build Apps with A.I.

Every major evolution of user experience forces developers to rethink how we build apps. From the first days of the Internet, to Social, to Mobile and now A.I. - we are entering the new age of HUI.

Welcome to the age of H.U.I. aka theHuman User Interface.

TL;DR

Every developer has mixed feelings whenever groundbreaking technology enters the market. On one hand, they are excited to tinker with the new toy. On the other, they begin to realize that everything they have built up to that point might need a rewrite or might end up in the garbage.

The entrance of Mobile and Social certainly changed how we built apps, but for a lot of legacy software, you could still get strong benefits by focusing on the UX layer. For example, you didn't have to mess with the logic or database layers of your app; you just needed to make the UI responsive for a mobile experience. Unfortunately, this won't be the case with A.I.

A.I. permeates the entire stack: data, logic, and interface. So, everyone is faced with a big decision: do we rebuild with an A.I.-first mindset, or do we try to slap A.I. on top and hope nobody notices?

You can already see every SAAS company doing the "A.I. shuffle" where they spin up an LLM, chatbot, and some new cool feature that generates text, and they say, “Look at us!”

The reality is that this may buy them a little time while they figure out how to rebuild without spending millions and years doing so, but it is a losing strategy in the long term.

You have to ask the trillion-dollar question:

In 5 years, which is the more likely scenario:

A) You will still be logging into your SAAS (e.g. CRM), filling out forms, and typing in notes and spending more time doing data entry than any productive work.

Or….

B) You will call or text your A.I. Assistant, give it the details, and the A.I. does the rest, including updating the CRM, reminding you of an appointment or even reaching out to your customers directly to fill in the missing pieces.

I think everyone is starting to see evidence that Option B is possible now with generative A.I. (much less AGI).

Want to Dig Deeper?

The paradigm of development is shifting from a traditional SaaS stack to an AI-first architecture, driven by several key reasons:

1. Enhanced User Experience and Personalization: AI and machine learning (ML) models, particularly large language models (LLMs), offer unprecedented levels of personalization and context-aware interactions. This allows businesses to deliver highly tailored experiences that adapt to individual user preferences and behaviors in real-time, significantly improving user satisfaction and engagement.

2. Efficiency and Automation: AI-first architectures enable greater automation of tasks and processes. By integrating LLMs and ML models, businesses can streamline operations, reduce manual workloads, and improve efficiency. This shift reduces the need for extensive human intervention in routine processes, allowing employees to focus on more strategic and creative tasks.

3. Data-Driven Decision Making: AI-first architectures leverage vast amounts of data to provide actionable insights and predictive analytics. This capability allows businesses to make more informed decisions, optimize strategies, and anticipate market trends. The continuous learning and adaptation of AI models ensure that insights are always up-to-date and relevant.

4. Scalability and Flexibility: AI-first architectures are inherently more scalable and flexible compared to traditional SaaS stacks. They can dynamically adjust to varying loads and demands, making them suitable for a wide range of applications and industries. This flexibility allows businesses to rapidly deploy new features and services without extensive reconfiguration.

5. Cost Reduction and Resource Optimization: By automating complex processes and optimizing resource utilization, AI-first architectures can lead to significant cost savings. The ability to process and analyze large datasets efficiently reduces the need for extensive hardware investments and manual data handling, resulting in more cost-effective operations.

Conversational APIs as the Default

Conversational APIs, powered by LLMs, are becoming the default due to their ability to facilitate natural and intuitive interactions between users and systems. These APIs enable seamless integration of conversational capabilities into various applications, making it easier to build and deploy chatbots, virtual assistants, and other conversational interfaces. The rise of conversational APIs is driven by:

- Improved Natural Language Understanding (NLU): Advanced NLU capabilities allow systems to comprehend and respond to user inputs with high accuracy, enhancing the overall user experience.

- Ease of Integration: Conversational APIs offer straightforward integration with existing systems, enabling businesses to add conversational interfaces without significant redevelopment.

- Cross-Platform Compatibility: These APIs support multiple platforms and devices, ensuring consistent user experiences across web, mobile, and other interfaces.

Voice, SMS, and Email Interfaces:

The integration of voice, SMS, and email interfaces is transforming how users interact with systems. These communication channels are gaining prominence due to their convenience and accessibility:

- Voice: Voice interfaces offer hands-free, intuitive interactions, making them ideal for on-the-go use and accessibility purposes. The increasing adoption of voice-activated assistants like Siri, Alexa, and Google Assistant highlights the growing importance of voice interfaces.

- SMS: SMS interfaces provide a simple and ubiquitous communication method that is accessible on virtually all mobile devices. They are particularly useful for quick updates, alerts, and customer service interactions.

- Email: Email remains a critical communication tool for both personal and professional use. Integrating AI capabilities into email interfaces enhances productivity by automating responses, sorting messages, and providing context-aware suggestions.

In summary, the shift to an AI-first architecture, supported by conversational APIs and multi-channel interfaces, is driven by the need for more personalized, efficient, and scalable solutions that enhance user experiences and optimize business operations.