AI Beyond Chatbots and Assistants: Adaptive Applications

AI has amazed the world with natural language chatbots and coding assistants. But as the hype peaks while legal and regulatory issues remain unclear, decision makers are beginning to ask: Is that all there is? How can AI provide deeper value in applications? The full answer is still unknown, but perhaps the future lies in adaptive applications.

The Business Benefits of AI

AI can bring significant benefits to businesses through increased efficiency and enhanced customer service.

AI-powered code assistance tools like GitHub Copilot, Cody, and Capella iQ streamline the development process, enabling faster and more accurate code generation and interpretation to reduce development time and costs. These tools identify and fix errors, help new team members get to grips with the code base faster, assist with testing, offer recommendations, and more to improve the overall quality of your software.

In customer service, AI chatbots provide instant 24/7 support, handling routine inquiries and resolving issues quickly, which increases customer satisfaction with faster responses and allows human agents to focus on more complex tasks, further increasing operational efficiency. By integrating AI, businesses can significantly improve productivity and deliver superior customer experiences.

Business Questions to Consider

While enterprises recognize the long-term benefits of integrating generative AI (GenAI), they are approaching it cautiously due to concerns around interacting with publicly available large-scale language models (LLMs), data sharing, data reliability, complexity, quality of answers, development, rapid engineering skills, cost, and security.

• safety: Engaging with LLMs poses the risk of disclosure of sensitive information, including personally identifiable information (PII), health data, intellectual property, and trade secrets. Such disclosure could lead to competitive disadvantage, lost revenue, and legal repercussions.
• Old Data: With knowledge expanding day by day, an LLM can quickly become outdated. Specialized LLMs focusing on specific knowledge areas are coming into play, which, combined with Search Augmentation Generation (RAG), will provide you with better and up-to-date answers.
• Fragmented DataIn many large enterprise applications, data is spread across a variety of data tools. Point solution tools are added to the application to support an expanding set of use cases. However, the result is data spread across multiple databases, none of which provide complete information, and an increasing compliance, licensing, and cost footprint.

These issues make teams hesitant to deploy AI for tasks beyond chatbots and coding assistants.

The Benefits of Adaptive Applications

So, is that the end of it? Is all that AI can do for you is make clever chatbots? For now, probably. But as legal and regulatory implications start to emerge and the market for AI tools and services starts to mature, new kinds of applications will emerge.

Users don't necessarily need to know that AI is involved. The application is customized for the user without them having to use a chat interface or provide a lot of information up front. The user's activity, profile, location, and other context are processed by the AI ​​and combined with non-AI queries to build the best experience.

Adaptive applications include AI to:

• Hyper-personalization: Customize the user interface (UI), content, and functionality based on the user.
• Context Awareness: Adapts based on location, device type, network conditions, time of day, and user behavior, as well as personalization preferences entered by the user.
• Adaptability: Functionality changes based on user context and behavior.
• Learning and Intelligence: Uses predictive machine learning and real-time calculations.
• Flexibility: Supports diverse inputs and flexible data modeling.
• automation: Efficiently automate tasks and processes.
• Superior performance: Reacts to user activity in real time.
• Mobile/Edge Compatible: Works on mobile devices and at the edge, with or without an internet connection.
• Interconnectivity: Integrates personalized information and displays a 360-degree view of a person.

What does the future hold?

As the saying goes, “it’s hard to predict, especially the future.” But here are some examples of traditional applications and their AI-powered adaptive application versions.

These examples demonstrate the evolution from basic functionality to more sophisticated AI-driven solutions that anticipate user needs and adapt in real time. This change not only improves the user experience but also optimizes efficiency and personalization across a range of areas.

How to get there

Getting there will require some imagination, a willingness to innovate, and risk mitigation strategies, but this is where adaptive applications come in.

There is still a lot that is unknown about the future of AI and how it will be applied to applications. Another key factor is how data is stored and accessed so that it can be adapted. Enterprise applications scattered across half a dozen or more databases and dozens of integration tools are ill-suited to provide a unified view of all the data needed to build adaptive applications.

As a NoSQL database, Couchbase provides a robust distributed platform and all the access methods needed to build adaptive applications, including vector search, SQL, offline-first mobile, automatic synchronization, and caching.