AI Beyond Chatbots and Assistants: Adaptive Applications

AI has impressed the world with natural language chatbots and coding assistants. But as the hype comes to a peak while legal and regulatory issues are unclear, decision-makers are starting to wonder: Is that all there is? How can AI provide deeper value in applications? The full answer remains to be seen, but perhaps the future lies in adaptive applications.

AI’s Business Benefits

AI offers substantial business benefits, driving efficiency and enhancing customer service.

AI-powered code-assist tools like GitHub Copilot, Cody and Capella iQ streamline the development process, enabling faster and more accurate code generation and interpretation, which reduces development time and costs. These tools can identify and fix errors, help a new team member understand the code base faster, assist with testing, make recommendations and more, enhancing the overall quality of software.

For customer service, AI chatbots provide instant, 24/7 support, handling routine inquiries and resolving issues quickly. This improves customer satisfaction by offering prompt responses and frees up human agents to focus on more complex tasks, further increasing operational efficiency. By integrating AI, businesses can achieve significant productivity gains and deliver superior customer experiences.

Business Problems To Consider

Enterprises see the long-term benefits of integrating generative AI (GenAI) but approach it cautiously due to concerns about interacting with public large language models (LLMs), data sharing, data trustworthiness, complexity, answer quality, development, prompt engineering skills, costs and security.

• Security: Engaging with LLMs risks leaking sensitive information like personally identifiable information (PII), health data, intellectual property and trade secrets. Such leaks can result in competitive disadvantages, revenue loss and legal repercussions.
• Outdated data: LLMs can quickly become outdated as knowledge expands daily. Specialized LLMs focusing on specific knowledge domains will emerge and combine with retrieval augmented generation (RAG) to provide better, more up-to-date answers.
• Fragmented data: In many large enterprise applications, data is spread among a variety of data tools. Point solution tools are bolted on to an application to support growing use cases. But this results in data being spread out to multiple databases, none of them providing a complete picture, and all of them increasing the compliance, license and cost footprint.

These problems make teams shy to adopt AI for tasks beyond chatbots and coding assistants.

Advantages of Adaptive Applications

So, is that it? Is a great chatbot all you can hope to achieve with AI? For now, probably yes. But as legal and regulatory ramifications start to clarify, and as AI tooling and the AI services market start to mature, a new breed of applications will emerge.

Users won’t necessarily know that AI is even involved. Applications will customize themselves to adapt to a user, without the user engaging a chat interface or even providing a bunch of upfront information. Their activities, profiles, locations and other context will be processed by AI and combined with non-AI queries to construct an optimal experience.

Adaptive applications will include AI to achieve:

• Hyper-personalization: Customizes 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 behaviors, not just the personalization settings they fill out.
• Adaptability: Features change 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: Automates tasks and processes efficiently.
• Exceptional performance: Reacts to user activity in real time.
• Mobile/edge-enablement: Operates on mobile devices and at the edge, with or without an internet connection.
• Cross-connectedness: Integrates personalized information for a 360-degree view of a person.

What the Future Looks Like

As the adage goes, “It’s difficult to make predictions, especially about the future.” However, here are some examples of a traditional application and how an adaptive application version with AI might look:

These examples illustrate the evolution from basic functionalities to more sophisticated, AI-driven solutions that anticipate user needs and adapt in real time. This shift not only enhances user experience but also optimizes efficiency and personalization in various sectors.

How to Get There

It will take some imagination, the will to innovate and a risk-reduction strategy to get there, but adaptive applications are coming.

There remains much to be seen about the future of AI and how it will be applied to applications. Another important component is how you store and access your data in order to be ready to adapt. An enterprise application burdened by a sprawl of six or more databases and dozens of integration tools will be ill-equipped to provide a cohesive view of all the data needed to build an adaptive application.

Couchbase, the NoSQL database provides a solid, distributed platform and all the access methods you need to build an adaptive application, including vector search, SQL, offline-first mobile, automatic sync, caching and more.

The post AI Beyond Chatbots and Assistants: Adaptive Applications appeared first on The New Stack.

Chatbots are evolving into hyper-personalized, automated and context-aware AI applications that anticipate user needs and adapt in real-time.