Traditional software development is based on the fundamental principle of deterministic behavior. If a user clicks a button, the software is expected to perform a predefined action. But AI-powered software is different. AI products are designed around probabilistic behavior. They generate answers and adapt to context. They anticipate and make predictions.
The lack of a defined path forces AI digital product developers to approach design in a very different way. They have to think about user experience (UX) in terms that go far beyond aesthetics and latency.
What Are We Trying to Accomplish?
GojiLabs is a leader in AI-powered product design. Whenever they start a new project, they begin with a fundamental question: what are we trying to accomplish? The company’s strategy isn’t one of inserting AI into an existing product. Instead, product development is all about producing software or components that reduce friction, streamline tasks, and improve decision-making capabilities.
When it’s done right, an AI digital product does exactly what its developers expect it to do without making users feel like they are interacting with complex machines. Interactions are seamless and natural.
Information Architecture
To fully utilize AI’s capabilities, product designers must lean heavily on what the technology does well. This includes avoiding what AI does not do well. A good starting point for understanding this concept is something known as ‘information architecture’ (IA). It determines how information is organized, prioritized, and presented. AI does it well.
Information architecture is exceptionally dynamic in the AI space compared to traditional software development. This is because models generate content on demand. So product designers must figure out:
- What information users need first
- How information should be categorized
- The supporting documents and citations users might need
- How users will transition from broad questions to more specific actions
In terms of categorizing data, a well-designed tool organizes its response in a way that makes human scanning and digestion easier. For example, a response might be structured as follows:
- Executive summary
- Metrics and KPIs
- Local or regional comparisons
- Data-driven recommendations
- Supporting documents and reports
Predictive data categorization leads to content that end users can quickly scan. They can locate the data that is most important to them and extract it for whatever purposes they see fit. The fact that they are not getting large blocks of seemingly disorganized text reduces cognitive load.
Conversational Interfaces
Another thing AI does exceptionally well is conduct progressive conversations with users. For some applications, AI-powered product design calls for a conversational interface that allows users to interact with the tool using natural language. No menus or forms are needed.
A good conversational interface has to do more than provide static answers. So designers must pay attention to:
- Prompt suggestions
- Conversational memory
- Clarification questions
- Graceful error recovery
- Follow-up actions or recommendations
When a conversational interface is well designed, users don’t have to know exactly what to ask. Even having a general idea allows the tool to actively guide a user through an entire conversation to the desired result.
The Possibilities Continue to Grow
AI does a lot of things well. And as more things are added to the list, the possibilities of AI-powered product design only continue to grow. That’s good for AI digital product development, companies like GojiLabs, and the enterprises that hire them to produce AI solutions.
Whether you are a software developer or a procurement specialist, here’s the most important thing to know about AI-powered products: the most useful are built around what AI already does well. They aren’t built on novelty and untested features that may or may not deliver real value.