Like just about everything else that trends, AI comes with its own library of buzzwords. But those buzzwords do not even begin to tell the whole story. In fact, the entire conversation around artificial intelligence has shifted dramatically in the last 12 to 18 months. Developers are not asking only what AI can do.
They are actively working on ways to embed it seamlessly into the software people use every day.
Businesses looking to build competitive digital products have jumped on the AI train as well. But they face challenges, among them the task of understanding the core AI technologies that transform raw data into intuitive and human-facing experiences.
Success Rests on Four Strategic Pillars
The biggest AI mistake a company can make is chasing trends. Success in AI product development isn’t found in what is trendy; it is found in deploying AI technologies in ways that improve efficiency, streamline workflows, drive revenue, and retain customers. Moreover, it rests on four strategic pillars:
1. Machine Learning
At the core of every intelligent software package is the ability to evolve and improve without the need for explicit programming. This sort of adaptability is missing from traditional software that follows strict if/then rules. Adaptability is possible thanks to machine learning capabilities.
Machine learning is a technology that allows systems to track user behavior, combine it with vast sums of historical data, and adjust how they respond dynamically. Machine learning is what makes creating digital products that feel deeply personal possible.
Whether an app tailors a unique dashboard to a particular user or predicts content preferences based on frequently visited media platforms, embedded algorithms treat every user interaction as an opportunity to learn and adjust. A properly written app becomes more efficient, intuitive, and valuable to the end user over time.
2. Natural Language Processing
Creating user interfaces (UIs) for digital products is an exercise in probabilistic modeling. Because human communication is messy – it is loaded with slang, idioms, and all sorts of contextual nuances – modern AI apps must be built with embedded natural language processing (NLP) capabilities.
NLP invites user interactions using spoken words or text written out using words and phrases the user would normally speak. Sophisticated algorithms can understand intent and analyze user sentiment in-conversation. This facilitates context-aware responses that make for a remarkably human experience.
3. Visual Comprehension
AI product development has traditionally relied on text and voice inputs for conversational transactions. But increasingly, digital products are expected to work with images as well. GojiLabs explains that these new capabilities are known in the industry as ‘computer vision’.
The company says treating pixels from individual images or videos as structured data points makes it possible for algorithms to identify objects. They can also read text in the wild or overlay digital information on the real world by way of augmented reality.
4. Predictive Analytics
Far from generating images or helping office workers draft better emails, AI-driven digital products can be designed to make use of predictive analytics. Simply put, predictive analytics help users anticipate what might happen next.
It is already being used in the healthcare industry to help clinicians inform patients of certain historical health risks and what they can do to maintain good lifelong health. Predictive analytics makes heavy use of historical data points to predict future behaviors, events, and the like.
This post barely scratches the surface of modern AI product development capabilities. Companies like GojiLabs are redefining how AI is being used by both consumers and enterprises. They are going beyond buzzwords and trends to make AI relevant to the most critical aspects of life.
