AI-Powered Software: Enhancing User Experience

It’s the use of AI for eyeballs – first to anticipate user needs and second to automate tasks to layer on experiences that users don’t even realise they are engaging with. Most examples of AI today are focused on this first category – for example, if my household shops online with an eCommerce app, it can learn to suggest products I might need again, perhaps thinking it’s time to restock because I haven’t ordered anything for a while. Or maybe it can present some relevant activity offers that it thinks I’d like to know about – want to drive around for a nice meal in the sun?

Here’s another way AI might lead to personalisation: by taking personal data to predict how users would like content and features. This would require new kinds of ethical consideration, such as people’s right to privacy, and new types of transparency.

Voice User Interface (VUI)

VUI technology permits users to interact with and control technology without the need to use a screen, increasing accessibility for persons with hearing or speech impairments, and decreasing reliance on users’ hands or eyes to access media.

Prompts need to be contextually appropriate and intuitive, guiding users in a way that makes sense to them in the immediate task context, and providing fallback modes such as typing or gest

There might be errors, but they should be handled gracefully: Cortana’s error messages ask for clarification or suggest alternatives when she doesn’t understand what the user said. This diminishes the amount of annoyance for the user and gives the impression that Cortana is listening, which increases engagement and user happiness with the overall experience.

Personalization

In this way, AI can create bespoke experiences built on an individual’s data and keyphantom points, driving more personalised content and better customer service, while removing delays, dead time, distractions and bureaucracies, simplifying interactions and boosting satisfaction across the board.

Machine learning algorithms can help to fine-tune the type and number of results returned to visitors to an e-commerce site, say, or the number of content recommendations they receive, but they can also be used in areas such as healthcare where AI systems can be trained to identify stroke-like lesions in CT scans sent to neurologists to flag up a potential issue.

And even if AI technology can help to enhance user experiences, certain ethical concerns must be addressed, such as making sure that users are clearly aware of any AI being used, or ensuring that AI algorithms are not biased or otherwise unfair. This is why it makes sense to have teams consisting of multiple disciplines working with any AI-powered tools – they can save significant design time.

Automation

Artificial intelligence (AI) is in the process of automating software development, DevOps and IT at a rapid pace. For writing application code from a natural-language prompt, developers use generative AI tools such as GitHub Copilot and Tabnine.

AI has become an embedded user experience by design in many brands and products, from the personalisation of e-commerce transactions, to healthcare chatbots, home assistants such as Amazon Echo or Google Assistant, and much more. We get productised recommendation, performance optimisation, and interaction design – on e-commerce stores, healthcare chatbots or virtual home assistants such as Alexa or Google Home Assistant, for instance.

If AI becomes omnipresent, especially with deeper access to our inner selves, we will need to take into account ethical aspects such as algorithmic transparency and minimising bias against certain groups. In this context, one promising approach would be having multidisciplinary teams of people from diverse backgrounds collaborate more extensively, with different mindsets and competences brought together; their endeavours, shaped by AI, would create valuable experiences.

Natural Language Processing (NLP)

Natural Language Processing (NLP) brings about a change in computer technology user interfaces whereby users can communicate with the system either through voice or dialogic, thereby simplifying the interaction with users and raising their level of satisfaction.

This can be very useful for the NLP-industry, which spans across sectors such as customer service, data analysis and text classification. For instance, NLP could be used for the analysis of customer feedback to identify trends across the business, as well as to classify text for spam identification.

NLP can, of course, play a fundamental role in enabling a great deal of these business processes – and enhance the more bespoke end-user experience. Text prediction and correction, conversational chatbots, natural language searches, even some virtual assistants are built on NLP capabilities, all geared towards creating the best customer journey with your product or service.

Accessibility

While UX/UI designers can imagine what will captivate an audience and use that vision as a starting point for their design, AI can collect big data and apply machine learning to tailor user experiences to our specific needs. This enables the development of personalisation features for websites and applications by using voice interfaces, conceptualising visual design using generative models, and automated user-testing. How can a computer know what our preferences are? Yet, in truth, the AI cannot possibly ‘read between the lines’ as humans can, and it is unlikely that UX/UI designers will ever place full confidence in an AI that is able to consider context beyond basic semantics and syntax. No matter how advanced AI technology becomes, it will never be able to replicate human creativity.

The influence of AI on various sectors, from healthcare, e-commerce and e-services to vocie recognition and text-to-speech technologie has been ubiquitous and not limited to improving process flows, enhancing data analytics and customer experience. Many of these technologies, such as text-to-speech technologies, increase accessibility and inclusion in terms of servicing and delivery that might not have otherwise been possible.

However, AI is also subject to bias. It must therefore be watched closely so that its results don’t reflect the sexist or racist values of society or culture (and even then, not in a way that exposes children or frail elderly people to unwarranted risk).