In today’s fast-evolving world, the role of artificial intelligence in product and service design has become an everyday reality for many designers. AI is reshaping our approach to digital product development, influencing how companies create and deliver services.
Much of the existing content about AI and design focuses on tools that assist in generating graphics, text, or wireframes—helpful for accelerating certain aspects of design work.
However, we want to explore this topic from a different angle: how AI is changing what we design, or will design in the future, and what this means for UX designers. By diving deeper into this subject, we will showcase how AI is shaping our approach to creating digital products and experiences, redefining the relationship between technology and users.
Examples of AI applications
Let’s start by exploring how artificial intelligence is already shaping the products and services around us. Beyond just design-supporting tools, AI is actively redefining user experiences and unlocking new opportunities for businesses. In which areas is AI already transforming the way we create and interact with digital products?
Chatbots
Chatbots are computer programs designed to engage in conversations with users via text or voice. They leverage artificial intelligence (AI) and natural language processing (NLP) to understand and respond to user queries in a natural and context-aware manner. Chatbots range from customer support assistants on websites to voice-enabled smart devices like Alexa and Siri.
Main advantage is accessibility—when properly configured, they can instantly respond to user inquiries at any time of day or night.
They handle multiple queries simultaneously, enhancing customer service efficiency. They are already being used in industries such as e-commerce, finance, and tourism.
One of the key design challenges is defining the bot’s “personality”—its voice, communication style, and conversational framework. It is also crucial to establish clear conversational boundaries and ensure that responses are accurate and genuinely helpful to all of the users.
Automation of business processes
With advanced algorithms and machine learning capabilities, AI enables the automation of complex processes. Companies like Zendesk and Intercom implement AI chatbots that automatically respond to customer inquiries, with more complex inquiries being routed to the appropriate teams.
Another great example of automation is the use of AI for document processing, which not only enhances efficiency but also ensures accuracy in managing invoices, contracts, and reports.
With the help of Optical Character Recognition (OCR) technologies, paper documents are converted into digital formats, significantly improving their archiving and searchability.
Artificial intelligence also supports HR in recruitment, employee assessments, and talent management. Algorithms can analyse resumes, conduct initial interviews via chatbots, and even monitor employee performance.
There are many potential examples, but the Designer’s role in this context is to understand and map processes to identify areas for improvement. These enhancements should responsibly and thoughtfully improve experiences for both users and employees.
Personalisation of services and recommendations
The potential of AI can also be leveraged in personalising user experiences. E-commerce platforms like Amazon use AI to analyse shopping and browsing histories in order to recommend products that may interest the user.
Streaming services like Netflix and Spotify use AI algorithms to create personalised playlists and movie recommendations based on the user’s past preferences.
However, it’s important to remember that these solutions are powerful tools, and when implemented inappropriately, they can harm users and raise concerns about data security and control. Therefore, it is crucial to carefully consider solutions in your product that transparently explain the rules of engagement and provide users with the option to change their minds about the content presented and how their data is used.
Data Analysis
AI excels at analysing vast amounts of data in real time. Financial companies use AI to analyse historical market data and predict future trends, which helps in making better investment decisions.
Tools such as IBM Watson can analyse extensive amounts of social media data, providing deeper insights into user sentiments and opinions about products or services.
AI algorithms can also be used to detect anomalies in complex data sets, assist with categorisation, and tagging.
Artificial intelligence can help in monitoring competitors, which makes it easier to gain a better understanding of the market.
New areas of work
In addition to the above examples, it’s worth highlighting areas that, due to technological limitations, have been less explored in the daily work of designers. However, in the near future, these could provide inspiration for testing other forms of interaction on a broader scale.
We are referring here to technologies enabling sound processing (such as noise reduction, voice recognition), as well as the development of Sensory AI, which involves combining artificial intelligence with various sensors and sensory devices.
Steps to implement AI in a Digital Project
The design process that incorporates AI is not significantly different from the well-known paths we follow—each time, it should start with defining the problem that artificial intelligence will provide a smart solution to.
- Identifying needs and goals
Using workshop methods, we identify the business problems that the product or service will address. What is the project’s goal? Who will be the users? What does the current market look like? What data do we have regarding product usage?
- Analysis
The collected information is analysed to identify patterns and areas where AI can bring the most benefits. This is the moment to ask if an AI-based solution is really what we need.
- Identifying AI opportunities
Based on the data analysis, we define which aspects of the project could benefit from AI. This may include automating processes, personalising content, predictive analytics, or supporting customer service.
- Choosing tools and technologies
The designer, together with the team, selects the appropriate AI tools and technologies that best meet the identified needs. These may include data analysis tools, machine learning algorithms, chatbots, or recommendation systems.
- Prototyping and testing
The team creates prototypes, which are then tested with users. The goal is to verify whether the AI solutions introduced truly enhance the user experience and meet the intended objectives.
The future of AI
Forecasts for the near future suggest that we may soon move from Artificial Narrow Intelligence (ANI) to Artificial General Intelligence (AGI), which will be capable of performing complex and advanced tasks. (1)
We are likely to see significant development in Conversational AI. Chatbots and virtual assistants will become increasingly advanced, offering more natural and efficient interactions with users.
Moreover, the combination of AI with IoT (Internet of Things) devices will enable the development of intelligent systems for managing buildings, cities, and industrial production.
Ethics and responsibility
However, focusing on current challenges—along with the growing use of AI technology in various sectors, the topic of responsibility in its use is becoming more prominent.
One of the key ethical challenges AI presents is ensuring that algorithms work in a fair manner and do not discriminate against any social groups. In practice, this means the need to monitor and eliminate biases that may be unintentionally introduced into AI systems through training data.
Additionally, transparency in algorithmic decisions is crucial, allowing users to understand how and why certain decisions are made. Lack of transparency can lead to a loss of trust and acceptance of AI technology.
Another important aspect is the responsibility for decisions made by AI. In the event of errors or undesirable outcomes from algorithmic actions, it is crucial to determine who is accountable for these decisions.
We must also consider how AI usage impacts the environment. In 2023, artificial intelligence was estimated to consume around 4.5 gigawatts of energy worldwide, accounting for 8% of total energy consumption in data centers. (2)
The increasing energy demand of AI could lead to higher carbon emissions and strain power grids. Therefore, it is essential to develop and implement AI technologies sustainably, minimising their negative impact on the environment.
Read more in our Sustainable Digital Design Guidebook.
It is important for companies and institutions using AI to be aware of potential risks and take steps to mitigate them, such as through regular audits and ethical assessments of their systems. Only by doing so can we ensure that AI development proceeds in a responsible way, benefiting society as a whole.
In a world where artificial intelligence continuously surprises us with new possibilities, we feel both excitement and responsibility in creating ethical and useful solutions.
We are witnessing the dynamic growth of generative AI, its integration with the Internet of Things (IoT), and the enhancement of conversational assistants. AI is continually expanding the scope of what we can design—from advanced personalisation to autonomous “background” interactions.
This article does not cover the full range of AI-related topics in UX design but opens the door for further exploration. User experience designers face a challenge and an opportunity—how to leverage AI to create products that not only simplify life but also build trust and responsibility in the relationship between technology and users.
