Machine learning and artificial intelligence (AI) have revolutionized UX (user experience) in a variety of ways across a multitude of industries. When leveraged correctly, these tools can help create a tailored and responsive sales or user process, enhancing user experience and increasing brand loyalty.
Machine Learning & AI: A Brief Primer
At their core, machine learning algorithms use statistics to find predictable patterns in large quantities of data so that the system can attempt to predict what the user will want to do next or what response best fits their query.
Services such as Netflix use machine learning to curate your feed, with the goal of selecting films and television shows you are likely to enjoy. Digital personal assistants such as Google and Alexa also rely on machine learning to determine what you are saying so they can provide an appropriate and useful response.
Though the scientific community has yet to settle on a universal definition for AI, it is generally defined as machines with the ability to respond to stimulation in a way that is consistent with the traditional responses humans give to the same stimulations. This allows AI to make decisions that would normally require a human level of expertise.
Machine learning is a subfield of AI and involves automating the building process for the analytical models AI requires to learn and function. Using methods such as neural networks, statistics, physics, and operations research to find hidden insights within the provided data without the need for explicit programming to tell the AI what to look for or what conclusions to draw based on its findings.
ML/AI Continues to Revolutionize UX
According to Gartner, the number of organizations using ML/AI has grown 270% in the last four years. Organizations across many industries and verticals, including manufacturing, E-commerce, and the automotive industry, are increasingly incorporating machine learning and AI technologies into their operations and sales models.
E-commerce companies are frequently turning to ML/AI to offer increasingly personalized shopping experiences, smart purchasing, and ML/AI-powered assistance. For example, E-commerce companies may use ML/AI to drive their recommendation engines, allowing them to better engage with their customers by analyzing users’ preferences, interests, and usage history to create a personalized approach. As a result, conversion rates rise, and the relationship between the E-commerce company and the customer improves.
Recommendation engines powered by ML/AI can also give users the ability to perform audio or visual searches without the need to type in their query. Users can simply upload images of the item they are looking for, and the ML/AI will use that input to find images (and the product listings that contain those images) of that specific item or similar items.
The Automotive Industry
ML/AI has also changed the automotive industry on a fundamental level, improving safety and paving the way for truly autonomous vehicles. Autonomous vehicle ML/AIs are able to use the vehicle’s cameras, radar, cloud services, GPS, and control signals to operate the vehicle without human input, improving safety for all road users and pedestrians.
ML/AIs in vehicles can also use user behavior to personalize each user’s UX and UI in real-time, creating a tailored driving experience.
In both the E-commerce and automotive examples, ML/AI-powered smart systems were able to gather information about users and apply changes to the model to further optimize it for future use. This continuous cycle of learning and adjusting creates a user experience that is continually improving, becoming more engaging and personalized with every user interaction.
The Impact of COVID-19 on Machine Learning & AI
COVID-19 disrupted our lives and our work in many ways. Before the pandemic, the machine learning and AI adoption process was, for many organizations, progressing and adapting well to new information. However, the disruptions COVID brought have changed how many users interact with and use products, which can affect the ML/AI’s learning outcomes.
Though machine learning and AI organizations have always been quick to evolve and adapt to changing environments, the disruption COVID-19 has caused requires users and professionals throughout the industry to adapt even more rapidly to stay ahead of the curve.
Using ML/AI to Improve Your Organization’s UX
How organizations leverage the internet to reach customers and conduct business has changed rapidly in recent years. In addition to machine learning and AI, chatbots, touchless screens, and voice and image recognition have become commonplace. These challenging new opportunities bring many unknowns to the table, and the best practices, standards, and patterns they require are still being determined.
The best advice I can give to organizations considering leveraging machine learning and AI is to not be afraid to experiment and to seek expert guidance whenever necessary. Your organization can overcome these challenges by experimenting, analyzing your results, and learning from your mistakes.
Though machine learning and AI are becoming increasingly essential for business operations and user experience, many organizations continue to struggle to integrate and leverage these technologies effectively. When your organization can harness these technologies successfully, you can give your company and your employees the tools they need to work faster, better, and smarter.
The rise of cloud computing has made machine learning and AI increasingly accessible to many companies, and many are taking advantage of that fact to help them pivot in response to COVID-19 and the disruptions it has caused. Using machine learning and AI, companies can process data more quickly, providing them with the insights they need to create better products for customers faster so they can adapt to the sudden change COVID-19 wrought on customer behaviors.
However, in order to get the most out of your ML/AI solution, from successful implementation to smooth running, you need an experienced data scientist. While IT engineers are able to analyze data, a data scientist is needed to give your organization a full picture of the data and offer actionable insights for further iteration. A successful approach requires a feedback loop, with data scientists iterating the ML/AI engine’s findings as needed over time.
How InterVision Can Help Your Organization Leverage Machine Learning & AI Effectively
Discovering how to leverage a new technology effectively can be challenging, and some organizations may find the prospect intimidating. A lack of expertise, disparate data consolidation, and rapidly changing environments can create barriers that prevent organizations from leveraging AI and machine learning effectively.
For more information, or to get started with your AI or machine learning project, please contact our team today.