How AI Transforms the User Experience – Personalization

I have worked at the intersection of UX and AI for several years and my focus has been on understanding the enterprise side of the equation. Even with this focus, there is a great need to understand and plan for the total experience of those ultimately impacted by this sweeping technology, not just the enterprise developers. Several designers who have written about the UX of AI spend their time focused on interaction patterns and the impacts of AI on the design profession, with less insight on broader aspects of the human element. This is what I would like to address more. But first some context.

What powers AI?

AI already impacts daily life in many ways, often unknown to most people. AI is a driving force for some of the largest companies across the globe. Whether you are receiving a price on an online purchase, viewing a list of results from a search engine, ordering a shared ride, getting approved for an online transaction or finding a new song through a recommendation — AI is there influencing or driving many parts of those daily interactions.

AI algorithms are fed by massive amounts of data to find patterns that enable your interactions with various companies. Through this complex network of data and algorithms AI removes limits in learning. People then apply this data and algorithms to interesting goals. For example, some data science researchers are exploring ways to use AI to automate user research. They experiment with techniques like understanding sentiment, classifying feedback  — with limited success deriving truly meaningful and actionable impacts.

What is AI-enabled personalization?

At the highest level, two of the leading drivers for AI adoption are delivering a better customer experience and helping employees to get better at their jobs. Some AI futurists believe that “consumers will demand even more customized experiences –  giving rise to hyper-personalization and greater customer experience within the e-commerce sector” (Weissgraeber, 2021). AI makes it possible to realize something that has eluded marketers and product owners for years. It makes it possible to have large scale personalization, true one-to-one experiences for all. Note that this is very different from the earlier concept of mass-customization of the early 2000’s which focused on the user agency to choose a design or product configuration that they desired.

What are some examples of AI-enabled personalization?

Here are some current and future examples of personalization and the reasons they need a stronger focus on the user experience:

Retail and eCommerce – AI will continue to empower “hyper-personalized” ecommerce experiences. Companies like Salesforce realize this level of personalization by applying personalized recommendations and by providing tools that enable web developers to customize the layout of content that users see. AI in design enables user-centered design, to “an extreme level of granularity” – concept of design for every single person (personalization at scale). 

Entertainment – One example in media and entertainment is how Netflix combines user behavior modeling with recommendations to offer each viewer a personalized viewing experience. AI provides for more efficient classification, tagging and recommendations of existing and newly generated content like online videos (Rao, 2017). 

Dialog interfaces are an interesting case of an AI enabled solution that are supposed to drive better user experiences in customer service. While this is a complex area dominated by data scientists, people recognize the need to focus on basic UX principles and people-centered design, such as giving the user a way to bypass an automated system that is not working out and reducing the user’s cognitive load. While this is a noble goal, most deployments of dialog Interfaces are centered on driving cost benefits to the service provider versus driving real beneficial user experiences.

Healthcare, Personalized Medicine and Precision Medicine. In the comparatively AI-nascent field of healthcare, the future prospects are for diseases are more quickly and accurately diagnosed, drug discovery is sped up and streamlined, virtual nursing assistants monitor patients and big data analysis helps to create a more personalized patient experience (Thomas, 2019).

These are just a few examples of how AI-enabled personalization is impacting life today and will in the future. While the proposed benefits are many, there are tremendous risks that make an emphasis on the human element of this powerful technology even more critical.

What are the risks of increased AI-enabled personalization?

Privacy sacrificed for convenience. The convenience of more personalized experiences requires even greater access to personal data.  This raises the tremendous risk of encroaching on personal privacy by the mixing of data and techniques.

Security. With increased access to personal data there is always the looming risk of security breaches related to the massive amounts of data that needs to be accessed and stored.

Loss of Personal Agency or Control. Some designers focus on hiding the AI from users as a way to minimize exposure to complexity. Lack of visibility makes it hard to understand or challenge the outcomes. This raises the question: Does too much personalization inhibit discovery? Will a loss of that skill for discovery inhibit people’s opportunity to experience serendipity?

Bias and Discrimination. We already know that companies leverage third-party cookies and complex algorithms to track users’ online activities and that information is used to serve different prices to different customers. With little commercial guidance on fairness or transparency in pricing and other automated decisioning processes, how  can we ensure people are not penalized based on personal characteristics like gender, race or economic status?

Less Human Interaction more Automation. While AI will enable retail and entertainment experiences that are more personalized, this increased personalization raises the risk of decreasing interpersonal interactions.

What are some future research directions?

I have not yet found a lot of research or good examples of how end users are engaged in identifying the benefits of this new technology. Most user research focuses on improving the interaction with technology, not finding the real benefit to the end user who generally does not have agency in whether or not to use the technology. What is the prospect for personalization driven by AI,  if the benefit to humans is not clear?


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Creative Process from the Perspective of one Playwright

Creative process : collage

While (half) watching a PBS documentary one evening this past week, I was introduced to one facet of the creative process of a famous playwright, August Wilson. This Pulitzer Prize winning playwright had the habit of taking odd jobs for exposure and immersing himself in environments that provided an opportunity to interact with, and observe many people that would eventually feed his writings. Wilson was known for writing expressions, conversations, descriptions and situations on loose slips of paper or even napkins. He would then collect these in a box and later assemble these snippets into characters, dialogue, scenes and acts and finally completed plays. The creative process for him was never about sitting down to draft an entire play from beginning to end, rather it was about being open to weave disparate pieces into a coherent whole. Wilson gave himself the time and space to create theatrical collages out of snippets of his interactions with his environment.

On Leadership, Design Leadership

Institute for Human Centered Design
Institute for Human Centered Design

I attended a two day leadership training retreat, one that the organization sends everyone to regardless of your role, under the premise that no matter what your function is you can display the qualities of a good leader. From past participants, I saw photos of people holstered in ropes, blindfolded etc. Prepared for the worst, I donned my t-shirt, gym pants and a pair of run-down sneakers. I was ready for whatever would be dished out, except bugs..especially not spiders.

As usual, I was surprised. No swinging from trees. It was a beautiful and reflective few days where I gained a view of leadership (for the professional world) that I haven’t see communicated in this way. The main points are that true leaders*:
1. Lead by personal example
2. Develop cooperative relationships with people they work with
3. Follow through on commitments
4. Treat everyone with dignity and respect
5. Empower people by giving them the freedom to choose
6. Show gratitude and humility
7. Encourage and never discourage either by word or by deed

Good examples of leadership in the design and technical professions can be hard to come by if measured by the above criteria. Hubris and heroics can seem to reign over humility and cooperation. Yet looking around, I can point to wonderful examples of true leadership I have been exposed to. I’m thankful to the many positive examples of leadership I have had in my professional life (and beyond) and I will let them know this.

Many lessons learned, ideas to apply. And yes there were bugs, even spiders.

Training was based on the book: The Leadership Challenge by James Kouzes and Barry Posner

Deciding for Decisive

Decisive_heathI’ve been a fan of the Heath Brothers book ‘Made to Stick’ so I was pleased to find that they came out with the book ‘Decisive: How to Make Better Choices in Work and in Life’. I have a small set of books that I plan to re-read every year and this one made the cut.  As a designer, understanding how people decide is critical. Making decisions on design without a process can lead to poor outcomes. Without giving up too much of this book, I have to cite two of my  key take aways; (1) Ask yourself, ‘What would have to be true for an option you are considering to be the right one? ‘ and (2) ‘Multi-track’, don’t limit yourself to a few options, ‘think and not or’. Brilliant book for many reasons. Highly recommended read for anyone interested in the mechanics of decision making.