When AI is the new King - than IA and UX is the Queen

One or two might remember my article "Content is King - UX is Queen" from 2012 - that was the days we hadn't had the abbreviations KI and AI not everyone's lips. In order to follow up on my thoughts back then.

Context provides KI and AI with the necessary background information to fully understand user requests and respond accurately. Without context, AI may misinterpret data resulting in inaccurate responses.

Consider a question like "What's the temperature?" Without context, AI wouldn't know whether to provide the current temperature, the temperature for a specific day or location, or even what scale to provide the temperature in (as someone from Germany who sees 25° as pretty warm and OK - I was 'challenged' as I so the 25° on my motorcycle tour from NY to SFO - Fahrenheit or Celsius 😉).

Context also helps AI understand complex relationships and make predictions or recommendations based on previous interactions or gathered data. For instance, if a user often orders pizza on Friday nights, AI, remembering the context of previous orders, might suggest pizza places on a Friday night.

In conversational AI or chatbots, maintaining context is important to carry on meaningful and coherent conversation. For example, if a user says "tell me a joke," and then follows up with "tell me another," the AI needs to understand the context of the conversation to respond with another joke. Without understanding the context, AI would struggle with processing natural language, understanding user intent, and personalizing interactions. Hence, context is crucial for AI.

Following I will go more in detail about ...

• Information architecture (IA)
• Exploring how Artificial Intelligence (AI) and User Experience (UX) intersect
• Never underestimate the human factor(s) 
• Difference Between AI and GenAI

Information architecture (IA)

Information architecture (IA) is all about how we arrange, represent and categorize data.
There are four main components to consider:

1. the ways we organize and structure information
2. the labeling systems we use to represent information
3. navigation systems which dictate how users move through different sections of information
4. search functions which facilitate users in finding specific information

IA is like a roadmap, guiding us on how to handle different activities such as content curation. It is particularly helpful when dealing with unstructured or partially structured data, enhancing big data analysis.

Understanding and properly utilizing IA is crucial for organizations, especially in relation to artificial intelligence (AI). 

• It's important for organizations to set the right foundation in terms of information and data, while also allowing for agility in implementing and utilizing AI.
• When planning the information architecture, always keep in mind the business outcomes you're aiming for. Align your IA to support your strategies and requirements to continuously drive value and innovation.
• Achieving success with AI depends on the specifics of the use case, quality of data, and the readiness of your IA in terms of infrastructure and data maturity. Your organization's attitude towards AI and its operating model also play a significant role in determining your success with AI.
• If your organization is unsure about its information architecture, assess the maturity of your data and information while exploring AI options. Once you have decided to operationalize AI, think about the capabilities needed to integrate, deploy and manage your AI models. Are DataOps and AIOps well understood and managed in your organization? These are crucial considerations for successful integration of AI into your workflows and processes.

Exploring how Artificial Intelligence (AI) and User Experience (UX) intersect

Both AI systems and UX designers gather and analyze data, study user behavior, and can enhance the delivery of services and overall user experience. Examples of this in action include chatbots, self-driving cars, Google Translate, and Siri. Using AI could make UX design easier and more effective, opening up easier access to different tasks and creating better user experiences. However, AI also has its drawbacks. The technology is still in its development stages and people are still figuring out how to best use it.

In context you might to look at this article: Ethics and Responsibility regarding KI, AI, ML and DL

I recognize the immense value of AI for improving user experience, and I see AI as a tool that can create new experiences for products, business and life. However, I also highlight the importance of established UX methods and frameworks in imagining and creating positive interactions between humans and AI, handling the idea and understand of 'truth' vs Suggestion or option, handling friction in a new from, seeing the positive aspects of friction. We need to use these established UX methods and service design techniques, and modify them if needed, to address the challenges. In my view, UX and AI can and will benefit each other in a synergistic way.

But never underestimate the human factor(s) 

Language and how to phrase something will even more be a 'challenge' ... watch  this, my favorite clip when it comes to explaining 'language challenges' 

Difference Between AI and GenAI

Finally I like to write a few thoughts about the term GenAI - one or two might have already thought is he talking about AI, isn't he talking about GenAI?  Yes I do - but as I am thinking in picture I can hardly distinguish between the two - as finally from 'my picture in mind' - both are so much going hand in hand - While, by definition, AI enables a service or tool to mimic human cognition in a broad sense, and ML allows it to steer its own learning and improvement by processing data. GenAI for sure takes it a leap further by actually creating novel, human-realistic content using learning algorithms. But why do we have to think in these silos? But please don't get me wrong for technical, coding people it might be super important to making , having these differences - and they require clear differences between general AI or generative models.