Confluence design: the synergy effect of AI, ML, and UX - Artificial Intelligence & Machine Learning - Driven User Experience and vice-versa
“The present is the past of the future.” Wojciech Fangor
Humans are outstanding creatures when it comes to imagination and creations of tools to augment their capabilities. Since the 1st Industrial Revolution, our potential has been growing exponential, and today, we are giving the first steps on what can be the next greatest revolution — the age of Artificial Intelligence (AI).
As well as within the onset of the personal computer, mobile smartphones, and cloud computer, now, AI undeniably is shaping the way things work forever.
It is already a cliché refer that data is transforming the business landscape. In meantime, it is not so obvious to acclaim that the data is rapidly shaping Design, especially the User Experience (UX) field. Humanity is moving fast into a new digital era where Big Data, internet of things (IoT), Blockchain, Artificial Intelligence, Virtual and Augmented Reality (VR & AR) are increasingly becoming a part of designers’ lexicon.
Technology is changing the way we think about design.
Intrinsic to this transformation we witness data shaping everything since finance to social systems, social media to design. Therefore, what we are fetching is physical systems interacting with social systems, the interaction produces data, and starts to be driven by it. Artificial Intelligence (AI), Machine Learning (ML) & Big Data, are switching the scope of design projects.
“AI will undeniably shape the user experience of tomorrow.”
Jo?l van Bodegraven
The role of a User Experience Designer (UXD) is evolving daily, and subsequently, their mindset and skills required to keep expanding and challenging their boundaries are too.
Briefly, if we examine the role of a UXD currently, one wear multiple hats inside an organization. It is expected to include skills of a product strategist, a user research, a visual designer, an interaction designer, and the list are growing each passing day. While it is exciting to confront the new challenges of emerging practices and the expanding influence in design, it is also difficult to maintain professional currency under rapid change. And for example, with the rising of AI SaaS, the life of a UXD a priori will not get any simpler.
We are moving forward from designing process towards designing systems.
The touchpoints a UXD needs to considerer in a service are growing in complexity as AI starts to spread around all types of industries. Nevertheless, there is an apprehension in the design field of how the design process has still been seen as a mechanism of just improving the appearance and function of messages, products, and environments. Likewise, AIGA (2018) recent report, shared another apprehension, correspondingly a big portion of today’s designers still focus on an object-driven process that addressed one independent physical constraint at a time. As previously mentioned, through AI & ML, the field is walking into an opposite paradox.
To be successful in solving complex problems designers must recognize how systems-level work is different from designing objects; how inputs, processes, outputs, and feedback contribute to system effectiveness. They must analyze patterns in poorly defined situations, through research, studies of system models and theories of change, and visualization strategies (see also: AIGA — Accountability for Anticipating Design Outcomes). They must build connections across disciplines when design knowledge is insufficient for the problem at hand, and they must collaborate in teams comprised of experts from many fields. This work doesn’t negate the value of formal elegance or functional efficiency but instead acknowledges that planning and analysis require equally creative insight and those complex problems are rarely solved by form alone. SOLUTION is to ally UX with ML & AI in a Confluence Design.
Confluence Design
Perhaps the best way to think about the relationship between UX and AI, ML, and Deep Learning is to test it on the field. And to be able to do so is important to build a narrative of understandings on how these distinct but some how correlated fields of study can work together in pursuit of the same goals.
The synergy between these fields, I resemble to the designation of — Confluence Design — In my perception, for the literal meaning of the word, there isn’t better analogy for the conversion of AI and related fields with UX into a one domain.
“the place where two rivers flow together and become one larger river; a situation in which two things join or come together” English Cambridge Dictionary
Photo by Dan Roizer on Unsplash
AI is taking Designers’ by storm. Since the 1st Industrial Revolution that Design is perceived as an extension of the human capacity to enhanced its ability without replacing the Human from the process. However, with the ascendancy of AI, and the opportunity of AI solutions start to take actions and decisions on behalf Humans — Anticipatory Design — we may be a witness, for the 1st time, the need to redefine Design principles and/or core notions.
This Confluence will shift Design values to an all-new dimension and/or paradox. And we are becoming to feel it. The time is now, and Designers’ needs to become more data literate from now on — Human-centered Machine Learning (HCML).
Regardless of the fact that AI field is in its infancy, is already starting to rise some friction within the design process. Sharing the same uncertainties and perspectives of Allen (2017), there are a lot of questions to be raised and answered such us, will conventional approaches to UX work well in contexts like this? What would a journey map look like? How would user interviews work? What would a content strategy be? How would a competitive analysis be useful? How will heuristics conduct evaluations? Would personas still be an effective model for understanding interactions? How will we measure adoption and retention rates within AI? Since the user will interact (input) less with the system. Those are just a few questions there is no accurate answer today. But there is a lot more to be addressed under these circumstances.
Nevertheless, AI does have not yet great predominance in the field of design, despite that, is still important that designers start getting hand-on now, or at least be aware of it, so it doesn’t fall behind in the future.
It is imperative to start seed these ideas into designers and raise their awareness’.
How will designers learn and be prepared to create and anticipate design solutions to predict human behavior in a way that better serves them?
It is central to inspire the current and future generations of designers to aspire for user-centered solutions throughout the capacity of these new technologies. The issue now is that, in general, the current design education is deeply struggling to follow and adapts to the current changes, more so with the upcoming challenges. Curricula must be rethought from the ground up, not modified through endless additions to an industrial age model. By the same token, despite some efforts to prepare students broadly among design, business, and technology, a significant debate arose over the ability of undergraduate design generalists to solve complex problems and to navigate highly politicized business environments.
Designing with Purpose
“AI can be the catalyst for great user experience.”
Jo?l van Bodegraven
The global economy has shifted from a product to a service industry, and my especial attention goes to SaaS (Software as a Services). Amazon markets their ability to deliver products they don’t produce through sophisticated service ecologies. Zipcar makes nothing but provides access to personal transportation for people who don’t want to own cars. Anything can become a service in today’s marketplace, typically accessed through self-service technological systems. And with the fast development of AI & ML, this scenario will fast increase into these demanding markets.
Technology plays an out-sized role in shaping the future of design.
Either as a strategy or as a methodolog, AI & ML are challenging traditional notions of information as something material, “static” in time and space. Circumstances with deep roots under the design field. But as more and more experiences are built with ML, it’s clear that UX designers still have a lot to learn about how to make users feel in control of the technology (paradox of choice), and not the other way around (Noessel & Gilbert, 2017).
For Bodegraven (2017, p. 437),
“This alienation to the notion of choice and free will, raises a serious of concerns, for the author “ With automation, much human knowledge and skills are distilled into algorithms. This causes a risk of users getting alienated of the distilled knowledge and skills. The more we automate, the further we remove ourselves from understanding what and why we do things.”
We need to respond to forecasts of future employment in the design fields and anticipates the design competencies for which college students and practitioners will be accountable in the near future. These developments reflect increasing complexity in the nature of design problems (wicked problems, often a symptom of another larger problem) that call for new methods appropriate to systems-level work. Not surprisingly, they describe an outsized role for technology and its growing influence on everyday lives, as well as business. The importance of values-driven work and design leadership deepen in both business and the social sector and how technology is impacting both. And developments call for expanded research efforts that support decision-making in design.
Today’s scenario represents challenges for professionals in broadening their knowledge and skills as well as preparing young designers for long, productive careers. Is important to start debating which will be the designer’s professional demands of the future? And also who we will be promoting the importance of design in a rapidly changing landscape of opportunities.
