Designing with AI: Moving Beyond Tools to Orchestrate Meaningful Experiences
The discourse surrounding Artificial Intelligence often feels like a race against a clock no one quite knows how to read. From the “FOMO” (fear of missing out) driving corporate investments to the radical claims that traditional design processes are dead, the industry is at a crossroads. Is AI merely a productivity tool to be mastered, or is it a new material that requires a fundamental shift in how we approach experience design?
This was the central question of UX Talk #42, hosted by POLI.design’s Experience Design Academy. The event featured @Yulya Besplemennova, Partner and Complexity & Innovation Expert at oblo, and Davide Posillipo, Co-founder of Komebi Studio. Together, they challenged the “hype machine” and presented a more rigorous, human-centered framework for the AI era.
“The Triple O”: Why Outcomes Must Be Designed
For years, the “Double Diamond” has been the gold standard for design thinking. However, when designing for AI systems — which are continuous, evolving, and often unpredictable — the traditional model falls short. Yulya Besplemennova introduced the “Triple-O” framework, an evolution that adds a critical third phase: Outcomes and Impacts.
The rationale is clear: AI systems do not “end” at delivery. They evolve through use, and their true impact is often difficult to capture through traditional ROI alone.
- The Measurement Gap: Many organizations struggle to see the Return on Investment in AI because they focus on narrow productivity gains.
- Qualitative Indicators: The Triple Diamond encourages designers to map “softer” but vital impacts, such as job satisfaction, the ability to learn, and how knowledge is structured within an organization.
- Continuous Feedback: Once outcomes are defined, they must feed back into the initial opportunity phase, creating a closed loop of continuous refinement.
Bridging the Great Divide: The Data Design Unit
A recurring theme in the talk was the friction between technical and creative profiles. Davide Posillipo, a statistician by training, argued that the industry must move toward a “post-technical” perspective where data is not seen as a cold artifact, but as a crystallization of human relationships.
To bridge this gap, he proposed the creation of a “Data Design Unit” — a collaborative team where designers and data engineers work in tandem from day one.
- “Interviewing” the Data: Instead of receiving data as a static requirement, designers should “interview” data to uncover its meanings and the organizational stories it tells.
- The Data Journey: Just as designers map the User Journey, they must now map the Data Journey in parallel. Understanding how information flows — and where it breaks — is essential to connecting user needs with technical implementation.
AI as Material: Orchestrating the “Invisible” Experience
One of the most provocative takeaways was the shift from designing interfaces to orchestrating relationships. AI should not be viewed solely through the lens of a chatbot or a screen; it is a material with a “spectrum of presence”.
Yulya described this spectrum as ranging from invisible background technology (like spam filters) to proactive agents and even anthropomorphized entities that evoke emotional responses.
- Managing Expectations: The designer’s role becomes one of managing user expectations. If a user misunderstands what an AI can actually do, the experience fails regardless of the technical quality.
- The “Post-Screen” Future: As AI agents handle more tasks autonomously, the focus of design may shift away from pixels and screens toward service design and the orchestration of complex, multimodal interactions.
The Stoic Designer: Resisting the Hype
In an era of “techno-solutionism,” the speakers advocated for a “Stoic” approach to AI adoption. This involves resisting the urge to implement AI just because it is available and instead asking: Is this the virtuous choice?
The Opportunity Landscape tool was introduced to help organizations navigate this. Before jumping to Generative AI, designers should evaluate whether a problem requires automation, assistance, augmentation, or perhaps no AI at all. Sometimes, the best “design” solution is a purely organizational change rather than a new algorithm.
Future Skills: Speaking the Language of the Machine
As the role of the designer evolves, so must their toolkit. The talk concluded with a reflection on the skills needed for the next five years.
- Technical Literacy: Designers do not need to be coders, but they must speak the language of technicians. Understanding what a “data lake” or a “model” is allows a designer to have a seat at the strategic table.
- Strategic Vision: Designers must move from being “pixel-pushers” to being architects of meaning. In a world where AI can generate layouts in seconds, the human value lies in asking why we are building something and what its long-term impact will be.
- Historical Context: Both speakers emphasized that these technologies are built on centuries of statistical and design traditions. Understanding this history helps practitioners resist the “instant gain” rhetoric of the current hype and focus on sustainable innovation.
The Bottom Line: AI is not a shortcut to skip the research or the hard work of design. It is an expansion of the field. By embracing data as a design material and focusing on long-term outcomes, we can move past the “Oppenheimer moment” of design and create systems that truly augment human potential.
