Artificial Intelligence Experience.
AIX is MindPort's framework for understanding and improving how humans experience AI systems. It combines UX, HCI, product research, behavioral science, psychology, and social research to examine how people interpret, trust, control, adopt, and build relationships with AI-enabled products.
AI products introduce new experience challenges.
They can generate, recommend, automate, infer, personalize, converse, act, and adapt. That changes what users need to understand. It changes how trust is formed. It changes the role of control, agency, feedback, and expectation.
AIX helps teams examine those questions with greater clarity.
Technical capability is only one part of product success.
A model may be powerful, but users may misunderstand it.
A feature may be impressive, but people may not know when to trust it.
A workflow may be augmented, but users may feel a loss of autonomy.
A product may be novel, but not habit-forming.
AIX helps teams understand the human conditions that shape whether AI products are useful, trusted, adopted, and returned to.
Seven lenses on the human side of AI.
Comprehension
Do users understand what the technology can do, and where its limits are?
AI products often fail when users form the wrong mental model. They may expect too much, understand too little, or misread what the system is doing. Comprehension looks at how clearly the product communicates its role, capability, limitations, uncertainty, and value.
Trust
Do users know when, why, and how much to trust the system?
Trust in AI is rarely binary. Users may overtrust, undertrust, or trust the system in the wrong moments. Trust looks at confidence, reliability, transparency, uncertainty, failure communication, evidence, consistency, and calibration.
Control
Do users feel they have agency over the experience?
AI systems can act, recommend, generate, and automate. That creates value, but it can also reduce a user's sense of control. Control looks at steering, correction, customization, consent, reversibility, override, and recovery.
Usefulness
Does the product create meaningful value in the user's real context?
An AI product can be technically impressive without being practically useful. Usefulness looks at task fit, workflow fit, relevance, effort reduction, quality of output, decision support, and the degree to which the product helps users achieve something they care about.
Interaction
Does the interaction model feel natural, efficient, and appropriate for the task?
AI changes interaction patterns. Users may prompt, converse, delegate, supervise, correct, compare, or collaborate with the system. Interaction looks at interface patterns, tone, modality, feedback loops, latency, prompting, conversational flow, and multimodal experience.
Adoption
What makes users start, continue, or stop using the product?
Adoption depends on more than initial interest. It depends on perceived value, habit formation, onboarding, repeat use, social context, workflow integration, and moments of reinforcement. Adoption looks at the behavioral conditions that turn an AI capability into a repeated product behavior.
Meaning
How does the product change the user's sense of identity, capability, relationship, status, or role?
AI products can change how people see themselves, their work, their creativity, their agency, and their relationship with technology. Meaning looks at the deeper human, social, and cultural implications of AI interaction.
From early concepts to live products.
AIX can be applied to early concepts, live products, prototypes, roadmaps, or emerging technology bets. We use it to structure research, diagnose experience issues, identify product opportunities, and help teams make clearer decisions about how AI should work for humans.
Depending on the question, an AIX engagement may include product immersion, expert review, user research, behavioral analysis, interaction analysis, concept testing, roadmap review, or strategic advisory.
- Where users misunderstand the product
- Where trust is too low, too high, or poorly calibrated
- Where users need more control, feedback, or recovery
- Where the product creates value, friction, or ambiguity
- Where the interaction model needs to change
- Where adoption breaks down
- Where the product creates emotional, social, or behavioral meaning
UX asks how people use a product. AIX asks how people experience an intelligent system.
That includes usability, but it also includes trust, agency, uncertainty, adaptation, delegation, perceived intelligence, emotional response, and long-term relationship with the product.
As AI systems become more capable, the experience layer becomes more complex. AIX is designed for that complexity.
- You are building a new AI product, feature, or interaction model
- You need to understand how users experience an existing AI product
- You are seeing adoption, trust, retention, or comprehension issues
- You are exploring a new modality: agents, voice, multimodal, autonomous products, robotics, wearables, embedded AI
- You need research-led clarity before making a major product or roadmap decision
AI succeeds when people can understand it, trust it, and find meaningful value in using it
AIX helps teams design for that reality.