Technology Experience Research | AI, Automation & Connected Devices

Technology research

Make technology understandable in real use

A technology can offer useful capabilities without making it clear when or how to use them. People may misunderstand a recommendation, struggle to connect a device or be unsure how to correct an automated action. These are experience questions as well as technical ones.

UX Pacific researches how people understand, interact with and respond to technology. We examine the tasks it supports, the expectations it creates and the control available to users. The aim is not to make people trust every output, but to help your team create an experience that supports informed use.

When to research

When technology experience research is useful

Use this study area when exploring a new capability, testing a prototype, preparing a rollout or investigating an experience already in use. It is relevant when users hesitate, misunderstand outputs, abandon setup or rely on workarounds your team did not anticipate.

A small team may need evidence for one interaction. A growing business may need to investigate onboarding and ongoing use. An enterprise may need to understand several roles, workflows or markets. We agree a focused question and practical scope rather than assuming a new technology requires a large study.

What we investigate

Questions we help you answer

Do people understand what the technology does and when it is useful? Can they set it up and complete relevant tasks? Do explanations help them judge an output, or create false confidence? Can they review, reject or correct an action? Do they know when to seek help or use an alternative?

We also explore how use changes with familiarity, task importance, access needs and context. Reported willingness to try something is kept separate from evidence of actual use or sustained adoption.

01

AI assistants and conversational interfaces

Study how users ask questions, interpret responses and recognise what an assistant can or cannot do. Explore follow-up interactions, verification behaviour and the transition to human assistance. User research does not independently establish model accuracy or safety.
02

Automation and decision support

Examine automated steps, recommendations, alerts and approval points. Understand whether people know what happened, what remains their responsibility and how to intervene when an action is unsuitable.
03

Connected devices and self-service technology

Investigate pairing, permissions, controls, notifications and transitions between hardware and software. Kiosks, smart devices and other connected experiences can be studied where the equipment, access and safe test conditions can be arranged.
04

Voice, wearable and immersive interactions

Explore instructions, feedback, task completion and reported comfort in unfamiliar interaction formats. The study design must suit the device, environment and participant needs; feasibility and safeguards are confirmed before fieldwork.

Staying in control

Control, uncertainty and recovery

A useful experience needs more than a successful first attempt. We investigate whether people can identify uncertainty, understand consequences and recover from interruptions or mistakes. This includes undo options, alternative paths, clear status information and appropriate access to help.

Where a scenario involves an incorrect or unexpected output, we agree how to simulate it safely. Findings distinguish the user’s interpretation of an explanation from whether that explanation accurately represents the technology’s underlying operation.

Our approach

Methods matched to the decision

Interviews explore expectations and concerns. Concept testing investigates proposed capabilities, while prototype testing and usability testing examine agreed interactions and tasks.

Contextual observation and diary studies can investigate use in daily routines or work. Surveys and authorised usage records may add breadth or context. We explain whether evidence comes from a concept, simulation, working prototype or live experience, because each supports different conclusions.

From insight to action

From your question to actionable evidence

We begin with the capability, intended users and decision you need to make. We agree tasks, participant criteria, test conditions, technology version and deliverables. For changing or variable outputs, we document relevant configurations and what participants actually encountered.

Findings identify comprehension gaps, interaction barriers and opportunities to support informed use. We discuss priorities and dependencies with your team, separating experience recommendations from technical changes that require specialist evaluation. Follow-up testing can investigate proposed improvements where included.

The output

What your team receives

Agreed outputs can include user expectations, task-level findings, setup and adoption barriers, and evidence about explanations, controls and recovery paths. Prioritised recommendations connect each issue with affected users, supporting evidence and questions that remain unresolved.

A findings discussion helps product, design, engineering and operations teams consider next steps together. The proposal specifies deliverables and any follow-up work. Software development or technical model evaluation is not implied by a research engagement.

Research in context

Different people, roles and markets

Technical familiarity is not the same as familiarity with a specific task. We consider relevant experience levels, access needs, languages and roles, including people who decline or stop using the technology where recruitment permits.

For cross-market questions, we agree common tasks and document differences in language, connectivity, device access and local workflows. We do not assume that one participant group represents an entire market.

Find the right service

Choose the service around your question

Explore Technology Experience Research for a focused study. AI & Automation Experience examines automated and AI-assisted interactions. For a connected product, consider Product Experience; for work across tools and roles, System Experience may be more relevant.

Research principles

Responsible research and clear limits

Participant consent, recording permissions, confidential information and safe test conditions are agreed before the study. Sensitive or consequential tasks may need controlled scenarios, representative data and additional safeguards rather than live actions.

User experience research is not a cybersecurity audit, model benchmark, safety certification or legal compliance assessment. A successful task in a study does not prove technical reliability or long-term adoption. We document the conditions and limits of the evidence rather than guarantee business outcomes.

Before we begin

Frequently asked questions

Can we start before the technology is built?

Yes. Concepts or prototypes can investigate expectations and proposed interactions. Simulated functionality is documented, and findings are not presented as proof of live-system performance.

Will research tell us whether our AI is accurate?

It can show how users interpret, check and act on outputs in the studied tasks. Technical accuracy, robustness and safety require separately designed evaluations by the relevant specialists.

Can you investigate why people stop using a technology?

Yes, where relevant participants and evidence are available. We explore possible contributors such as usefulness, effort, uncertainty and context, without assuming one cause or treating stated intentions as sustained behaviour.

What should we share to start?

Tell us about the technology, intended users, development stage, markets and decision you need to make. Include available prototypes, known difficulties, timing and access restrictions. You do not need to choose a method first.

Technology