UI/UX for AI Experiences

Design AI features that users trust and adopt - clear interaction patterns, grounded outputs, safe workflows, and human-in-the-loop design for copilots, agents, and AI-enabled applications.

AI features change the user experience in fundamental ways: outputs can vary, confidence can be hard to judge, and the system may need clarification or context before it can help. Strong AI UX reduces friction and risk by making intent clear, guiding users to provide the right inputs, setting expectations, and presenting outputs in ways that are actionable. The goal is to design experiences that feel reliable and transparent rather than unpredictable.

LW IT Solutions designs AI-enabled journeys for real operational use. We take an engineering-aware approach: define the user tasks, map the workflow, identify risk points (data sensitivity, hallucination impact, approvals), then design interaction patterns that encourage correct use. Where AI is integrated into business workflows, we incorporate human review points and clear auditability so the organisation can scale AI adoption without compromising quality or compliance.

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Service Overview

Highlights

Workflow-led AI experience design for copilots, agents, and AI-enabled apps
Guided input patterns that help users provide the right context
Output designs that make actions, sources, and uncertainty clear where relevant
Human-in-the-loop design for approvals and quality control
Engineering-aware specifications for states, errors, and edge cases

Business Benefits

Higher adoption through AI journeys that match real user tasks and workflows
Reduced risk by designing review and approval points for business-critical outputs
Better output quality by guiding users to provide the right context and constraints
Lower user friction through clear interaction patterns, error states, and guidance
Improved trust through transparent output presentation, sources, and limitations where appropriate

Typical use cases

Designing a copilot or agent experience for a specific business process
AI features that users avoid due to unpredictable outputs or unclear value
High-risk workflows that need review and approval before actions are taken
Applications where users struggle to provide context or reuse AI outputs
Scaling an AI feature set and needing consistent interaction patterns across products

Objectives & deliverables

What Success Looks Like

Improve adoption by designing AI journeys that fit how teams actually work
Reduce risk by incorporating review/approval steps where AI outputs are business critical
Increase productivity by removing friction from prompt entry, context selection, and output reuse
Improve output quality by guiding users to provide better context and constraints
Increase trust by making limitations, sources, and uncertainty visible where appropriate

What You Get

AI UX discovery outputs: personas/roles, journey maps, and prioritised user tasks
Wireframes / page-level flows for AI-enabled interactions (scope dependent)
AI prompt and interaction templates aligned to your workflows
Risk and control mapping: where review/approval is required and why
UX specification notes for developers: states, errors, guidance, and edge cases
Measurement plan: KPIs and feedback mechanisms to validate success post-launch
Backlog: prioritised improvements and next features based on expected impact

How It Works

Discovery - define roles, tasks, and target workflows; confirm risk posture and success measures.
Design - create AI interaction patterns, guided inputs, and output formats; map review points where required.
Prototype - produce clickable prototype/wireframes for key flows (where appropriate).
Validate - review with stakeholders and representative users; refine flows and controls.
Handover - document designs and provide build-ready specifications for engineers.
Measure - define post-launch measurement and improvement cadence.

Engagement Options

Design Sprint - Short engagement to map journeys and produce initial flows for a single use case
Feature Design - Detailed UX and specifications for an AI feature set within an application
Product Track - Ongoing UX support across multiple AI features with iterative validation

Common Bundles

Customers who use this service often bundle with these services

AI Use Case Discovery & Value Assessment
Structured AI use case discovery to identify, score, and prioritise opportunities, producing a delivery-ready backlog with feasibility and value assessment.

RAG / Chat with Your Data
Build governed RAG chat with your data solutions using secure retrieval, permissions-aware context, and measurable answer quality controls.

Data Strategy & Architecture
Define a clear data strategy and target architecture that aligns platforms, governance, security and cost with measurable business outcomes.

Agentic AI & Orchestrated Workflows
Design and deliver agentic AI workflows with multi-step orchestration, approvals, monitoring, and guardrails for controlled execution across business systems.

Prompt Governance & Approval
Prompt governance and approval services providing lifecycle management, ownership, versioning, audit trails, and controlled change for production AI prompts.

Information Protection & Sensitivity Labels
Design and deploy Microsoft Purview sensitivity labels to classify data, apply protection controls, and support safer collaboration across Microsoft 365.