Empowering Employees with AI
The Product Strategy Behind an Enterprise Internal Assistant
HP Digital & Transformation Office
Project Overview
Senior UX Strategist leading cross-functional product transformation
Context & Challenge
The Context
HP's Digital & Transformation Office launched an internal AI assistant to streamline employee workflows. The goal was to support thousands of employees globally, enabling them to access documents, generate content, and automate repetitive tasks through a secure, generative AI interface.
The Problem
However, post-launch metrics showed low adoption, inconsistent usage patterns, and unclear user satisfaction. I was brought in as a Senior UX Strategist, operating at the intersection of UX, AI capability, and business needs to reshape the assistant into a scalable, intuitive internal product.
Strategic Objectives
My Mission
- Increase adoption and satisfaction of the AI assistant
- Align assistant capabilities with real user needs
- Define a product strategy and vision for scalable AI integration
- Facilitate cross-functional collaboration between engineering, legal, security, and business stakeholders
Strategic Approach
Problem Framing & Discovery
- Conducted 1:1 interviews with employees from multiple departments (Sales, Marketing, HR, R&D)
- Mapped current assistant touchpoints and friction areas using a service blueprint
- Facilitated stakeholder workshops to align on priorities and constraints
- Identified critical Jobs-To-Be-Done (JTBD) the assistant could solve
Experience Audit & Benchmarking
- Audited current flows and UI of the assistant (prompt experience, feedback loop, output clarity)
- Benchmarked against external copilots (Microsoft Copilot, Notion AI) for capabilities and UX
- Defined usability KPIs (completion time, satisfaction, repeated usage)
Strategy & Product Vision
- Created a product roadmap with short-, mid-, and long-term objectives
- Prioritized use cases with high value and technical feasibility
- Proposed a modular AI design, enabling future prompt packs and assistant personas
Collaboration & Delivery
- Led sprint planning sessions with engineering and legal to address AI safety, data access, and security
- Defined product specs and user stories using prompt engineering logic
- Introduced feedback loops via in-product surveys and analytics tracking
Pilot, Learn, Iterate
- Deployed a pilot to a group of 200 employees
- Captured both qualitative and quantitative insights
- Iterated on features such as smart prompt suggestions, persona-based modes, and guided onboarding
User Journey Transformation
Discovery
Employee hears about AI assistant through email, struggles with unclear onboarding, feels confused and skeptical about value.
First Interaction
Opens generic interface, unsure what to ask, gets irrelevant response, feels frustrated and disappointed.
Attempted Usage
Tries multiple queries, doesn't understand output format, no guidance provided, becomes impatient and doubtful.
Abandonment
Returns to familiar tools, tells colleagues "it doesn't work", forgets about assistant, feels resigned.
Guided Discovery
Receives targeted department announcement, sees relevant use cases, starts interactive onboarding, feels curious and optimistic.
Contextual First Use
Selects department persona, uses suggested templates, gets well-formatted relevant output, feels impressed and engaged.
Progressive Learning
Explores other use cases, uses smart suggestions, provides feedback, saves useful patterns, feels confident and productive.
Integration & Advocacy
Integrates into daily workflow, shares success with colleagues, suggests improvements, becomes power user and advocate.
Modular AI Framework
Scalable architecture for enterprise AI assistant deployment across departments
User Interface Layer
Adaptive interface that personalizes based on user department, role, and experience level
Persona Switcher
Toggle between dept-specific modes
Smart Onboarding
Role-based tutorials and examples
Contextual Help
In-app guidance and suggestions
Progressive Disclosure
Advanced features by experience
Prompt Intelligence Layer
Smart prompt engineering and context management to improve AI interactions
Template Library
Pre-built prompts for business tasks
Context Injection
Automatic business context addition
Query Enhancement
AI-powered prompt improvement
Output Formatting
Structured responses by use case
Business Logic Layer
Department-specific workflows, integrations, and business rule enforcement
Workflow Integration
Connect to existing processes
Data Access Controls
Role-based permissions
Business Rules
Department-specific guidelines
Approval Workflows
Multi-step sensitive processes
Learning & Analytics Layer
Continuous improvement through user feedback, usage analytics, and performance monitoring
Usage Analytics
Track adoption patterns
Feedback Loops
In-app ratings and suggestions
A/B Testing
Experiment with variations
Performance Monitoring
Track quality and satisfaction
🎨 UI/UX Transformation
Key Issues:
- No context about user's role
- Generic responses
- No guidance or examples
- No feedback mechanism
Subject: Next Steps for Your Printing Solutions
Hi [Client Name], Thank you for discussing your printing needs...
Improvements:
- Department-specific context
- Smart suggestions
- Actionable responses
- Integrated feedback system
🤝 Stakeholder Alignment Strategy
Navigating complex organizational dynamics to align engineering, legal, security, and business stakeholders
- User adoption & satisfaction
- Product-market fit
- Scalable framework design
- Cross-functional alignment
- Technical feasibility
- Performance & scalability
- Development timelines
- System architecture
- Data privacy regulations
- AI liability & transparency
- Content generation risks
- Vendor agreements
- ROI & productivity gains
- Change management
- Budget constraints
- Employee satisfaction
- Data security & access
- Infrastructure requirements
- Integration complexities
- Audit & monitoring
📈 Business Impact & Results
Quantifiable improvements in user adoption, satisfaction, and organizational AI capability
Beyond quantitative improvements, I defined a scalable AI assistant framework applicable across departments and was recognized internally as a best practice for other AI initiatives in the Digital Office.
Strategic Product Learnings
- Successful AI tools are not just about tech—they require deep user empathy, iterative feedback, and clear guardrails
- Internal tools benefit from persona-driven use cases and smart onboarding, not generic functionality
- Facilitating alignment across security, legal, UX, and engineering is a critical skill in AI product management
- Jobs-To-Be-Done framework is particularly powerful for AI tools where users may not initially understand the value proposition
- Modular AI design enables scalable deployment across diverse organizational needs
Strategic AI Product Leadership
This case study demonstrates my ability to operate at the intersection of user experience, AI capabilities, and business strategy. I specialize in transforming underperforming AI tools into strategic organizational assets through evidence-based product strategy and cross-functional leadership.
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