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AI-Driven Platform Design and Optimization at AT&T

Role: Head Product Designer
Company: AT&T Chief Data Office
Duration: December 2023 – April 2024
Context:  AT&T, a global leader in telecommunications, aimed to leverage artificial intelligence to transform its Ask Platform, enhance the user interface, and implement advanced testing frameworks to optimize user engagement and operational efficiency.

Challenge

AT&T faced specific challenges in its AI-driven initiatives:

• Complex Platform Design: The Ask Platform required a scalable, intuitive UI to drive adoption and deliver seamless user experiences.

• Optimizing AI Models: AI recommendation engines needed enhancements to provide actionable insights and improve decision-making accuracy.

• User Engagement Validation:A lack of structured A/B testing impeded AT&T’s ability to measure the impact of new features effectively.

Approach

AI-Powered Ask Platform Redesign

• Designed and implemented an AI-driven interface for the Ask Platform, enabling real-time natural language query resolution and predictive insights.

• Focused on persona-based design, ensuring the platform catered to diverse user segments, including business analysts, executives, and technical teams.

• Introduced AI-assisted navigation features, reducing query resolution time by 30%.

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ask ATT Team Doc


Advanced A/B Testing Framework

• Built a robust A/B testing infrastructure to validate new feature rollouts across the Ask Platform.

• Collaborated with cross-functional teams to design experiments that analyzed user behavior, engagement, and retention metrics.

• Delivered insights that improved feature adoption rates by 25%, ensuring continuous optimization of AI model performance.

AI Model Training and Enhancement

• Implemented machine learning models for predictive analytics, improving the accuracy of recommendations by 20%.

• Partnered with data scientists to develop feedback loops, enabling AI algorithms to learn from user interactions and refine suggestions.


User-Centric Design and Iteration

• Conducted quantitative and qualitative user research to inform UI/UX decisions.

• Led iterative design cycles with a focus on improving ease of use, accessibility, and engagement.


• Utilized heatmaps and engagement analytics to prioritize interface changes that increased user satisfaction by 18%.


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Results

• AI-Driven User Engagement:
• The redesigned Ask Platform achieved a 40% improvement in query resolution efficiency.
• Real-time insights empowered users to make faster, data-driven decisions, driving $8M in business value annually.

• Improved Feature Validation:
• The A/B testing framework reduced feature deployment time by 15%, ensuring data-backed decision-making for every rollout.
• Feature adoption rates improved by 25%, with over 70% of users reporting enhanced satisfaction.


• Operational Scalability:
• The platform scaled to support 100K+ monthly users, leveraging AI for dynamic workload management.
• Enhanced AI models reduced latency by 20%, ensuring smooth operations for high-demand scenarios.

• Customer-Centric Innovation:
• Delivered an intuitive, AI-powered interface that boosted NPS scores by 25%.
• Increased customer retention rates by 15% through continuous engagement optimization.






Key Learnings

• AI as a Catalyst: Seamless integration of AI can revolutionize platform usability and decision-making efficiency.

• Iterative Testing: A/B testing is crucial for refining AI-driven features and ensuring user-centric designs.


• Collaboration Across Functions: Partnering with cross-functional teams drives innovation and operational success.

Works

Works

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Product, Behind the scene

DIRECTV

Product Enhancements

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Enhanced Decision-Making

AT&T

Business Challenge & Vision

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End of End

Data Quality

Right Information at the Right Time

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AT&T Chief Data Office

AI-Driven Platform

Ask AT&T Platform

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