Thai Bao An Phan
Enterprise AI Transformation Architect | Bridging Technology and Business to Build Scalable, High-Impact AI Solutions for Enterprises
New York City, New York, United States
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Annie Phan is a data and AI leader transforming how modern enterprises scale impact through data analytics, AI/ML innovation, and digital operations.
At Diligent Corporation, she serves as Head AI Solution Architect, driving enterprise-wide AI transformation across Sales, Marketing, Legal, and Finance. Annie leads the design and deployment of secure, AI-powered solutions built to automate complex knowledge retrieval and workflows. Her work focuses on architecting scalable AI stacks, ensuring responsible deployment within a governance, risk, and compliance (GRC) context, and integrating AI into core enterprise systems to drive measurable business outcomes.
At Fanatics Collectibles, she led one of the company’s most ambitious digital transformation programs, owning product strategy and delivery for dozens of AI/ML applications supporting hundreds of users across design, production, and business operations. Her work spans cross-functional stakeholder management, agile delivery, generative AI onboarding tooling, and KPI design—driving widespread adoption and lasting operational change.
Previously, Annie served as a Data & AI expert at McKinsey & Company, helping clients in real estate, healthcare, and consumer sectors unlock substantial business value. She led McKinsey’s Advanced Industries AI & Analytics Squad, building capabilities across hundreds of client engagements, and drove global go-to-market execution for Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI, reaching thousands of executives across dozens of countries.
Annie is a Founding Member of the AI Think Tank, an international community advancing AI thought leadership and innovation, a member of the Forbes Business Council, recognized for her contributions to technology leadership, and a member of Hackathon Raptors—a prestigious community of seasoned experts and leaders in the field of technology and AI dedicated to advancing innovation and impactful solutions. She is a published thought leader with articles on data talent, analytics translation, governance, and AI-driven change management featured in Forbes, HackerNoon, and DZone. Annie serves as a judge for global tech awards, including Globee Awards and Business Intelligence Group, and speaks frequently on data and AI strategy at industry events and universities, including Brown University.
Annie holds a Master’s in Data Science and a Bachelor’s in Economics & Development Studies from Brown University, graduating magna cum laude. She is fluent in English, Vietnamese, and Spanish.
Area of Expertise
Topics
How Engineering Leaders Can Deploy and Scale AI Enterprise Solutions for Sustainable Impact
Countless talks and guides exist on building AI enterprise solutions (including AI agents, generative AI frameworks, AI-powered analytics platforms, and intelligent automation tools), designing infrastructure, and ideating use cases. Yet few address a key challenge faced by engineering leaders and developers—how to reliably deploy and scale these solutions over time across complex, multi-unit organizations, distributed geographies, and teams with diverse AI expertise. Despite abundant engineering talent, rapidly evolving AI tools, and large investments, the core challenge remains: moving solutions from pilots into production environments where they sustain impact for thousands of users.
For many engineering organizations, especially within global enterprises, multi-unit companies, or specialized SMBs, the initial build phase is frequently under control. However, operationalizing AI enterprise solutions at scale is a nuanced challenge, requiring scalable, resilient architectures, smooth integration with business and development workflows, continuous performance management, and tailoring to varied operational tempos and AI maturity levels.
This session goes beyond surface-level thought leadership or conventional best practices for engineering leaders and developers. It provides practical, actionable technical guidance tailored to the realities and responsibilities of those leading AI deployments. Designed for engineering managers, developer leads, product owners, and C-suite technology executives navigating AI rollouts across complex enterprises, it addresses the coordination of diverse teams and skills distributed over multiple units and regions.
Key Topics Covered
- Getting Buy-In and Driving Initial Usage: Strategies and frameworks to secure organizational buy-in and catalyze early adoption across engineering, product, and operational teams. This includes build vs. buy decision-making, embedding AI solutions into core workflows, and managing change to overcome resistance and skill gaps through tailored training.
- Scaling Adoption Across Business Units and Geographies: Technical playbooks and architectural patterns to extend AI solutions beyond pilots into enterprise-wide deployment. Key considerations include workflow standardization, robust governance, security compliance, and fostering collaboration across distributed teams to maintain solution reliability at scale.
- Performance Measurement and ROI Tracking: Approaches for monitoring AI adoption, solution effectiveness, and quantifiable business impact with KPIs that resonate with engineering teams. Emphasizes continuous reliability monitoring, compliance, and evolving operational metrics well beyond initial demo success.
Success Storytelling: Documenting and Communicating Wins to Sustain Support:
- Best practices for capturing and presenting AI deployment successes internally and externally to galvanize executive sponsors, stakeholders, and clients. Effective storytelling secures sustained investment and momentum to grow AI initiatives at scale.
Attendees will gain tangible frameworks, blueprints, and insights vital for deploying, scaling, and optimizing AI enterprise solutions in today’s complex engineering environments. This session arms engineering leadership with the tools to drive lasting AI impact, balancing innovation with operational excellence.
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