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About
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 Staff 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.
Thai Bao An Phan
Published content
expert panel
No longer confined to analytics dashboards and recommendation engines, AI systems are now initiating transactions, approving workflows, flagging anomalies and even orchestrating other software agents. With this sudden increase in autonomy, business leaders are left asking: Where should humans step back—and where must they stay firmly in control? According to a 2025 McKinsey survey on the state of AI, nearly nine out of 10 organizations now report using AI in at least one business function, yet most are still early in scaling these technologies and many lack robust governance and risk controls. As artificial intelligence advances from advisory tools to agentic systems capable of multi-step planning and execution, the leadership challenge shifts: defining not just what AI can do, but what it should do. Members of the Senior Executive AI Think Tank—a curated group of experts in machine learning, generative AI and enterprise-scale transformation—argue that the real issue isn’t capability but accountability. Across their industry expertise, they all converge on one theme: The boundary between human judgment and machine decision-making must be dynamic, evidence-based and anchored in responsibility. Here is how they recommend drawing—and redrawing—that line.
expert panel
Internal AI assistants are quickly becoming the connective tissue of modern enterprises, answering employee questions, accelerating sales cycles and guiding operational decisions. Yet as adoption grows, a quiet risk is emerging: AI systems are only as reliable as the knowledge they consume. Members of the Senior Executive AI Think Tank—a curated group of leaders working at the forefront of enterprise AI—warn that many organizations are underestimating the complexity of managing proprietary knowledge at scale. While executives often focus on model selection or vendor strategy, accuracy failures more often stem from outdated documents, weak governance and unclear ownership of information. Research from MIT Sloan Management Review shows that generative AI tools often produce biased or inaccurate outputs because they rely on vast, unvetted datasets and that most responsible‑AI programs aren’t yet equipped to mitigate these risks—reinforcing the need for disciplined, enterprise level knowledge governance. As organizations move from experimentation to production, Think Tank members offer key strategies for rethinking how knowledge is curated, validated and secured—without institutionalizing misinformation at machine speed.