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As generative AI accelerates across enterprises, the challenge has shifted from experimentation to execution. Many organizations can demo large language models, but far fewer can deploy them safely, scalably, and in a way that actually changes how work gets done. The gap is no longer about access to models. It is about data architecture, governance, and translating messy business realities into systems that can be trusted in production.
Annie Phan, an expert data strategist, Head of AI Solutions Architecture at Diligent and a Judge at AI Forge, operates precisely in that gap. Her work focuses on building the intelligence layer that allows GenAI and machine learning systems to function inside real enterprises, particularly across HR and go-to-market domains where decisions, compliance, and human impact intersect.
“Enterprise AI fails when it is treated as a feature,” Phan explains. “It only succeeds when it is designed as infrastructure, with data, governance, and adoption planned from the start.”
At Diligent, Phan leads internal AI solutioning within the Business Applications and Analytics organization. Her role spans the full lifecycle of enterprise AI deployment, from strategy and data architecture through orchestration, deployment, and measurement. Rather than focusing on isolated use cases, she works across Sales, Marketing, Customer Success, and People Analytics to identify where AI can meaningfully enhance productivity and decision-making.
A core part of her work involves architecting retrieval-augmented generation and LLM-driven systems that integrate enterprise data pipelines, vector databases, and model APIs. These systems are designed to support personalization, reasoning, and workflow automation while remaining secure and compliant by design. Phan partners closely with engineering and security teams to ensure models are productionized with guardrails, observability, and governance baked in.
Her approach reflects a broader philosophy. AI systems must adapt to organizational reality, not the other way around. That means translating business pain points into technical requirements through close collaboration with HR, Sales, and Marketing leaders, then iterating based on real-world feedback and telemetry—a dynamic she examines in her Forbes articles: More than Metrics: How to Cultivate Data Talent in 2025 and Designing A Healthcare Data Strategy That Actually Moves Margin, on how data strategy and talent decisions shape whether AI delivers lasting business impact.
Phan’s emphasis on structure and orchestration was shaped earlier in her career, notably during her tenure as Program Management Lead at Fanatics. There, she led one of the company’s most ambitious internal transformation efforts: the New Card Factory initiative.
Trading card production at Fanatics faced a familiar enterprise problem. A complex, multi-stage process relied heavily on manual workflows, offline documents, and fragmented data sources. As demand grew and new sports categories such as NFL and NBA were introduced, the existing system became a bottleneck. Delays, errors, and limited visibility threatened scalability.
The New Card Factory was designed as a centralized, integrated suite of applications that became the digital backbone of end-to-end card production. Built natively in and connected to enterprise platforms such as Adobe Workfront, the system introduced workflow management from product ideation through vendor delivery. It unified product descriptions, bills of materials, inventory assessments, printing workflows, and autograph and relic processes into a single source of truth.
Phan led the program management office overseeing this transformation. She coordinated cross-functional teams, maintained leadership communication, managed change management strategy, tracked KPIs, and ensured delivery timelines stayed aligned with business priorities. Her role was not simply operational. It was architectural, connecting people, process, and systems into a coherent whole.
The impact was structural. The initiative aimed to reduce the “idea to vendor” timeline by up to 40%, enable long-range production planning, and support more than 500 users across product, manufacturing, and operations. It represented the first system of its kind in the trading card industry, replacing manual processes that competitors still relied on.
The throughline in Phan’s work, from Fanatics to Diligent, is a focus on governance and operating models rather than isolated technologies. That perspective is articulated most fully in her recent book, AI Maturity Mandate, which examines how organizations align leadership, operating models, and data foundations as AI moves from experimentation into enterprise reality. The book argues that AI maturity is not defined by model sophistication, but by whether organizations can operate intelligence responsibly at scale.
Her expertise has also been recognized internationally. She was a keynote speaker at the 1st Artificial Intelligence for Development (AI4D) Conference, and the VIII International Scientific Congress Society of Ambient Intelligence 2025 Conference, contributing her perspective on evaluating AI systems in complex, real-world contexts.
“AI maturity is not about how advanced your models are,” Phan says. “It is about whether your organization knows how to operate them responsibly.”
Today, as enterprises rush to embed GenAI into HR, sales, and customer-facing workflows, Phan’s work highlights a quieter truth. The hardest part of AI is not intelligence, but integration. Systems must align with how organizations actually function, comply with governance requirements, and earn trust from the people who use them.
By focusing on architecture, data strategy, and adoption rather than novelty, Annie Phan has helped turn AI from an aspirational concept into an operational capability. In an age where many AI initiatives stall after pilot phases, her work, from leading internal transformation efforts at Diligent to her external industry recognition as Judge for Globee Awards for Impact, Business, Leadership, Impact and Agent Connect demonstrates that sustainable impact comes from building the invisible structures that let intelligence scale.
As enterprises continue to navigate this transition, leaders like Phan are shaping not just what AI can do, but how it responsibly becomes part of everyday work.
