Philip Miller

Director of Product Marketing & AI Strategist

Progress Software

Bio Overview

 

Philip Miller is Director of Product Marketing and AI Strategist at Progress Software, where he helps shape the strategy, positioning and adoption of AI- and data-driven solutions. With more than 20 years of experience in the technology industry, he specializes in enterprise AI, data platforms, AI governance and digital transformation. Philip is a frequent contributor to Progress blogs, webinars and community programs, translating complex AI concepts into practical guidance that helps organizations build trusted, scalable AI initiatives.

At Progress, Philip leads product marketing efforts for Progress Data Platform and helps organizations move from AI experimentation to real-world business outcomes. His work focuses on AI strategy, data platforms, semantic technologies, retrieval-augmented generation (RAG), AI governance and enterprise-scale adoption. He collaborates across product, engineering, sales and partner teams to develop messaging, thought leadership and go-to-market strategies that help customers unlock value from their data while building AI systems that are accurate, trusted and governed.

Prior to his current leadership roles, Philip served as Senior Product Marketing Manager and Senior Customer Success Manager at Progress, where he worked closely with customers and partners to solve complex data challenges and maximize the value of MarkLogic and related technologies. His experience spans customer advocacy, product marketing, community building, webinar leadership and enterprise technology adoption. 

Philip is an active writer and speaker on topics including AI governance, enterprise AI, data management, semantic technologies and digital transformation. Through Progress blogs, webinars, podcasts and industry discussions, he helps business and technology leaders understand how to implement AI responsibly and effectively. Outside of work, he is a lifelong learner, father of two daughters and dog enthusiast.

Areas of Expertise

  • Enterprise AI Strategy
  • AI Governance & Responsible AI
  • Retrieval-Augmented Generation (RAG)
  • Agentic AI
  • Data Platforms & Data Management
  • Semantic Technologies & Knowledge Graphs
  • Product Marketing Strategy
  • Go-to-Market (GTM) Execution
  • Digital Transformation
  • Customer Advocacy & Community Building

Credentials & Publications

  • Director of Product Marketing, Progress Software
  • AI Strategist, Progress Software
  • More than 20 years of experience in the technology industry
  • Contributor to Progress blogs covering AI, governance, data platforms and enterprise technology topics
  • Presenter and contributor to webinars and community programs focused on AI and data technologies
  • Recognized as a top influencer in data management by Onalytica
  • Co-host/contributor to Progress podcast initiatives and thought leadership programs, including Friday AI with Phil

Connect with Me

 

Articles by the Author

Governed AI system with connected responsible AI controls, a human reviewer and a smart city backdrop.
AI Ethics and Governance - A Practical Guide
This blog explains the difference between AI ethics and AI governance, showing how responsible AI principles such as fairness, transparency, privacy, safety, accountability, and human oversight can be translated into practical policies, controls, workflows, and evidence. It outlines why governance matters as AI adoption grows, how organizations can manage risks, and how a structured approach helps teams build AI systems that are trusted, explainable, auditable and aligned with both enterprise goals and broader public-good outcomes.
Data & AI Progress Data Platform
Philip Miller May 18, 2026
Blog Post Thumbnail Image
ISO 20022 Is Entering Its Next Phase: How Payment Providers Can Prepare for CHAPS 2027
With CHAPS 2027 ahead, the real opportunity for payment providers is not just meeting ISO 20022 requirements, but building the data foundation needed to use richer payment information with confidence.
Data & AI Progress Data Platform
Philip Miller April 22, 2026
Man in a denim shirt looks at a smartphone while holding a credit card, suggesting an online payment or shopping transaction.
AI Pricing Isn’t Broken. Your Context Might Be.
AI pricing is increasing, but the real problem is token waste. Enterprises are overspending because poor data architecture forces models to process too much irrelevant context. Better retrieval, semantic enrichment, rules, and governance reduce cost, improve accuracy, and make AI more scalable.
Data & AI
Philip Miller April 13, 2026
Guy looking at screen with woman sitting and thinking
Friday AI with Phil: Why Reliable AI Matters More Than Flashy Demos
AI success in the enterprise is no longer about how powerful it looks in demos, but whether it can be trusted to operate reliably, transparently and at scale within real business workflows. Organizations that win will be those that prioritize governance, context and repeatability to turn AI from hype into dependable infrastructure that supports real decisions.
Data & AI Progress Data Platform
Philip Miller March 25, 2026
Person working on a laptop with floating AI and security icons over a blue geometric background with green accents.
Why “Boring AI” Is the Key to Scaling Trusted Enterprise AI
Trust is now the differentiator: AI capability is rising fast, but enterprise adoption depends on governance, explainability and control. User-first beats tool-first: The winning model is bringing AI into the flow of work, not forcing people to learn complex tooling. Boring is what scales: Predictable, policy-aligned and auditable AI is what turns pilots into production outcomes.
Data & AI
Philip Miller March 12, 2026
Abstract digital illustration of a cloud with a padlock icon, surrounded by glowing circuit lines—representing AI and Data governance.
AI Fines Have Started. Now What?
AI fines are no longer theoretical; regulators are now enforcing control requirements as AI moves from pilots to production in financial services. The post explains why contracts alone won’t satisfy supervisors, what evidence regulators will expect to see in production and how organizations can operationalize governed, defensible AI with runtime guardrails, provenance, lineage and auditable controls—setting the agenda for the RegTech Conference in London on March 26.
Data & AI
Philip Miller March 09, 2026
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