Tricia Diamond
Director/Founder of Diamond PMO Solutions | Speaker (AI, Portfolio and Program Management, Professional Development, Heritage Management)
Directeur/Oprichter van Diamond PMO Solutions | Spreker (AI, Portfolio- en Programmamanagement, Professionele Ontwikkeling, Erfgoedbeheer)
Seattle, Washington, United States
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Dr. Tricia Diamond is a portfolio and program management executive, aerospace engineer, and organizational strategist with more than two decades of leadership across the public sector, technology, and consulting in the United States and the Netherlands. She holds a PhD in Aerospace Engineering and professional aviation experience, alongside advanced credentials including the PMP®, PMI-ACP®, PMO-CP®, CPMAI, GPM-b®, CAL®, ITIL®, AWS-CCP, AWS-AIP, MCASE. As Director of ARPA Implementation for a major U.S. city, she built and led a PMO governing a $386M federal recovery portfolio with 100% regulatory compliance. She has spoken at ASPA and PMI conferences, previously served as VP of Professional Development for PMI Puget Sound, and is a Seattle Parks and Recreation Commissioner. She is the founder of Diamond PMO Solutions, an MWBE owned management consultancy specializing in portfolio governance, PMO design, and organizational strategy as well as PMI certification trainings.
Dr. Tricia Diamond is een uitvoerend leider op het gebied van portfolio- en programmamanagement, luchtvaartingenieur en organisatiestrateg met meer dan twee decennia aan leiderschapservaring in de publieke sector, technologie en adviesverlening in de Verenigde Staten en Nederland. Zij heeft een doctoraat in de luchtvaartechniek en professionele vliegervaring, naast geavanceerde certificeringen waaronder PMP®, PMI-ACP®, PMO-CP®, CPMAI, GPM-b®, CAL®, ITIL®, AWS-CCP, AWS-AIP en MCASE. Als directeur ARPA-implementatie voor een grote Amerikaanse stad heeft zij een PMO opgericht en geleid dat toezicht hield op een federaal herstelportfolio ter waarde van $386 miljoen, met 100% naleving van alle regelgeving. Zij heeft gesproken op ASPA- en PMI-conferenties, was eerder vicevoorzitter Professionele Ontwikkeling bij PMI Puget Sound en is commissaris bij Seattle Parks and Recreation. Zij is de oprichter van Diamond PMO Solutions, een door een vrouw en minderheid geleid managementadviesbureau gespecialiseerd in portfoliogovernance, PMO-ontwerp en organisatiestrategie, evenals PMI-certificeringstrainingen.
Area of Expertise
Topics
When Your Product Fails, Who Pays? Accountability in High-Stakes Tech Delivery
The product management community has developed sophisticated frameworks for discovery, delivery, and iteration. It has a much thinner vocabulary for what happens when a product fails in an environment where the users cannot simply churn, the organisation cannot simply pivot, and the consequences of a bad release are not a dip in engagement metrics but a disruption to someone’s housing, healthcare, or livelihood.
Technology is no longer confined to environments where failure is recoverable by default. Government services, healthcare platforms, financial systems, public infrastructure, and community programmes run on software built by product teams who often have no framework for reasoning about the accountability structure that surrounds their decisions. That gap is becoming increasingly consequential as technology moves deeper into the systems people depend on.
Dr. Tricia Diamond has built and led technology-enabled programme delivery in public sector environments where product decisions carry regulatory, legal, and community consequences. She offers product managers, developers, and technology leaders a direct, practical framework for thinking about accountability in high-stakes delivery contexts: what it means structurally, how it changes the product decisions you make, and how to build it into your team’s operating model without sacrificing the speed and adaptability that make product teams effective.
Key Takeaways for Attendees
• A clear framework for identifying when a product is operating in a high-stakes accountability context versus a standard commercial one, and why the distinction changes your entire approach to risk, requirements, and release decisions.
• How to structure product documentation, decision traceability, and stakeholder communication to withstand scrutiny from regulators, auditors, executives, and the public without turning your team into a compliance function.
• The product management anti-patterns that are harmless in low-stakes environments but catastrophic in high-consequence ones, including move-fast cultures, undocumented pivots, and stakeholder-light discovery.
• Practical techniques for building accountability into product team culture in a way that strengthens rather than slows delivery, drawn from real programme environments where the cost of unaccountable product decisions was measured in public harm.
• How product leaders can develop their own accountability fluency as a career differentiator in an industry that is increasingly being asked by governments, regulators, and the public to justify its decisions.
Why This Matters Now
Tech Fuse attendees are practitioners navigating an industry that is simultaneously gaining more power over people’s lives and facing more scrutiny than it ever has. This session does not moralize about that reality. It gives product and technology leaders the concrete tools to operate in it responsibly and confidently, from someone who has done it at scale in one of the most scrutinised spending programmes in recent U.S. history.
The Accountability Gap: Why AI Governance Fails Before the Breach Occurs
The AI risk conversation in enterprise security is dominated by two categories of threat: external adversaries exploiting AI, and model-level failures such as bias, hallucination, and data leakage. Both are real. Neither is the governance failure that causes the most damage in high-stakes project, program and portfolio environments.
The most consequential AI risk in organizations today is internal and organizational: no human in the workflow has clear, documented, enforceable accountability for verifying what the AI produced before it became a decision. By the time the error surfaces, it has already propagated through compliance documentation, resource allocations, or regulatory filings. The breach did not come from outside. It was built into the process from the start.
Dr. Tricia Diamond draws on her experience directing a $386 million ARPA Implementation PMO, where AI-assisted tools were deployed in a federally audited environment with zero tolerance for undetected error, to present a practitioner's framework for closing the accountability gap before it becomes a liability. This session examines the specific organizational conditions under which AI errors compound undetected, the human-in-the-loop validation architecture that actually works under operational pressure, and the documentation and traceability practices thatmake AI-assisted decisions defensible when the regulator or the auditor arrives.
This is not a theoretical framework. It is a field account of AI governance built and operated under real federal scrutiny, with real consequences for failure, in an environment where the cost of an undetected error was measured in potential clawback of public funds and community harm.
Learning Objectives are
Identify three organizational conditions that allow AI errors to compound undetected in enterprise environments, and the governance interventions that interrupt each one before they become a security or compliance event.
Design human-in-the-loop validation checkpoints that function under operational pressure rather than creating the appearance of oversight while the process moves too fast for genuine verification.
Build the documentation and traceability architecture that makes AI-assisted decisions auditable, defensible, and correctable after the fact by satisfying both internal risk management requirements and external regulatory scrutiny.
When AI Gets It Wrong and Nobody Notices: Governance, Accountability, and the Cost of Unaccountable
Conference Alignment
This session addresses the conference’s Ethical AI and Governance track with a case-study-grounded focus on bias, transparency, accountability, and the organisational conditions that allow AI errors to compound undetected in environments where the consequences are measured not in metrics but in community harm and regulatory exposure.
Problem Statement
Most AI governance discourse focuses on model-level risks: bias in training data, lack of explainability, regulatory non-compliance. These are real problems. However, the governance failures that cause the most damage in practice are not model failures — they are organisational failures. They happen when no human in the process has clear accountability for verifying AI outputs, when the speed of AI-assisted analysis creates institutional pressure to skip the validation step, and when the systems that should catch errors are themselves partially automated. In high-stakes environments — government programmes, healthcare systems, public infrastructure, financial services — these organisational governance gaps are not hypothetical. They are operational realities with measurable human costs.
Session Description
When a federal programme deploys AI-assisted tools to analyse eligibility, prioritise allocations, or generate compliance documentation, the AI does not bear the consequences if the output is wrong. The programme director does. The community does. The regulator does. The accountability gap between what AI produces and who answers for it is not a technical problem. It is a governance problem and it requires a governance solution.
Dr. Tricia Diamond draws on her experience directing a $386 million ARPA Implementation PMO where every allocation decision was subject to federal audit, every requirement was traceable to a community outcome, and the cost of an undetected error was measured in potential clawback of public funds to present a practitioner’s framework for AI governance in high-stakes programme environments. This session examines the specific conditions under which AI errors go undetected in organisational workflows, the governance structures that prevent them, and the accountability architecture that makes AI-assisted decision-making defensible under scrutiny.
This is not a theoretical session about responsible AI. It is a direct account of what governance looks like when the stakes are real, the scrutiny is constant, and the humans in the loop must be able to explain every decision to a federal auditor.
Key Takeaways
• The three organisational conditions that allow AI errors to compound undetected in high-stakes programme environments, and the governance structures that interrupt each one.
• How to design human-in-the-loop validation checkpoints that are genuinely effective rather than performative, including the accountability assignment that makes them function under operational pressure.
• A practical AI governance framework drawn from federal programme management practice that is transferable to any high-stakes delivery environment — healthcare, finance, government services, infrastructure.
• How to build the documentation and traceability practices that make AI-assisted decisions auditable, defensible, and correctable after the fact.
• Why the accountability gap between AI output and human responsibility is the most consequential and least addressed dimension of AI governance in organisations today, and what closing it actually requires.
How This Session Aligns With the Conference Theme
The conference’s Ethical AI and Governance track is explicitly seeking content on bias mitigation, regulatory frameworks, privacy, and transparent AI systems. This session contributes a practitioner-level case study perspective that is rare in AI governance discourse: not a policy researcher or a vendor, but a programme director who built and operated AI-assisted governance systems in a federally scrutinised environment and can speak directly to what worked, what failed, and what the accountability architecture actually needs to look like.
Intended Audience and Prerequisites
Advanced. Intended for senior leaders, programme directors, governance professionals, and policy practitioners who are responsible for AI adoption decisions in environments where errors carry regulatory, financial, or community consequences. Assumes basic familiarity with AI concepts and organisational governance frameworks.
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