Bio

Maryam Haghighi is Director of Intelligent Systems and Innovation at the Bank of Canada. She leads an enterprise-wide portfolio spanning artificial intelligence, advanced analytics, data and compute platforms, digital infrastructure, and emerging technologies. She shapes the Bank's strategic direction in these areas and works across the institution to translate technological advances into secure, responsible, and scalable capabilities that strengthen decision-making, operational resilience, and the delivery of the Bank's mandate.

Ms. Haghighi also serves as Co-Chair of the G7 Central Bank Quantum Technologies Working Group, where she advances international collaboration and strategic preparedness for the opportunities and risks associated with quantum technologies. Her work contributes to strengthening technological readiness and resilience across central banks and the broader financial system, while helping shape global dialogue on the future of emerging technologies in financial services.

A recognized leader in artificial intelligence, data, and digital transformation, Ms. Haghighi previously served as the Bank's Director of Data Science, where she established the Bank's Data Science Hub and advanced its responsible AI governance approach. She also played a leading role in developing the Bank's quantum strategy and in building enterprise capabilities that enable innovation at scale. Throughout her career, she has led multidisciplinary teams, delivered complex transformation initiatives, and translated emerging technologies into practical solutions across highly regulated sectors.

Ms. Haghighi holds a PhD in Mathematics from the University of Ottawa and is a Chartered Director (C.Dir.). Her work sits at the intersection of technology, governance, and public policy, advancing responsible innovation and strengthening institutional resilience.


Staff research

Monetary Policy in an AI-Driven Two-Speed Economy

Staff working paper 2026-27 Joshua Brault, Maryam Haghighi, Jing Yang
We analyze monetary policy responses to AI in a two-sector New Keynesian model, distinguishing augmentation and automation. Both reduce labor demand, requiring accommodation that creates inflation trade-offs. Automation worsens them. Aggregate inflation depends on AI’s form and breadth, making policy stabilization more complex and aggregate data potentially misleading.

Integrating Non-traditional Data and AI into Central Banking: A Canadian Perspective

This paper reviews how central banks are integrating non traditional data and artificial intelligence (AI) into policy analysis and operations. Using the Bank of Canada’s experience, it examines emerging applications, governance challenges, and strategic choices for responsibly scaling AI to enhance insight, efficiency, and institutional resilience.

AI Paradox: Promise vs. Reality—What It Means for Monetary Policy

Staff analytical paper 2026-4 Joshua Brault, Maryam Haghighi, Jing Yang
This note reviews the emerging evidence on AI’s labour-market and productivity effects, highlighting early task-level impacts, sizable micro level productivity gains, and the macroeconomic challenges these pose for monetary policy during the transition.

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