Exploring the frontiers of AI in business through rigorous research and practical applications
Our research focuses on the intersection of AI technology, business strategy, and ethical governance.
Partnering with executive leadership to shape AI vision and strategy, supporting the deployment of intelligent systems that enhance decision-making, optimize business operations, and accelerate organizational growth and transformation.
Designing strategies, policies, and tools to ensure responsible and effective oversight of AI systems, aligned with organizational goals, risk management practices, and regulatory requirements. In real time.
Developing comprehensive metrics and tools to analyze AI adoption trends, system architectures, operational performance, and risks—enabling continuous assessment and optimization, supporting data-informed decision-making.
Developing metrics and tools to measure workforce participation and productivity, enabling analysis of AI's effects on upskilling, role transformation, and workforce displacement. Supporting strategic workforce planning and human capital development.
Analyzing the growing significance of semantic data methodologies and tools in constructing advanced AI pipelines. Optimizing data classification and data quality initiatives to establish more reliable and accurate foundations for AI systems.
Providing insights to AI system architects and business management on addressing unique sector challenges and opportunities. Supporting the development of purpose-built AI solutions.
Explore our ongoing research initiatives and collaborative projects.
As artificial intelligence systems grow in scale, complexity, and capability, the question of how these systems can be effectively governed is increasingly urgent. This project is developing a cross-sector analysis of how industry and public sector leaders are confronting the challenges of developing effective governance policies and ensuring accountability in frontier AI development.
This project is the first installment of a larger effort to design an empirically oriented research program tracking AI, asking the first question, what AI can we track? Our initial effort will focus on developing measures and frameworks suitable for tracking AI technology adoption and usage in two heavily regulated industries, financial services and pharmaceutical, the latter in drug discovery and development.
This project investigates the advancement of semantic techniques in data engineering, with a focus on improving data classification accuracy and ensuring higher data quality standards. It explores the use of ontologies, knowledge graphs, and context-aware metadata to enable more meaningful classification of structured and unstructured data across heterogeneous sources. By incorporating domain semantics, the project aims to move beyond keyword-based or syntactic methods, enabling systems to understand the intent, relationships, and hierarchy of data elements.
This project explores how AI is reshaping storage system strategies and architectures through innovations such as predictive workload modeling, automated data tiering, semantic locality-aware data placement, and self-optimizing storage frameworks. A key dimension of this transformation lies in AI-enhanced storage management, particularly in optimizing data retention strategies and media selection, leading to more cost-effective and sustainable storage strategies.
Interested in partnering on research or accessing our findings? Join our consortium to collaborate on cutting-edge AI research.
Become a Research Partner