Measurement

Beyond implementation: A practical framework for measuring AI adoption success [Outlook of AI Evolution]

June 30, 2026 by Bhumika Shah

Organizations worldwide are accelerating their investment in artificial intelligence (AI), yet many struggle to define what success actually looks like once a system goes live. This article introduces a practical, four-dimensional framework for measuring true AI adoption, covering workflow efficiency, decision quality, cultural adoption, and responsible AI practices. Grounded … [Read more...] about Beyond implementation: A practical framework for measuring AI adoption success [Outlook of AI Evolution]

Insights on entrepreneurship education and mentoring programs

May 1, 2021 by B. Ravi

Many engineers are drawn to technologybased start-up ventures for professional and personal fulfillment. Success in entrepreneurship, however, is not easy. A survey conducted at the Indian Institute of Technology (IIT) Bombay of 326 start-up founders, aspiring entrepreneurs, mentors, and other stakeholders showed a critical need for training in business finance, design … [Read more...] about Insights on entrepreneurship education and mentoring programs

Privacy-preserving distributed learning: Techniques, applications, and future challenges

The rapid increase in data generated by connected devices has created a pressing need for privacy-preserving techniques in distributed learning. This article examines methods that enable collaborative machine learning (ML) while maintaining data security and user privacy. Key approaches such as federated learning (FL), differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) are analyzed for their unique capabilities and various applications. FL facilitates model training across decentralized data sources, ensuring data remain local, while DP mitigates privacy risks by adding controlled noise. SMPC and HE support secure computations on encrypted data, maintaining confidentiality during processing. Despite their effectiveness, these techniques face challenges related to computational complexity, scalability, and regulatory compliance. The article reviews current advancements, practical implementations, and future directions, emphasizing the need for optimized, accessible solutions to enhance data security in distributed systems.

Measurement

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