Insights & Research

Insights before everyone else.

116 essays, interviews and field notes from the Council — alongside AIM Research reports written for the people who have to make the decision, not just read about it.

Leadership Moves· Apr 3, 2025

A New Chapter for AI at Verizon with Mano Mannoochahr

Verizon has appointed Mano Mannoochahr as Chief Data, Analytics & AI Officer to accelerate AI adoption and integrate data-driven decision-making into the company's core operations.

By Aashi Agarwal

Leadership Moves· Apr 3, 2025

From Visa to Zopa: Jeremy Penzer Takes Charge of Analytics

Zopa Bank appoints Jeremy Penzer as Chief Analytics Officer to enhance its data-driven banking strategies and expand its range of financial products.

By Aashi Agarwal

Council Posts· Mar 31, 2025

Preparing Your Product for a World of AI Agents Acting Autonomously

To thrive in the era of autonomous AI agents, products must evolve beyond human-centric design. They should embrace machine-readable interfaces, adaptive feedback loops, and new success metrics to support intelligent and goal-driven automation.

By Devendra Singh Parmar

Council Posts· Mar 29, 2025

Quantum Machine Learning: The Next Frontier

Quantum Machine Learning (QML) merges quantum computing and AI, offering innovative solutions to complex data challenges and presenting opportunities for significant economic impact, particularly in fields like finance and drug discovery.

By Gurbans Chatwal

Council Posts· Mar 28, 2025

Navigating AI Governance in the Generative AI Era

As generative AI reshapes industries, a recent AIM Leaders Council roundtable brought experts together to tackle the complexities of AI governance, balancing innovation with responsible adoption in a rapidly evolving regulatory landscape.

By Anshika Mathews

Council Posts· Mar 28, 2025

The Need for Machine Unlearning in Enterprise AI Applications

Machine unlearning enables AI systems to selectively ‘forget' specific data, ensuring compliance with privacy regulations while enhancing efficiency in enterprise applications.

By Rajan Gupta

Council Posts· Mar 28, 2025

AI Agents: The Jarvis Dilemma – Build or Use AI Agent Framework?

The evolution of AI agents presents a dilemma for developers: build a custom solution for complete control or harness pre-built frameworks for quicker development.

By Prashant Ramappa

Council Posts· Mar 28, 2025

Scaling GenAI in Your Business Domain

Scaling GenAI in a business is a journey that requires strategic focus, adaptable governance, and solid data management to unlock its transformative potential.

By Aroon Jham

Council Posts· Mar 28, 2025

From Black Box to Glass Box: Embedding Explainable AI in Business Operations

Integrating Explainable AI into enterprise frameworks enhances transparency, trust, and accountability in automated decision-making processes, crucial for compliance and ethical standards.

By Arjun Srinivasan

Council Posts· Mar 26, 2025

DeepSeek vs. ChatGPT: Choosing the Right AI for Your Business

Choosing between DeepSeek and ChatGPT comes down to priorities—DeepSeek is a powerhouse for technical tasks, while ChatGPT shines in versatility and ease of use.

By Junaid Farooq

Council Posts· Mar 24, 2025

MT Logan Capital Management Welcomes Sun Siang Liew as Chief Analytics Officer

MT Logan Capital Management appoints Sun Siang Liew as their chief analytics officer.

By Aashi Agarwal

Leadership Moves· Mar 20, 2025

Joe Vellaiparambil Joins Allied World to Strengthen Data and Analytics Strategy

Joe Vellaiparambil joins Allied World as VP of Data and Analytics, bringing his expertise to enhance data-driven decision-making and innovation in the insurance industry.

By Aashi Agarwal

AIM Research

Analyst reports, in members' hands first.

Council members review findings before publication, brief the analysts on what to ask next, and receive every premium report as part of membership.

What we cover.

Strategy & operating models

How AI teams are structured, funded and governed inside large enterprises — and what breaks at scale.

Platforms & infrastructure

Build-versus-buy decisions, model choices, data foundations and the real cost of running AI in production.

Governance & risk

Policy, assurance and audit practice from the leaders answering to boards and regulators.

Talent & leadership

Hiring, retention, capability building and the changing shape of the data and AI leadership role.