Chandrakanth Lekkala

expert panel

In the race to feed AI’s insatiable appetite for training data, model builders are increasingly butting heads with the platforms that host the content they depend on. The latest flashpoint is Reddit’s lawsuit against Perplexity AI, which accuses the company of “industrial-scale” evasion of anti-scraping protections and the indirect harvesting of Reddit posts through search engine caches. The case raises a knotty question: When is public web content a legitimate training resource, and when is it legally and/or ethically off-limits? Responses are arriving from both the marketplace and governments, with emerging startups helping content creators monetize AI-harvested data and Europe advancing the Artificial Intelligence Act, which would require firms to disclose or summarize copyrighted training data. The members of the Senior Executive AI Think Tank bring a practical and experienced perspective to the discussion of what responsible data acquisition should look like. Here, they break down where ethical and legal lines should be drawn and what responsible access must entail for AI developers, and they share insightful tips to help platforms rethink their data-licensing and access-control strategies.

expert panel

As artificial intelligence advances at breakneck speed, the question of trust has become more urgent than ever. How do senior leaders ensure that innovation doesn’t outpace safety—and that every stakeholder, from customers to regulators and employees, retains confidence in rapidly evolving AI systems? Members of the Senior Executive AI Think Tank—a curated group of seasoned AI leaders and ethics experts—are confronting this challenge head-on. With backgrounds at Microsoft, Salesforce, Morgan Stanley and beyond, these executives are uniquely positioned to share practical, real-world strategies for building trust even in regulatory gray areas. And their insights come at a critical moment: A recent global study by KPMG found that only 46% of people worldwide are willing to trust AI systems, despite widespread adoption and optimism about AI’s benefits. That “trust gap” is more than just a perception issue—it’s a barrier to realizing AI’s full business potential. Against this backdrop, the Think Tank’s lessons are not theoretical, but actionable frameworks for leading organizations in a world where regulation lags, public concern mounts and the stakes for getting trust wrong have never been higher.

expert panel

As artificial intelligence continues its rapid advance—from foundational models to enterprise-scale deployments—questions about sustainability are taking on new urgency. While much of the discourse has centered on the carbon footprint of data centers and model training, sustainable AI must also address long-term economic, labor and societal impacts: How will value from AI be shared? Who bears the downstream risks? Well-designed systems matter not only for performance, but also for fairness, trust and longevity. The Senior Executive AI Think Tank brings together seasoned experts in machine learning, generative AI and enterprise AI applications who offer deep insight into these challenges and opportunities. Below, they explore what truly sustainable AI looks like—beyond energy metrics—and who should be accountable.