Mani Padisetti

expert panel

The first generation of AI adoption asked a simple question: What work can we automate?The next question is harder: What happens to the people who were experts at doing it before?Expertise has traditionally been built through practice. Engineers write and review code. Analysts work through problems. Marketers study audiences and campaigns. Architects design systems. Leaders accumulate judgment by making decisions and seeing what happens next. If AI increasingly performs those activities, where does that leave the role of the expert?Members of the Senior Executive AI Think Tank—a curated group of leaders specializing in machine learning, generative AI and enterprise AI applications—are thinking about that transition from inside the organizations and disciplines being reshaped by AI. Together, they raise an important question that reaches into the design of the organization itself: How do you build a company where AI can do more of the work without losing the human expertise that tells the company what work is worth doing?

expert panel

Startup fundraising has always followed a familiar formula: build the product, prove traction, raise enough money to reach the next milestone.But AI is disrupting that formula. Models change quickly, inference costs fluctuate, enterprise sales cycles can stretch for months and today’s breakthrough feature can become tomorrow’s commodity. The assumptions behind a traditional runway or growth plan can change before a company reaches its next round.That means AI founders need to rethink what they are raising capital to prove. Is it customer demand? Technical feasibility? Sustainable unit economics? Enterprise readiness? Or a durable advantage that can survive the next model release?Founders shouldn’t just consider how much money their startup can raise, but what that capital needs to accomplish—and what evidence it should produce along the way.Members of the Senior Executive AI Think Tank—a curated group of executives and practitioners working across machine learning, generative AI and enterprise AI applications—share the fundraising advice they believe AI founders should leave behind, from chasing maximum capital to relying too heavily on traditional traction, and what investors should see instead.

expert panel

Anthropic’s decision to add invisible watermarks to Claude-generated text has brought a long-running AI debate into sharper focus: How should businesses establish the origin of AI-assisted content, and what should they do with that information once they have it?The question matters as generative AI becomes embedded in everyday knowledge work, from drafting and research to marketing, analysis and customer communications. The European Union’s AI Act is also pushing the industry toward machine-readable disclosure of AI-generated content, making provenance an increasingly important part of enterprise AI strategy.But provenance is not the same as authorship, quality or accountability. A watermark may establish that an AI system was involved without explaining how extensively it was used, what a human changed or who ultimately stands behind the work.Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, offer a range of perspectives on what should come next. In the discussion that follows, they examine whether provenance should be standard or optional, where watermarking fits, how companies can protect privacy and human judgment, and what AI leaders should do to build greater trust and accountability around AI-generated content.

expert panel

Supply chains have historically been designed around a simple premise: Build the best possible plan, then execute it as efficiently as possible. Artificial intelligence has made those plans smarter, helping companies forecast demand more accurately, optimize transportation routes and reduce inventory costs. But those improvements, while significant, still operate within the same playbook.The next chapter looks fundamentally different.Rather than simply making existing processes faster or cheaper, AI is beginning to reshape how supply chains are designed, managed and even governed. Emerging technologies such as agentic AI, digital twins and real-time decision engines can continuously evaluate changing market conditions, simulate alternative scenarios and recommend—or in some cases execute—responses before disruptions ripple across the business. In this model, supply chains become adaptive systems rather than static networks.The business case for that evolution is growing stronger. Gartner predicts that by 2030, half of supply chain management solutions will incorporate agentic AI capable of making autonomous cross-functional decisions, reflecting a broader shift from automation to intelligent orchestration. At the same time, geopolitical instability, changing trade policies and increasingly unpredictable customer demand are forcing organizations to rethink resilience as a competitive advantage—not just an operational objective.Against this backdrop, members of the Senior Executive AI Think Tank, a curated community of leaders specializing in machine learning, generative AI and enterprise AI applications, see a common theme emerging. The greatest transformation will not come from AI replacing planners or optimizing another workflow. Instead, they argue, AI is becoming the connective tissue that links procurement, manufacturing, logistics, finance and leadership into continuously learning decision systems. That shift promises to redefine not only how supply chains operate but also how organizations make decisions, assign accountability and create value in an increasingly uncertain world.

expert panel

For executives leading AI transformation, one of the most important decisions is also one of the most difficult: deciding where sensitive data should be processed, stored and governed as artificial intelligence becomes part of the enterprise operating model. The architecture choices organizations make today will shape not only their ability to innovate, but also their ability to protect critical information, meet regulatory expectations and maintain trust.The challenge is that there is no single blueprint for secure AI adoption. Leaders must weigh competing priorities, including the speed and scalability of cloud platforms against the control and data sovereignty of private or hybrid environments, the need for strong governance against the risk of slowing innovation and the benefits of advanced AI capabilities against the responsibility to maintain visibility over how data is used. As AI systems create new layers of information through prompts, outputs, embeddings and logs, organizations must consider not only where data resides, but where it flows and whether they can control its entire lifecycle.Members of the Senior Executive AI Think Tank, a curated group of experts specializing in machine learning, generative AI and enterprise AI applications, examine the most important trade-offs leaders should consider when designing AI architectures for sensitive or regulated data. They also identify common mistakes organizations are making, from focusing only on storage location to overlooking data derivatives, governance gaps and the operational capabilities required to manage AI responsibly. Because architecture decisions are no longer just technical choices—they are business decisions tied to risk, resilience and long-term value.

expert panel

Artificial intelligence may be transforming the enterprise, but behind every successful AI initiative is something far less glamorous: disciplined data engineering.As organizations race to deploy generative AI, agentic systems and increasingly sophisticated analytics, many are pouring resources into new models, cloud platforms and AI applications. Yet time and again, ambitious projects fail to deliver meaningful business value—not because the technology falls short, but because the underlying data is inconsistent, poorly governed or difficult to trust.According to McKinsey's latest State of AI research, organizations seeing the strongest returns from AI distinguish themselves not by the models they choose, but by the maturity of the data, governance and operating foundations supporting those models. In other words, AI success begins long before a prompt is entered or an algorithm is deployed.Members of the Senior Executive AI Think Tank, a curated community of executives specializing in machine learning, generative AI and enterprise AI applications, have witnessed this firsthand across industries ranging from healthcare and financial services to manufacturing, retail and cloud computing. Below, they outline the foundational data engineering capabilities they believe consistently deliver the greatest business value and why leaders should take more notice.