Korena Keys

expert panel

For many customers, the first question they have about an AI-powered product is no longer “What can it do?” It’s “What happens to my data when I use it?”That question is becoming harder for organizations to answer as AI moves deeper into everyday business processes. A customer using an AI assistant, a patient interacting with a healthcare platform or an employee relying on an AI-powered workflow may not know what systems are operating behind the scenes—but they increasingly want to understand how their information is being handled.Where is the data processed? Who has access to it? Is it being used to improve a model? What control does the customer have if they want to change their preferences? These questions are forcing executives to rethink what transparency means in the AI era. A privacy policy alone is no longer enough. Customers want clear explanations, practical choices and confidence that organizations are applying the same principles internally that they communicate externally.Members of the Senior Executive AI Think Tank—a curated group of experts specializing in machine learning, generative AI and enterprise AI applications—say trust will depend on more than meeting regulatory requirements. From stronger governance processes to clearer communication about data use, these leaders share how organizations can build trust while continuing to innovate.

expert panel

Artificial intelligence has become remarkably good at creating competent work. It can draft marketing copy, generate product descriptions, design visual assets and even emulate established brand voices in seconds. Yet as organizations adopt many of the same foundation models and workflows, a different challenge is emerging: sameness.Instead of creating stronger differentiation, AI often produces outputs that reflect statistical averages rather than distinctive thinking. The result is an increasing number of websites, advertisements and product messages that feel interchangeable.Members of the Senior Executive AI Think Tank, an invitation-only community of leaders advancing enterprise AI, argue that the real opportunity for differentiation lies far beyond selecting the latest LLM. Across industries ranging from design and marketing to cloud infrastructure and retail technology, they point to a common set of competitive advantages: proprietary knowledge, human judgment, organizational context and leadership that gives AI clear direction.Their insights reveal a fundamental shift in how executives should think about AI strategy. Rather than asking which model is best, organizations should ask what unique expertise, customer understanding and decision-making processes they can bring to those models. The following perspectives explore where lasting competitive advantage is emerging—and why the companies that stand out in the AI era may be the ones that invest most heavily in the capabilities machines can't replicate.

expert panel

Artificial intelligence is rapidly redefining the cybersecurity battlefield, shifting the balance between defenders and attackers at a pace many organizations are struggling to match. As enterprises embed generative AI, autonomous agents and machine learning into critical workflows, the attack surface is expanding just as quickly as defensive capabilities evolve.This tension is at the center of discussion among members of the Senior Executive AI Think Tank, a curated group of leaders specializing in enterprise AI, machine learning and responsible AI deployment. To them, AI is not just a technology upgrade—it is a structural shift in how cyber risk is created and managed.According to the National Institute of Standards and Technology’s AI Risk Management Framework, organizations adopting AI face heightened risks related to system reliability, security vulnerabilities and adversarial manipulation, even as they gain powerful new defensive tools. At the same time, a Google Threat Intelligence Group analysis on AI-enabled threat activity warns that adversaries are increasingly using generative AI to accelerate vulnerability discovery, exploit development and initial access—signaling a shift toward more automated and scalable cyber intrusion models.With this knowledge, senior executives are asking a pressing question: Over the next five years, should we be more optimistic about AI’s role in cybersecurity—or more concerned? And more importantly, what concrete actions should leaders take today to stay ahead of the curve?Their insights suggest the answer is not binary—but it is urgent.