Vandana Singh

expert panel

When an AI system produces a disappointing answer, the first instinct is often to rewrite the prompt. Add more context. Give it an example. Spell out the rules. Try again. Sometimes that works—but there’s a point where prompt refinement becomes a way of avoiding the real problem. If an AI system still struggles after repeated rounds of instruction, leaders need to ask a different question: Is the prompt actually the bottleneck?That question matters because AI performance depends on far more than the words sent to a model. The quality and availability of data, the tools a system can access, the workflow surrounding it and the model’s own capabilities can all shape the result.For leaders, the challenge is knowing when to stop tweaking and start redesigning. If the problem is the way work gets done, the answer may be a new workflow. If the system lacks reliable information, better data may matter more. If the task requires actions or specialized capabilities, different tooling or a different model may be necessary. And sometimes the right answer is to rethink the product or process altogether. Members of the Senior Executive AI Think Tank—a curated group of executives and practitioners with expertise across machine learning, generative AI and enterprise AI applications—share how they recognize those inflection points and what leaders can do when a better prompt is no longer enough.

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.