Tal Lev-Ami

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

In AI, success is no longer determined solely by selecting the largest language model or achieving the highest benchmark score. Increasingly, organizations are deploying AI systems composed of multiple foundation models, retrieval systems, APIs, business applications and autonomous agents working together to complete complex tasks.As these systems become more autonomous, reliability becomes a systems engineering challenge rather than simply a model evaluation problem. According to the National Institute of Standards and Technology's AI Risk Management Framework, trustworthy AI requires organizations to continuously monitor, govern and manage risks throughout the lifecycle—not simply evaluate a model before deployment. Those principles become even more important as organizations adopt agentic AI capable of making decisions and invoking external tools.Members of the Senior Executive AI Think Tank, with expertise in machine learning, generative AI, and enterprise AI applications, believe orchestration is rapidly becoming the operational foundation that makes this possible. Rather than simply routing requests between models, orchestration determines how systems select models, validate outputs, monitor execution, recover from failures and balance competing priorities including latency, quality, security and cost.In the following sections, they explore the critical decisions leaders must make as AI systems become more autonomous—from improving routing and evaluation to strengthening observability, managing complexity and creating safeguards that allow organizations to scale AI with confidence.