Create distinct AI agents representing key executives (e.g., CEO, CMO, CSO). By posing strategic questions to each, you can simulate how different departments might react, identify potential misalignments in priorities, and refine proposals before presenting them to real stakeholders.

Robin Vince, CEO of America's oldest bank, leverages a proprietary multi-agent AI platform named Eliza for his day-to-day work. Before client meetings, he prompts the AI to synthesize call reports, news, and past interactions to generate key talking points, showcasing executive-level AI adoption in legacy finance.

Beyond simple concept testing, AI simulations allow businesses to model downstream consequences. A car company can simulate how launching a new EV might change market perception of its entire gas-powered product line, revealing second-order effects that are impossible to test in the real world.

Existing AI tools like Societies can test marketing content by creating hundreds of AI agents based on a user's actual audience (e.g., from LinkedIn). The platform predicts how viral a post will be and suggests improvements before it's published, offering a data-driven approach to content strategy.

To enhance due diligence, Deerfield Management employs multi-agent AI systems that deliberate on investment theses. These systems simulate discussions between different experts, such as a pathologist and an oncologist, to identify market pricing or patient populations, uncovering insights human teams might miss.

LLMs trained on online text often reflect what people say, not what they do. Simile bridges this 'say-do gap' by collecting real behavioral data and personal life stories through partners like Gallup. This grounds their agent simulations in reality, making them more predictive of actual behavior.

Shopify's new SimGym tool, which uses AI agents to simulate how customers interact with a store, points to a new standard in marketing. Soon, launching a campaign, redesign, or product without first running it through a sophisticated AI simulation will be considered archaic and reckless.

To make its AI agents robust enough for production, Sierra runs thousands of simulated conversations before every release. These "AI testing AI" scenarios model everything from angry customers to background noise and different languages, allowing flaws to be found internally before customers experience them.

Markup AI's CEO built AI profiles for figures like Steve Jobs. Before board meetings, he runs his deck by this "fantasy board" to get instant, diverse feedback, effectively bringing expertise into the room that isn't physically there.