“Knowledge is power, ” philosopher Francis Bacon famously stated. Yet, many companies still rely heavily on instinct rather than concrete data when pursuing prospects, armed mostly with traditional marketing personas — fictional characters created to represent target demographics, as described by Wrike. com. However, these personas often fall short. Built from subjective interpretations or fragmented CRM data, they tend to be shallow and incomplete, limiting sales teams’ confidence and effectiveness in critical meetings. While the old adage was “There’s an app for that, ” today it might be better said: “There’s AI for that. ” Mark Osborne, CEO of Modern Revenue Strategies, champions synthetic personas—dynamic, AI-powered customer profiles that evolve by combining real and assumed data. These personas learn from ongoing interactions, providing a deeper, more nuanced understanding of prospects. This concept parallels "AI Mind Clones, " sophisticated AI recreations of individuals’ expertise that let users tap into the wisdom of historical figures like Peter Drucker or Friedrich Nietzsche. Osborne emphasizes that synthetic personas integrate diverse data sources: actual customer inputs from interviews and communications, second-party insights from competitor reviews and social media, and third-party demographic or psychographic data, sometimes supplemented by AI predictions. This rich blend helps salespeople simulate and anticipate a prospect’s behavior far more accurately than traditional methods. Geoff Woods, author of The AI Driven Leader, highlights another innovative use of AI: assigning it roles to extract knowledge by having AI interview the user, helping surface strategic insights. Such applications position AI beyond static tools like calculators or search engines, transforming it into a dynamic partner—capable of interactive, human-like dialogue that can, for example, mimic customer responses or provide informed advice. The key enabler of these advances is Large Language Models (LLMs).

Unlike basic chatbots, LLMs understand unstructured human language in a nuanced way, capturing context and reasoning beyond keyword matching, per IBM. They can summarize content, debug code, or draft legal text, adapting to numerous tasks. LLMs can be likened to modern oracles—once consulted by historical figures such as Alexander the Great—to reduce uncertainty and guide decision-making. This persistent human challenge, an “asymmetry of knowledge, ” makes forecasting difficult but also drives innovation. LLM-driven synthetic personas now allow sales and marketing teams to predict customer reactions and tailor strategies with unprecedented precision. Marketers can A/B test messaging on synthetic personas for optimal engagement, while sales teams use them for training in “hard mode” scenarios that enhance deal-closing skills. Osborne recounts a client in the dry-cleaning industry who relied on convenience as their brand pillar but was unsure which aspect—extended hours, services, or pricing—would resonate most. Instead of guessing, they used a synthetic persona to generate data-driven insights, significantly improving their marketing approach. The impact of synthetic personas extends far beyond sales. They promise to reshape how uncertainty is managed in various aspects of life. Even seemingly whimsical applications, like students creating synthetic personas to navigate social interactions via notes in school, may not be so far off—just as widespread AI adoption once seemed improbable. In sum, AI-powered synthetic personas represent a transformative leap in understanding and engaging with people, turning the long-standing quest to bridge knowledge gaps into a practical, dynamic reality for businesses and beyond.