For decades, the front door to a business was a website, a search result, or a sales conversation you could control. That's no longer true. Increasingly, the first — and sometimes only — impression a prospect, partner, or investor forms of your business is generated by an AI engine: ChatGPT, Gemini, Perplexity, or Google's AI Overviews. This session tackles two problems leaders don't yet realize they have: (1) most organizations are functionally invisible to these systems, and (2) when they are visible, the narrative being generated is often outdated, incomplete, or simply wrong — and almost no one is checking.

This isn't a marketing footnote — it's a strategic exposure. Decision-makers are now forming opinions about vendors, partners, and acquisition targets by asking an AI a question, not by reading a homepage. If your business isn't well-represented in that answer, you don't lose a click — you lose the meeting. And because these answers are generated, not curated, a single outdated press release, an old org chart, or a critical forum post can become the "truth" an AI repeats with total confidence, to everyone who asks. Leaders need to know how exposed they are before a client, board member, or investor finds out for them.

Drawing on real-world business development experience competing for enterprise and investor attention in a crowded, fast-moving market, this session walks through a live, practical methodology: how to audit what AI engines currently say about your organization (and your competitors), how to identify the gaps and inaccuracies driving a wrong impression, and how to systematically close them — through structured content, authoritative third-party signals, and consistent factual reinforcement across the sources these models actually draw from. Where possible, the session includes a live or recorded demonstration of querying multiple AI engines about a real organization and comparing what comes back.

Takeaways
Attendees will leave able to:

Run a basic "AI narrative audit" on their own organization using free, accessible tools
Identify the most common reasons businesses are invisible or misrepresented to AI engines
Apply a practical framework for shaping the inputs that AI systems use to describe your business
Build a simple, ongoing process for monitoring and correcting your AI-generated narrative — before a client or investor encounters it first