Jason Barnard

You Have Seven AI Employees Working 24/7 — Are They Selling for You or Your Competitors?

Format: 60-minute keynote with optional 120-minute practical workshop This program is perfect for: Business leaders and entrepreneurs who want AI platforms actively working for their brand Marketing and communications teams responsible for how the company appears on Google and AI platforms Any organisation losing deals to competitors who show up more confidently in AI-generated answers Conference organisers looking for a talk that leaves every attendee with something concrete to act on The audience will leave with: A clear map of the SEO → AEO → AAO transition, including how to carry every pound invested in SEO and move smoothly into the AI era A diagnostic they can run this week: what AI is saying about their brand today, where it hesitates, and where it recommends competitors The three revenue taxes to quantify and fix first: the Doubt Tax at the point of conversion, the Ghost Tax when AI recommends competitors, and the Invisibility Tax when their brand simply doesn't appear A phased action plan: consolidate before creating, zero new content required in phase one The framework behind the data: what 73 million brand profiles reveal about which signals tip AI from hesitation to confident recommendation The Kalicube Flywheel: the business architecture that turns a trained AI salesforce into a compounding revenue asset, and why it creates a moat competitors cannot reverse-engineer Google, ChatGPT, Perplexity, Claude, Gemini, Microsoft Copilot, and Alexa are talking to your prospects about your brand and competitors. For two decades, the strategy was SEO: get your website in front of Google searchers. That worked when there were ten blue links and the user chose. Then AI started answering directly, and the game shifted to AEO, Answer Engine Optimisation, where the goal became being the answer, not just a result. Jason coined that term in 2017. AI platforms don't just answer. They recommend, compare, and sell. That makes the real discipline AAO, Assistive Agent Optimisation: training seven algorithmic employees to actively close deals for you rather than fumbling the pitch, hedging on your name, or sending the prospect to a competitor. Most businesses don't know those employees exist. The ones that do are still optimising for search engines when they should be training a salesforce. The window to fix it is closing. AI platforms are building their foundational understanding of every brand now. The brands that train their algorithmic salesforce during that window become the default recommendation. The brands that wait spend years correcting what the AI already believes. And correcting a confident algorithm is like trying to change a fossil. Once your seven AI employees are working, the economics of your business change. Your next client arrives pre-sold: three AI platforms told the same confident story. That client succeeds. Their success becomes evidence. The next recommendation is more confident. They succeed faster. More evidence, more confidence, more revenue. This is the Kalicube Flywheel: the business architecture where every client you serve well makes every AI recommendation stronger. The brands running this loop are building an asset that compounds. The brands that are not are paying three invisible taxes every day while their competitors' flywheels spin faster. The optional workshop applies the keynote live, participants leave with a completed audit, a prioritised action plan, and a clear map of where their Flywheel is stalled.

How to Segue to AI Search without losing your investment in SEO

The Ten Stages of AI Implementation

Format: 60-minute keynote with optional 120-minute practical workshop This program is perfect for: Business leaders who are using AI but know they and their teams are only scratching the surface Leadership teams navigating the challenge of getting the whole organisation moving on AI together Entrepreneurs' groups, executive retreats, and leadership conferences The audience will leave with: A clear map of exactly where they and their team sit across ten stages of AI implementation — and what the next step looks like, so nobody is guessing or falling behind without knowing it The single most important action to take this week to accelerate their team's AI capability — specific, practical, and chosen for their exact situation A method for extracting the knowledge already inside the business — the expertise, instincts, and hard-won experience that makes AI work for this company rather than any generic competitor The step-by-easy-step framework that turns AI upskilling from an overwhelming mountain into a gentle, manageable slope — for the leader and for every member of the team

Start With the Asset You Already Have — The Ten Stages of AI Implementation

Format: 60-minute keynote with optional 120-minute practical workshop This program is perfect for: Business leaders who are using AI but know they and their teams are only scratching the surface Leadership teams navigating the challenge of getting the whole organisation moving on AI together Entrepreneurs' groups, executive retreats, and leadership conferences The audience will leave with: A clear map of exactly where they and their team sit across ten stages of AI implementation — and what the next step looks like, so nobody is guessing or falling behind without knowing it The single most important action to take this week to accelerate their team's AI capability — specific, practical, and chosen for their exact situation A method for extracting the knowledge already inside the business — the expertise, instincts, and hard-won experience that makes AI work for this company rather than any generic competitor The step-by-easy-step framework that turns AI upskilling from an overwhelming mountain into a gentle, manageable slope — for the leader and for every member of the team

learning to say "yes" to opportunity

I moved to Paris for love. She said no. I stayed anyway. The TWO best decisions I ever made... Punk musician. Cartoon blue dog. Desert island geek who predicted how AI thinks — and built the machine that trains it.