Speaking

1. ProblemFind, hire, onboard, train and chase a marketing team. Then they quit, and it starts again.
2. BenefitA VP agent per line, agents that do the work, a scorer and a compliance check. A person signs.
3. TransformationA company that runs on agents: marketing and sales today, then service, HR and finance.

The problem it solves for the room: executives who have been told AI will change their business and have not seen anyone actually do it.

At Guz.ai I build the agents myself. As of October 2026 the laboratory holds fifty-three agent definitions and eighty-two skills across nine product lines, from an advertising agent to a LinkedIn agent to a website builder. For most of my career, most of my energy went to hiring, onboarding, training and follow-up. Now a VP agent runs each line, worker agents do the work, a scorer grades it, a compliance agent can stop it, and a person signs.

The middle of the talk is a live lab run: the room picks a product, and on the screen the agents research the buyer, write a week of LinkedIn posts, draft an ad test and write a press release, each one scored before the room sees it. Then what it costs, where it breaks, and what the rest of the company looks like when customer service, HR and finance run the same way. The room leaves with the first three agents to start on Monday.

1. ProblemRunning on empty and still expected to perform.
2. BenefitA formula tested through traumatic brain injuries and two years in bed.
3. TransformationA team that keeps going under pressure, and one hourglass to take home.

The problem it solves for the room: people running on empty who still have to perform, and leaders who need a team to keep going under pressure without pretending the pressure is not there.

At fourteen I was in the back of a police car in Tijuana wondering how to talk my way out. I spent the next thirty years studying influence, motivation and happiness, and used them to run companies, raise money and write books. Then an airport shuttle hit a freeway barrier and I woke up with traumatic brain injuries, two years in bed, and no way to talk my way out. This is the talk about what held when nothing else did: the formula I had tested on myself, and one tool, an hourglass, that I hand the room at the end.

1. ProblemA good product, no one paying attention, and marketing measured by activity.
2. BenefitMarketing that gives first, and systems that compound.
3. TransformationA LinkedIn profile that works as a sales machine, with the steps on the slides.

The problem it solves for the room: companies with a good product and no one paying attention, and marketing teams measuring activity instead of what comes back.

Built on my book of the same name. Marketing that gives first, systems that compound, and the one channel I have taught from the biggest marketing stages in the country: a LinkedIn profile turned into a sales machine, which is the keynote I gave at the GKIC SuperConference. Practical, with the steps on the slides.

1. ProblemListings, ads and retail data move faster than a brand team can.
2. BenefitMore than twenty years in beverages and consumer goods, plus the agents.
3. TransformationA brand where agents run listings, ads and retail data, from shelf to sale.

The problem it solves for the room: beverage and consumer brands, distributors and retailers who need to know what AI changes in listings, ads, retail data and the sales floor.

More than twenty years in beverages and consumer goods, from distribution companies in the United States and Mexico to more than a thousand products, and now the agents. For industry conferences and company meetings, on the business itself or on AI applied to it.