Parry Malm

Post more. Find the perfect time. Let AI write it. Chase the algorithm. B2B teams are surrounded by confident LinkedIn advice. How much of it stands up when you look at the evidence?

Drawing on his analysis of LinkedIn posts and his work building Drumbeat, Parry explores what the data can tell us about finding the right connections, content, AI editing and performance, and where the evidence runs out. He makes the numbers accessible, challenges easy conclusions and asks a more useful commercial question: out of a million things you could do on LinkedIn, what should you actually do next?

The session takes the questions every B2B team actually argues about (what to post, who to invite, whether to comment, whether images help, whether AI-written content gets punished, whether anyone genuinely cares about a photo of a dog) and answers them against real data. It connects those decisions to the people a business wants to reach, the conversations it wants to start, and the difference between attention and an opportunity. Examples can be tailored to the industry in the room.

Something a skilled person used to do in a day now takes four minutes. The demos keep coming. Every morning before nine, somebody on LinkedIn explains that your job is finished. If it feels like the ground has moved under your business, or under your career, that is a reasonable thing to feel.

It is also a terrible basis for a decision. Panic and denial are the same mistake in different clothes: both skip the part where you work out what has actually changed. Parry has spent a decade building AI products and pulling apart the tasks people are paid to perform, and he is blunt about the difference between a demo that makes a room go quiet and a change that survives contact with a Tuesday afternoon. Most don’t. The ones that do rarely look impressive.

So the session gets practical about what to do next. Producing more of something is not the same as creating more value. Ask anyone who has received forty AI-written proposals this month. When execution gets cheap, the expensive part moves: to judgement, to accountability, to knowing which of the four-minute outputs is the right one and being willing to put your name on it. Most people are worth more than the task they were measured on, and have never had to say out loud which part that was.

Parry makes the case for working that out yourself, deliberately, before a customer or an employer prices it for you. No formula, because there isn’t one. Accessible evidence, honest uncertainty, and examples adapted to the industry in the room.

An AI system makes a consequential mistake. The supplier points to its terms and conditions. The organisation using it points to the supplier. The person affected wants to know who is responsible. Right now, the answer is rarely the people who built it.

Parry argues that meaningful AI regulation must answer that question. Drawing on his experience building AI businesses and contributing evidence to parliamentary discussions, he makes the case for rules tied to how AI is used and the harm it can cause, with responsibility that does not simply disappear into the supply chain.

He also challenges the idea that more regulation necessarily means better protection. Rules can protect people; they can also protect the largest vendors from competition. This keynote explores the incentives behind both, and the difficult division of responsibility between those building AI and those choosing to rely on it.

Seven days before a fundraise was supposed to close, the investors pulled out. They didn’t believe the business would grow fast enough. Parry and his team tightened their belts and carried on. The company later sold.

Eight days before his first child was born, his co-founders tried to remove him. If you come at the king, you best not miss.

These are two of the stories behind a founder journey that looks considerably tidier in a biography. Others include losing the second-biggest customer during sale exclusivity, and having to make difficult decisions about people when being liked was no longer an option.

Parry shares what those moments demanded of him: reassessing the plan, deciding what mattered most and taking responsibility for choices without knowing how they would turn out. And he outlines what it takes to go from idea to exit. There is humour, frustration and hindsight, including the limits of hindsight when the business eventually succeeds.