Summary:
Everyone has access to ChatGPT. Everyone is writing prompts. That is no longer a competitive advantage. Connor Biggar attended WordCamp US 2026 in Phoenix and came back thinking about a different question: not which AI tools you use, but how well you can actually lead them. The skills that produce better AI outcomes turn out to be the same skills that make someone a great leader of people, and most of us have not started developing them yet.
Who this article is for:
Marketing professionals, agency owners, founders and business leaders who are already using AI in their day-to-day work and want to understand what separates people who get consistently strong results from AI from those who get mediocre ones.
Key takeaways:
- Using AI tools like ChatGPT for emails and summaries is no longer a competitive advantage. Everyone has access to the same tools. The differentiator is how well you can lead and direct them
- AI should be treated like a new team member being onboarded, not a search engine being queried. Context, direction and expertise fed into the model change the output dramatically
- Loyal Pyczynski’s session at WordCamp US 2026 framed AI innovation around human leadership skills: experimentation, bringing different people into the process early, working through uncertainty and keeping human judgment at the center
- Expertise is not becoming less valuable because of AI. It is becoming more valuable, because the person who can give AI the right context, constraints and direction gets exponentially better outputs than the person who cannot
- The eight skills every AI leader should develop are clear communication, delegation, setting expectations, providing context, critical thinking, giving feedback, judgment and vision
- These are not new skills. They are the skills great leaders have always needed. AI is raising the cost of not having them
What’s inside:
- Why “I use AI” is no longer a meaningful differentiator and what is replacing it
- The mindset shift from using AI as a tool to leading it as a team member
- What Connor took away from Loyal Pyczynski’s WordCamp US 2026 session on innovation at Disney and Meta
- Why AI is making real expertise more important, not less
- The eight skills every AI leader should be developing right now
I recently attended WordCamp US 2026 in Phoenix, Arizona. Going into it, I expected to walk away with some awesome insights into how other people are using AI to build complex solutions… And don’t get me wrong, it was a big topic, but I still walked away with a somewhat conflicting takeaway.
I think we’re having the wrong conversations.
Not necessarily WordPress as a platform, not because WordCamp ignored it (they didn’t), but because of how we talk about it. I think we’re spending a lot of time talking about which AI tools we use, which model is best, how we’re making our lives easier by writing better prompts, and whether AI is going to replace certain jobs and not enough about what are the core skills an “AI leader” needs to spark innovation and acceleration.
Stop “Using ChatGPT.” Start Learning How to Work With AI.
Everyone is saying “Yeah, I use AI.” But what does that mean? There’s no standard playbook that comes with the different platforms, and there’s thousands of “Agentic Influencers” showing how they utilize AI to get views.
For most that I’ve seen, it means opening ChatGPT (or Claude) when they need help writing an email, summarizing meeting notes, creating some lines for a vibecoded plugin, or brainstorming a few ideas for their next content piece.
This isn’t terrible practice, but I think we’re reaching a point where using AI tools like this isn’t a ‘competitive advantage” anymore. Everyone is doing this and everyone has access to the tools.
I believe the real differentiator is becoming how well you can work with them… And more specifically, how well you can lead an agentic team.
The one talk at WordCamp that stuck out to me was Loyal Pyczynski’s talk “Dreaming with AI: Innovation Lessons from the Front Lines of Disney & Meta.” Loyal talked about how innovation doesn’t come from just adopting the newest technology. He highlighted how innovation comes from experimentation, bringing different types of people into the process early, being willing to work through uncertainty, and keeping human judgment at the center.
During this talk, I wrote down “AI should be viewed as an agentic partner or employee, and we need to reshape the way we set it up for success”. An example of this is rather than asking an AI tool to write or develop something for you, I think the better question is “how do I give AI enough context, direction, and expertise that it can help me do this significantly better.” It’s like onboarding a new entry level employee. Without a seamless onboarding and training process, you don’t have anything to keep them within certain boundaries, or help hone their craft towards the goal – they’re flying blind.
The way I think about it is you’re able to download research, data, and experience to an agentic partner instantly and completely change the course of what you’re working on. Years of experience and training that can help multiply your efforts applied directly to work.
For example:
If I was to ask a model to create a Meta ad for a roofing company, I can imagine it’s not going to be great. But what if AI knows my experience from working with this company:
- Who the company’s ideal client is
- Why customers actually buy
- Which campaigns failed
- Average purchase amount
- Sales cycle
- Close rate
- Positioning
- Landing page
- Common objections
Then I’m going to get a completely different result. I’ve just handed over the right knowledge, that has taken me years to accumulate, to a resource that can execute on it immediately.
AI Isn’t Replacing Expertise. It’s Making the Right Expertise More Important.
There’s the fear that AI is making expertise “less valuable”. And I don’t think that’s the case.
The wrong AI user would probably say, “now I can replace my employees because I have the expertise, and can multiply myself!” Which isn’t the point of this article.
In Loyal’s presentation he spoke specifically about “Core GenAI insights for Digital Interactive Experiences”. And it was at this point in the talk where I realized – these are also great skills to build an awesome connection with someone. These are core leadership skills to mitigate ambiguity, build a relationship, and ensure that expectations are clear. It wasn’t the direct headline that pointed that out, but as he continues to speak about why these are important, I immediately related it to what many great leaders do.
Interacting and building teams follow many of the same “best practices” that his core insights used for building a better interactive experience does. Are the AI tools really that different from us?
Start Thinking About About Leadership and Systems
AI isn’t just a tool you use anymore. It’s more like a collaborator that you lead. With poor direction and leadership, you will get poor outcomes. Going back to Loyal’s point about innovation, AI for most people is becoming part of the “team” that they bring in, and we need to focus on the skills to help guide its efforts towards our goals.
The core things that make someone a great leader is the ability to communicate clearly, provide context, set expectations, delegate, ask the right questions and give feedback. These skills are becoming incredibly more important when working with AI.
If you’ve ever had a great leader in your life, you know that through their leadership, and your skill, you’ve been able to achieve some really awesome things. There’s fewer people in leadership roles than there are people in execution roles – and the “executors” are the ones driving the innovation.
Are we all elevating to a new level of leadership now that we have the person who can execute on the work in our back pocket? And how many of us are actually great leaders?
5 Skills Every “AI Leader” Should Master
Rather than learning which connectors and MCPs work best with Claude or how I can get my development done faster/easier. Master these. You’ll get better outcomes down the road:
- Clear communication: Vague instructions produce vague outputs. Context and specificity matter.
- Delegation: Decide what AI should do, what humans should do, and where should they collaborate.
- Setting expectations: Give AI goals, constraints, examples, audience, format, and criteria for success
- Provide context : Outcomes dramatically improve when there’s reasoning behind a task
- Critical thinking: Know the right questions to ask, and be able to question assumptions
- Giving feedback: Iterating with humans works the same way. Identify what’s wrong, explain why, and proactively move forward by redirecting
- Judgment: Human judgment and experience will continue to be at the center.
- Vision: Keeping everything aligned on the long-term play
If you can do this, you haven’t just added a “new tool to the toolbelt”, but you’ve added another layer to your innovation. Most people are just learning how to use it without knowing how to guide it. AI is continuing to prove that it’s another layer to our thinking process and team, and we need to know how to lead it.
