The AI Gap Is a Leadership Gap

There is a divide forming at the top of organizations right now, and most leaders have not named it yet. On one side are the executives who have gone deep on AI, not just the LLM chat interface that most professionals now use as a search engine with better prose, but the agentic tools that actually take action, connect data sources, execute workflows, and operate as real infrastructure beneath the surface of the organization. These leaders describe something that sounds almost too good to be true: the ability to surface real-time data across their entire business without a data analyst, build a functional product without an engineering team, and run repeatable workflows without adding headcount. The competitive gap they are opening up is significant, and it is moving fast. On the other side are the leaders who know AI matters, reference it in strategy conversations, and have perhaps integrated it into a few individual tasks, but are still essentially operating the way they were eighteen months ago. Although on the surface this looks like a technology gap, it is actually a leadership gap.

We recently sat down with Songe LaRon, co-founder and CEO of Squire, a tech company he has been running for a decade. He has spent the last few months going deep with AI and restructuring how he and his entire leadership team actually operate. What struck us was less the specific tools he described and more the framework he offered for understanding where organizations get stuck.

“Most people stopped at the LLM level,” he noted. They are using AI the way they used Google in 2005, as a slightly better research tool. However, they are missing the next layer: agentic AI that can respond to questions, but also acts on them. This distinction is important because one tool helps you think, and the other helps you operate.

A Chief of Staff With a God View of the Company

For Songe, the agentic layer has become what he calls a “chief of staff” — a system connected to his company’s design files, code repositories, payment processing data, and communications infrastructure. It surfaces insights and executes tasks that used to require a data analyst, a days-long turnaround, and a formal request. Now it is immediate and ongoing.

Product managers who used to spend weeks on prototypes now build MVPs in days.

Engineers accomplish in hours what previously required full sprint cycles, because the support function runs largely on AI agents, freeing headcount for higher-leverage work. And the shift is cascading from him to his executive team, from executives to VPs, from VPs to directors. The whole company is being restructured around a new operating assumption: that anything that used to take weeks should now take days, and anything that used to take days should now take hours.

But the example that carries the most weight for non-technical leaders is this: on the side, he built an entire functioning company, a mobile application that is live in the App Store, acquiring real users, with a website, a marketing function, a design system, and a quality assurance process — entirely by himself, with no engineering team and no agency. Just one person, operating inside a set of AI tools that handled the rest.

“It would have taken me at least a year with two or three engineers. I did it myself.” — Songe LaRon, Co-Founder & CEO, Squire

The point is not that leaders should build companies alone. The point is what that capability signals about what is now possible for small, focused teams that are willing to operate differently. Two or three people, he said, can now build what previously required hundreds. The old playbook that required raising capital, building headcount, and executing slowly at scale is being disrupted by smaller teams moving faster with fundamentally different leverage.

The Leadership Imperative

This is the moment that has a clear leadership implication: the executives who will have the most leverage in the next few years are not necessarily those who know the most about AI. They are the ones who go deep enough to actually operate inside it and learn to move from conversation to action, and from awareness to infrastructure.

Songe’s practical advice is disarmingly simple: start with your most pressing business challenge, bring full context, and ask the tool how it would approach it. Not as a thought experiment but as a working session. Pick your top priority goal for the year, whether that is new revenue, a product launch, a faster sales cycle, and say: here is what we are trying to achieve, here are our constraints, help me figure out how to use this to get there. Then take one task from the answer and actually try it. That first hands-on experience, he said, is what changes the frame. 

This is a leadership call; organizations whose senior leaders are not operating inside these tools are building a gap that will compound over time, and the executives who close it earliest will have a head start that is very difficult to replicate later.

A few questions worth sitting with this week:

Are you using AI at the conversational level, the workflow level, or the operational level? And do you know what moving to the next layer would look like for your organization specifically?

What is one workflow your team runs every week that, if an AI agent handled it instead, would free them to focus on the work only they can do?

If a competitor of your size adopted this level of AI infrastructure today and you did not, how long would it be before you felt it?

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