How We Became an AI First Company in One Week
For the past 16 months, we have been working on the ideas, functionality, and vision for The Pause, our women’s midlife health platform. We started in ideation, before today’s AI tools were as effective, accessible, and integrated as they are now. Like many founders, we began by doing what we thought we were supposed to do. We hired a traditional development shop to build a prototype. That decision cost us pain, money, time, and a tremendous amount of frustration.
The prototype eventually stood up, but it was not what we wanted. It did not reflect the user experience, product intelligence, or emotional resonance we had envisioned for women navigating midlife health. We then hired another development shop to come in and clean up the confusion left by the first one. After six more months, we had what I will candidly call an expensive, ugly product. That is hard to write, but it is true.
This is one of the realities many founders do not discuss publicly. Building software through outsourced teams can sound efficient, especially when you are trying to move quickly and conserve internal resources. In practice, it can become slow, expensive, and emotionally draining. This is especially true when the people executing the product do not deeply understand the customer, the mission, or the urgency. In our case, we were building for women in midlife, and the product had to feel as intelligent and human as the problem we were solving.
Eventually, I made the decision to hire internally. We interviewed people, gave 30 day trials, and built a small team. We equipped them with the traditional product and development stack: Miro, Notion, GitHub, AWS, Figma, and all the other tools modern teams use to collaborate and ship. On paper, we were doing everything right. In reality, development was still slow.
There was a lot of feedback. There were a lot of meetings. There were a lot of ideas. However, the path from idea to execution remained painful. Every change seemed to require more discussion, more documentation, more tickets, more handoffs, and more waiting. The team was working, but the velocity was not where it needed to be.
The irony was not lost on me. Despite being named one of the women leading the AI revolution by CTO Magazine, recognized for my work at the intersection of AI, leadership, and emerging technology, I was frustrated by how slowly we were moving inside my own company. I kept standing on the soapbox saying, “Let’s leverage AI.” I knew the tools existed. I knew what was possible. What I did not yet have was the team structure and leadership alignment to make AI first the default way of operating.
Then everything changed. We brought in a new developer, and she immediately elevated the energy and capability of the team. We reconfigured responsibilities, looked honestly at what was working and what was not, and elevated a junior developer who had clearly been waiting for the opportunity to step up. We also brought in a fractional CTO, someone I had worked with before and deeply trusted. On day one, he said, “We are now an AI first company. Everyone will use Claude Code.”
It went beyond a directive, it was permission. There were smiles across the team, and I am fairly certain I heard angels singing. The shift was immediate because the statement removed ambiguity. AI was no longer a side tool, an experiment, or something to use only when convenient. AI became the operating system for how we were going to build.
Within one week, the development team was using more than Claude Code. They were using the AI tools inside Figma and Miro. We began iterating new user journeys faster, with more clarity and less friction. Our new CTO led a Claude training on plug-ins and skills, and the team quickly began asking better questions. Instead of waiting for perfect instructions, people started using AI to explore, test, document, refine, and execute.
The bottleneck had not been talent. It had been permission, leadership, and expectation. Once the team understood that AI first was not optional, the entire pace of the company changed. There was less fear around experimentation and more ownership around outcomes. The team started moving with confidence, and for the first time in a long time, I felt the product catching up to the vision.
Our experience felt dramatic, but it is not isolated. In a controlled experiment published by Microsoft Research, developers using GitHub Copilot completed a coding task 55.8 percent faster than developers who did not use the AI pair programmer. (Microsoft Research) McKinsey also found that generative AI can help software developers complete coding tasks up to two times faster, with especially strong gains in routine development work. (McKinsey) These numbers matter because speed is not only a technical advantage. In a startup, speed is survival.
For specific tasks, McKinsey reported that generative AI can reduce the time required to document code functionality by 45 to 50 percent, reduce the time required to generate code by 35 to 45 percent, and reduce refactoring time by 20 to 30 percent. (McKinsey) This has direct financial implications. If a development team costs $50,000 per month and AI reduces even 30 percent of execution time on the right tasks, the potential value is not theoretical. It can represent weeks of saved payroll, faster launches, fewer handoffs, and reduced opportunity cost.
That said, AI does not remove the need for judgment. In fact, it increases the need for judgment. AI first companies still need architecture discipline, QA, testing, documentation, security protocols, and human oversight. The goal is not to let AI blindly write and ship everything. The goal is to use AI to compress the distance between insight and execution while keeping experienced humans responsible for quality.
Some of the most influential technology founders are already saying this out loud. Meta founder Mark Zuckerberg has said that he expects AI to write most of the code for Meta’s AI efforts in the near future, describing agents that can take a goal, run tests, improve things, find issues, and write high quality code. (Engadget) Replit founder and CEO Amjad Masad has described a world where a person can use a prompt to build an app. (AOL) Y Combinator CEO Garry Tan has also pointed to the capital efficiency implications of AI coding, noting that founders may not need teams of 50 or 100 engineers if AI helps them build with smaller teams. (LeadDev)
This is the part founders must understand. AI first is not just about speed. It is about capital efficiency, team design, and competitive advantage. When your team can build faster, test faster, and learn faster, you change the economics of the company. You also change the emotional energy inside the company. People stop feeling buried by process and start feeling empowered by progress.
Perhaps the funniest moment came from my husband, our CFO, who came from big accounting. He proudly told me that he had used Claude to create a prospectus. I nearly fell over. This is someone who is financially rigorous, process oriented, and not easily impressed by shiny objects. When he started using Claude for strategic finance work, I knew the shift had moved beyond development.
It was as if all my standing on the soapbox and saying, “Let’s leverage AI,” had finally been heard, accepted, and executed. The change did not happen because I said it once. It happened because the right leader came in, made the expectation clear, and gave the team permission to operate differently. Sometimes transformation is not about more tools. Sometimes transformation is about a sentence that changes the standard.
Here is the part some leaders will not like. If you have team members who are resisting AI, they are costing you money. That does not mean everyone needs to become an AI engineer overnight. It does not mean people should blindly trust AI outputs. It means that resistance to learning, experimenting, and adapting is no longer a neutral behavior.
There is a major difference between healthy skepticism and active resistance. Healthy skepticism says, “Let’s test this output.” Resistance says, “I do not want to change how I work.” Healthy skepticism protects the company. Resistance slows the company down.
In this market, slow is expensive. A team that refuses to use AI will take longer to build, longer to iterate, and longer to recover from mistakes. That extra time turns into payroll, opportunity cost, founder exhaustion, and missed market windows. In the past, a slow team might have been frustrating. Today, a slow team can be a strategic liability.
The mistake many companies make is believing they are AI first because someone bought licenses for AI tools. That is not AI first. AI first means the team asks, “How can AI help us do this faster, better, or with fewer wasted steps?” before defaulting to the old process. It means AI is integrated into product, development, design, finance, operations, and leadership. It means the company changes how work gets done.
AI first means product managers use AI to pressure test user journeys. It means developers use AI to generate, debug, document, and refactor. It means designers use AI to explore variations faster. It means finance uses AI to draft, analyze, and model. Most importantly, it means leadership gives people permission to stop performing productivity and start producing outcomes.
That was the shift we made in one week. The Pause is now getting ready to launch the next version of the product. The energy is different. The speed is different. The accountability is different. We are not perfect, and we are still building, testing, and learning.
And yes, we are still praying nothing breaks.
For founders, CEOs, and product leaders, the message is simple. AI first is no longer a future strategy. It is an operating decision. The companies that embrace it will build faster, learn faster, and conserve capital. The companies that resist it will pay for that resistance in missed deadlines, bloated budgets, and frustrated teams.
We became an AI first company in one week. The decision took one sentence. The results changed everything.
