AI Operations Lead Accelerates Company Productivity
by Dan Shipper on July 17, 2025
Dan Shipper believes that having dedicated AI operations leadership is transformative for organizations adopting AI. At his company Every, they employ a head of AI operations who focuses exclusively on identifying repetitive tasks and building prompts and workflows to automate them across the organization.
This approach solves a fundamental challenge in AI adoption: when people are busy with their day-to-day responsibilities, they rarely take the time to automate processes even when they know they should. As Dan explains, "If you're working in a job all day, you're fighting fires, and you're like, 'Am I going to do this in the way that I know how, or am I going to do it in the new way that might not work?'" Having someone dedicated to AI operations allows teams to identify inefficiencies and solve them without requiring the people doing the work to take time away from their core responsibilities.
The ideal AI operations person combines process orientation with an understanding of the craft they're supporting and genuine excitement about AI technology. At Every, their AI operations lead has a background in content marketing, is a skilled writer herself, and loves tinkering with AI. This combination allows her to create solutions that truly fit the team's needs.
The impact extends beyond just building prompts. The AI operations role includes driving behavioral change to ensure adoption. For example, when they automated copy editing with Claude, they also had to establish new habits where editors would ask, "Did you put this through the prompt yet?" before reviewing content manually.
Compounding Engineering: Making Each Task Easier Than the Last
Every's engineering team practices what they call "compounding engineering" – for every unit of work, they make the next unit easier. Rather than simply completing tasks, they invest time in creating prompts and automations that reduce future effort.
For example, instead of repeatedly writing detailed PRDs (Product Requirements Documents) from scratch, they've created prompts that can transform rough ideas into structured PRDs. This approach creates a virtuous cycle where the team gets progressively more efficient with each iteration.
The team maintains a GitHub repository of these prompts and automations, allowing them to share and build on each other's work. They also use multiple AI agents simultaneously, finding that different models have different "personalities" and strengths for various tasks – similar to working with a diverse team of humans.
The Allocation Economy: Management Skills Become Universal
Dan believes we're moving from a knowledge economy to what he calls an "allocation economy," where the skills of managers become increasingly valuable for everyone. As AI handles more specialized tasks, humans need to develop skills in evaluating output, providing feedback, and deciding when to dive into details versus when to delegate.
"When you look at what skills are going to be valuable in the AI era, one big group of skills are the skills of managers today," Dan explains. "They're human managers today, tomorrow everyone's a model manager." This shift means management skills will need to be more broadly distributed throughout organizations.
For leaders, this suggests investing in developing management capabilities across all levels of the organization, not just among formal managers. For individual contributors, it means developing skills in prompt engineering, quality evaluation, and knowing when to trust AI output versus when to intervene – essentially becoming effective managers of AI tools.