The 1% in AI

The 1% in AI is designed as a structured learning journey for people who want to build an AI automation agency that delivers results for real businesses. Instead of focusing only on tools or theory, the program guides learners through a practical sequence: understanding the AI agency model, acquiring core technical skills, applying those skills to business problems, and then building systems to attract and serve clients at scale. The emphasis throughout is on measurable outcomes and repeatable workflows rather than abstract knowledge.

The learning path begins with a foundational phase that explains what an AI agency is, which problems it solves, and why businesses pay for automation and AI agents. Learners explore how AI automation fits into broader business processes such as lead generation, customer support, operations, and data handling. In this early stage, the focus is on building a clear mental model: the difference between tools, workflows, and services; how AI connects to business value; and what makes an offer compelling to clients. By the end of this phase, learners are able to define a simple, outcome-driven service concept rather than thinking in terms of vague AI features.

The next phase introduces core technical skills needed to deliver those services. Students work with platforms such as n8n, Make, GoHighLevel, and Claude Code to construct basic automation flows and AI-powered applications. They learn how to connect APIs, set up triggers, route data, and orchestrate tasks across different tools. The training shows how to break down a business process into discrete steps, then rebuild that process as an automated workflow. Through guided examples, learners practice creating simple AI agents that respond to inputs, make decisions based on rules or data, and perform actions like sending messages, updating records, or generating content. At this stage, the goal is not complex engineering but confidence in using modern tools to build functioning systems end-to-end.

Once the technical fundamentals are in place, the curriculum shifts toward applying these capabilities to real business scenarios. Learners examine use cases for local businesses, online service providers, and digital products. They see how AI agents and automation can handle lead follow-up, appointment booking, repetitive data entry, content generation, and customer communication. Exercises focus on mapping specific business outcomes, such as more booked calls or faster response times, to concrete automations. Students learn to document workflows, define clear inputs and outputs, and specify what success looks like. This stage helps transform technical skills into practical solutions that can be explained and sold to clients in straightforward terms.

Parallel to the implementation work, a dedicated segment of the program covers basic business principles for running an AI agency. Learners study positioning, pricing, and service packaging, with attention to how to present AI-driven solutions in a way that emphasizes results rather than technology. They explore different offer structures, such as done-for-you automation projects, monthly retainers, and performance-oriented deals. This phase calls attention to risk management, expectation setting, and the importance of aligning incentives with client outcomes. By the end of this segment, participants can outline a simple, coherent service offer that matches their current skill level while leaving room for future expansion.

The curriculum then moves into client acquisition and outreach methods. Learners are guided through setting up basic assets such as a simple website or landing page and a clear description of their core offer. They explore outreach workflows using cold email, direct messages, and content posted on social platforms. The program breaks down outreach into practical steps: defining a niche, researching businesses within that niche, crafting messages that focus on tangible improvements, and following up in a structured way. Training emphasizes consistent action over perfection and shows how to track conversations, test angles, and refine messaging based on responses. By implementing these practices, learners start building a pipeline of potential clients rather than waiting passively for opportunities.

As students begin to secure interest and early clients, the program addresses delivery, onboarding, and project management. Guidance is provided on running discovery calls, asking the right questions to understand processes, and translating client needs into clear automation and agent specifications. Learners practice creating simple scopes of work, outlining timelines, and setting measurable goals for each engagement. They learn how to gather access credentials, document existing systems, and design workflows that integrate with the client’s tools rather than disrupting operations. This stage reinforces the habit of designing solutions that can be maintained and improved over time, not just deployed as one-off builds.

A later phase of the journey focuses on scaling systems and building structure around the growing agency. Learners explore how to standardize successful automations into reusable templates, how to document procedures as SOPs, and how to create a consistent process from lead generation through delivery and ongoing optimization. The content explains how to manage multiple projects simultaneously, prioritize tasks, and use simple dashboards or tracking mechanisms to maintain visibility on performance. Students are encouraged to think in terms of systems that can be delegated, whether to subcontractors, future hires, or automation itself. By practicing this approach, they prepare for growth beyond a single-person operation.

Throughout the program, attention is given to improving technical depth while maintaining clarity. Learners revisit tools like Claude Code and other AI platforms to build more advanced agents, integrate external data sources, and refine prompts and instructions for reliable behavior. They learn how to test and iterate, diagnose failures in workflows, and adjust inputs or logic to achieve consistent outputs. The goal is to develop a practical engineering mindset: identifying constraints, designing experiments, and making incremental improvements. These practices help ensure that solutions remain robust as client needs evolve or tools update.

The final stage of the learning journey brings together all the elements into a coherent, ongoing practice. Learners are encouraged to review their niche selection, refine their offers based on successful engagements, and evaluate how their systems support their long-term goals. Attention is given to building relationships with clients, tracking the results of each implementation, and using those results to improve positioning and outreach. At this point, students should be able to design, sell, and deliver AI automation services that provide measurable business value, while continuously upgrading their skills and structures. The 1% in AI aims to leave participants with a repeatable, grounded approach to building and growing an AI agency, rather than a collection of disconnected tactics.

Who this course is for:

The 1% in AI is ideal for aspiring entrepreneurs who want to start an AI automation agency without needing a deep technical background, as well as freelancers and consultants who aim to add AI-powered workflows and agents to their existing service offerings. It suits agency owners looking to systemize delivery with automation, motivated beginners seeking a structured path to their first AI clients, and self-directed learners who prefer practical, implementation-focused training tied directly to real business use cases.