How to Become an In-House AI Specialist
Nate Herk | AI Automationgo watch the original →
the gist
Instead of starting an agency, position yourself as the internal 'AI person' by identifying business bottlenecks, building specific automated workflows to address them, and documenting the measurable impact on time, errors, or revenue.
The Internal AI Specialist Strategy
The primary opportunity for AI practitioners is to transition from external agency work to an in-house role. Companies are under pressure to adopt AI but struggle to scale, with 95% of pilots failing to deliver measurable returns. The goal is to become the internal expert who bridges this gap by turning AI spend into verified business outcomes.
The Three-Phase Roadmap
- Positioning: Start as a builder within your current team. Identify the most repetitive, low-risk tasks—such as weekly reporting, inbox management, or data cleaning—and secure approval to automate them using company-sanctioned tools.
- Proving Value: Before building, define a specific metric to move (e.g., hours saved, response time, or error rates). Build the workflow using Claude with relevant documentation and templates, then record a before-and-after case study to prove the impact.
- Scaling Impact: Once you have a track record of wins, shift focus to attacking business constraints. Determine if the company is supply-constrained or demand-constrained by identifying what breaks if customer volume doubles. Build systems specifically designed to alleviate these growth bottlenecks to make your role indispensable.
Measuring Success
To ensure your work is recognized, you must explicitly connect your automated systems to the business's bottom line. Do not rely on management to notice performance improvements; frame every win as a result of your specific systems. This approach justifies larger budgets and resources for future projects, effectively moving you from a task-automator to a growth-driver.