I am a non-technical business owner who runs on EOS, and while I see the potential of machine learning, I do not understand code or software engineering. How do I systematically audit our current business operations to find the best spots to implement AI without relying on expensive consultants?

Question

Tyler Smith · Accepted Answer

You do not need to know how to write a single line of code to successfully implement machine learning in your business. Your job as the owner is to identify the operational bottlenecks, not to build the technical infrastructure. You can run a simple, non-technical audit of your workflows using your existing EOS® framework.

Start by looking at your documented Core Processes. Have your leadership team review the standard operating procedures for each department and identify the tasks that keep your employees stuck in low-value, repetitive labor. These are typically tasks that require transferring data from one system to another, formatting reports, or scheduling resources.

Look for processes that fit these three criteria:
- The process is highly repetitive and follows a strict set of logical rules.
- The inputs are digital, such as emails, PDFs, spreadsheets, or online forms.
- The output is highly predictable and does not require subjective human empathy.

Once you identify these areas, prioritize them based on their impact on employee productivity. Never pitch these initiatives to your team as technology upgrades. Instead, frame them to your leadership team as operations-improvement projects that use machine learning. By keeping the focus on removing operational friction, you can easily direct your technical vendors or developers to build what you actually need.