The Order—Flow and Process

Optimal flow

Companies interested in creating value with AI/ML have a lot to gain compared to their more hesitant competitors. According to McKinsey Global Institute, “Companies that fully absorb AI in their value-producing workflows by 2025 will dominate the 2030 world economy with +120% cash flow growth.” The undertaking of embracing AI and commodifying it—whether in our product or for internal purposes—is complex, technical debt-heavy, and expensive.

Once our models and use cases are chosen, making that happen in production becomes a difficult program to manage, and this is a process many companies will struggle with as we see companies in industries other than tech starting to take on the challenge of embracing AI. Operationalizing the process, updating the models, keeping the data fresh and clean, and organizing experiments, as well as validating, testing, and the storage associated with it, are the complicated parts.

Process

In an effort to make this entire process more digestible, we’re going to present this as a step-by-step process because there are varying layers of complexity, but the basic components will be the same. Once we have gotten through the easy bit and we’ve settled on the models and algorithms we feel are optimal for our use case, we can begin to refine our process for managing our AI system.

Step 1: Data availability and centralization

Essentially, we’ll need a central place to store the data that our AI/ML models and algorithms will be learning from. Depending on the databases we invest in or legacy systems we’re using; we might have a need for an ETL pipeline and data engineering to make the layers of data and metadata available for our productized AI/ML models to ingest and offer insights from. Think of this as creating the pipeline needed to feed our AI/ML system.