The Order—Flow and Process
Understand how AI adoption involves complex processes like data centralization and continuous maintenance for ongoing success and risk mitigation.
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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 ...