I've been thinking about this shift a lot as I move from web dev into AI work: we've basically settled the debate over whether predictive models beat traditional forecasting. That's not interesting anymore. What's interesting (and harder) is what happens after the prediction, when a system decides to act on it.
This article frames it well. The real challenge in enterprise AI right now isn't accuracy, it's alignment: making sure an autonomous system's actions still reflect what the business actually intended, especially as it operates with less human review at each step. That gap between "the model predicted X" and "the system did Y because of X" is where I think a lot of the real engineering and governance work is going to happen over the next few years.
As someone coming from a background of building and maintaining systems that have to behave predictably for real users, this resonates. Autonomy without guardrails is just a different kind of risk than the one we used to worry about with bad forecasts.
https://www.technologyreview.com/2026/10/05/1143813/bringing-predictive-analytics-to-the-agentic-ai-era/ #AI #EnterpriseTech #MachineLearning