Predictive analytics use historical data to derive a prediction of future events. The result of a "predictive analytics" decision is a measure of probability, not a fact. There will always be some small number of processes where probability isn't good enough: decisions in these processes will either be deterministic (driven by black or white rules) or deferred to human judgement.
Increasingly, however, predictive analytics will become a vital part of most of our business processes. There are cars on the road in Nevada and California that are being driven, at least in part, by predictive analytics. People may just think of these as really smart machines, but a good part of the algorithm that makes them smart is analyzing massive amounts of data to get to predictions. Culturally, we will accept this level of computer intelligence just as we have come to accept the connected world created by mobile devices.
I see three models for how predictive analytics will drive processes in the future:
1) Predictive analytics drives the process. Many marketing processes are here already. Predictive analytics determine which offers a customer sees, which adds show up on websites, etc. These may seem like decisions out of context, but to a marketer, these predictive decisions are driving the very important process of the "customer journey."
2) Predictive analytics and rules drive the process. In this example, predictive analytics is governed by rules. We are seeing this with a number of process that involve risk analysis (selling credit products, increasing lines of credit, etc.). Analytics provide key insight, but are the probabilistic is governed by the deterministic: black and white rules provide gates where credit scores or debt-to-income ratios may override the predictive analysis.
3) Predictive analytics advise the human actor. I think this is the area where we will see the greatest growth in predictive analytics. We are seeing customer service agents advised during customer interactions by customer value calculations or retention budgets calculated via predictive analytics. We are seeing engineers who are using predictive analytics to triage root causes early in the incident management process. This sort of assisted-driving mode, where the human still has room for judgement and in the moment decisions is where I think many of our more dynamic processes will be going.