The Australian federal police says it had "no choice" but to lean into using artificial intelligence and is increasingly using the technology to search seized phones and other devices, given the vast amount of data examined in investigations. The AFP's manager for technology strategy and data, Benjamin Lamont, said investigations conducted by the agency involve an average of 40 terabytes' worth of data. This includes material from the 58,000 referrals a year it receives at its child exploitation centre, while a cyber incident is being reported every six minutes. "So we have no choice but to lean into AI," he told a Microsoft AI conference in Sydney on Wednesday. "It's beyond human scale, so we need to start to lean in heavily on AI, and we're using it across a number of areas."

We have come to witness the time when artificial intelligence is part of our lives. Of course, humanity is only at the beginning of a road we do not know where it will take us, a road that is still in the construction phase. Beyond technology and its inevitable advance, a social construction suitable for integrating AI into everyday life is needed. The first step in this direction is the creation of an economy of AIs. Regarding this path, the latest Deloitte report says that while AI continues moving closer to the core of the enterprise sector, outcomes seem to lag.

This article is a continuation of Part 1. You can find the first part here. It is likely that when you begin your AIOps journey, you will already have certain analytics in place. I do not mean the analytics that are embedded in your IT tools. I mean offline, mostly manual analytics, that you do regularly, irregularly, or periodically to identify areas for process improvement, reduce costs, improve performance, etc. These manual efforts are precisely what your AIOps solution should address and automate in its first iteration.