Network-operations types tell me that, in the future, AI is going to manage their networks. They also tell me that their vendors told them that very same thing. The good news is that\u2019s sort-of-true. The bad news is the same; with emphasis on the qualifier \u201csort-of\u201d. To get the most from AI network management, you have to navigate out of that hazy \u201csort-of\u201d zone, and you do it by thinking about ants and farmers.\nAnts can build wonderfully complex anthills, with all manner of interconnecting tunnels and levels. Do the worker ants have some mighty engineer-ant directing this process? Nope. Each of them is single-mindedly performing its own simple task, and instincts program them. There is in fact an ant-engineer, but it\u2019s their own DNA that\u2019s organized their work to accomplish the goal. That\u2019s a bit like how most network AI works.\n\nNetworks are made up of a bunch of technology \u201ccollections\u201d, each a bit like an anthill. There are collections based on vendor, on device type, on physical location, and on connection relationships. If you look at network AI today, it operates mostly on collections. Maybe it manages Wi-Fi or maybe edge elements like SD-WAN or SASE.\u00a0 AI applications to manage a collection have the management objectives built into their DNA, their design.\u00a0 We know how Wi-Fi works if we\u2019re a Wi-Fi vendor, and we build that knowledge into our AI management.\nThe challenge comes when we stop looking at collections as independent elements and start looking at networks as collections of collections.\u00a0 A network isn\u2019t an anthill, it\u2019s the whole ecosystem the anthill is inside of including trees and cows and many other things. Trees know how to be trees, cows understand the essence of cow-ness, but what understands the ecosystem? A farm is a farm, not some arbitrary combination of trees, cows, and anthills. The person who knows what a farm is supposed to be is the farmer, not the elements of the farm or the supplier of those elements, and in your network, dear network-operations type, that farmer is you.\nIn the early days, the developers of AI explicitly acknowledged the separation between the knowledge engineer who built the AI framework and the subject-matter expert whose knowledge shaped the framework.\u00a0 In software, especially DevOps, the management tools aim to achieve a goal state, which in our farm analogy, describes where cows, trees, and ants fit in. If the current state isn\u2019t the goal state, they do stuff or move stuff around to converge on the goal.\u00a0 It\u2019s a great concept, but for it to work we have to know what the goal is. We need, at the level of an enterprise network, the knowledge that our Wi-Fi expert subliminally introduced into the Wi-Fi AI management tool. If an AI vendor doesn\u2019t know how that knowledge is obtained, their AI can\u2019t help.\nBefore you decide that your hopes for AI are forever dashed, take heart!\u00a0 Many network-operations types are perfectly happy with AI that manages the collections of technology that make up their network.\u00a0 After all, why worry about coordinating Wi-Fi and SD-WAN management when whatever happens with one can\u2019t be remedied by jiggling the other?\u00a0 If this collection-AI model fits your needs, you\u2019re home free.\nA good way to see if it is OK being an ant (network AI-wise, at least) is to ask whether your technology collections are really atomic\u2014totally independent, self-contained. It comes down to the visibility and control scope of your AI.\u00a0 Collection-specific AI keeps to itself, basically.\u00a0 Ideally, you need your AI collection ants to do their own thing, without stepping into one another\u2019s activity.\u00a0 You don\u2019t want AI in one place to be looking over into another collection and reacting to conditions, or two AI-collection processes working on the same problem at the same time, without coordination.\nIf the remedies for issues in one collection might involve doing something to another collection, then you need your AI to rise up and cover the combination. So, if you see an expensive and overworked network operations center that manages ecosystemic problems and wonder whether AI could let everyone take a coffee break, you need some deeper insight into vendor AI claims.\u00a0 That\u2019s not easy for enterprises, because more than three-quarters of those I\u2019ve chatted with this year say that they don\u2019t have much, if any, AI expertise in-house.\u00a0 Many feel like they\u2019re at the mercy of vendors, who promise great things and don\u2019t seem to quite deliver what\u2019s expected.\u00a0 Is there nothing an enterprise can do?\nThe easiest way to get a handle on using AI for an entire network ecosystem is to look for a strategy that\u2019s kind of like the old \u201cmanager of managers\u201d approach.\u00a0 In modern terms, you could call this intent modeling.\u00a0 If each of your technology collections can be treated as a black box that models its behaviors against its own SLA, and if its AI process works to enforce that SLA, then all you need is for each of those collection AI tools to generate a failure report to a higher-level package. That package can then decide what to do if there\u2019s either a problem that goes beyond a single collection of technologies, or if one collection just throws in the towel and a higher-level fix has to be considered.\nThe challenge here will be finding that goal state and how to get back to it when something goes wrong.\u00a0 Remember those subject-matter experts and knowledge engineers?\u00a0 It\u2019s difficult to frame an AI solution to a network because all networks are a bit different, and only the users know what they consider \u201cgood\u201d or \u201cbad\u201d.\u00a0 Some AI tools may offer a machine-learning (ML) capability that looks over the shoulder of your NOC people and learns what to do, and some may use a baseline that a network vendor knows would usually represent normal options and common remedies.\nBoth approaches have some issues.\u00a0 Machine learning can take time, and while your AI system is learning its mission, it can drain your NOC\u2019s resources further.\u00a0 Vendor baselines work best when a network is largely made up of equipment from one vendor.\u00a0 Both can be tuned up, but both can run afoul of adaptive network behavior.\nIP networks essentially use topology discovery and do their own thing.\u00a0 Influencing the routing is difficult even for the NOC; they\u2019d often have to plan new MPLS routes to do traffic engineering, something AI isn\u2019t likely to do.\u00a0 Some companies (including Google) have gone to software-defined networking (SDN) to provide central control of routing, and AI could then control the network by controlling the SDN controller.\nAI in network operations goes back to the combination of events to signal changes, and a way of implementing an effective response.\u00a0 At any level, your prospective AI vendors should be able to talk you through how their offering gathers information, and how it implements its insights.\u00a0 Dig into the detail of those two things, because whatever magic AI claims to work, it won\u2019t work it without those two ingredients.\u00a0 Be a farmer, not an ant.