It’s easy to talk about AI, which many IT teams do today as they tout new tools and services that are supposedly powered by AI.
It’s much harder, however, to put AI to work in a practical, real-world way. When you look closely at many types of so-called AI solutions that are currently available, you realize that there is a gap between what they can do with AI in theory and what they offer in reality.
The secret to closing that gap is AI operations, an area of functionality that is absent from many AI tools today. Here’s a look at what AI operations means and why it’s so critical for making practical use of AI.
The False Promise of AI
You need not be a senior technology analyst to know that everyone, everywhere is talking about AI these days. From refrigerators to razors to software systems of all types, AI is supposedly revolutionizing everything under the sun
A closer look at many of these solutions, however, reveals that they are not necessarily as disruptive as they may appear at first glance. There are two main reasons why.
Loose Definitions of AI
The first is that many vendors use the term “AI” quite loosely. In their vision, any type of solution that analyzes data in any way in order to glean any kind of automated insight or recommendation is an example of AI.
Maybe that’s fair. There is no universal or official definition of AI. Broadly speaking, you can make an argument that any kind of system that analyzes data to make inferences in a way that is similar to the thought process of a human counts as AI.
The problem here, however, is that if this is your definition of AI, then AI isn’t very revolutionary at all. Is it AI when your car detects that your fuel tank is low and turns on a warning light? Is it AI if a circuit breaker in your house detects excessive voltage and shuts off the current? Is it AI if your monitoring tool detects that your server is running out of disk space and generates an alert? Arguably, yes—but these are all examples of technologies that have existed for decades, and they’re hardly very innovative.
The takeaway here is that, while you can place the “AI” label on many things, only some of them are truly innovative. If you want to leverage AI effectively today, you need to look for solutions that go beyond the basics of detection and alerting. You need tools that can proactively solve problems for you, or at least make intelligent recommendations that are more sophisticated than “add more disk space” or “put more gas in your tank.”
Poor AI Operations
The second shortcoming of many AI solutions today—including some that do go above and beyond the sorts of primitive use cases described above—is that they don’t offer efficient solutions for making use of the AI services they deliver.
It’s great if your fridge can tell you when you’re running low on food, or if your monitoring tools can tell you when an application is under-performing. But if all these solutions do is inundate you with alerts, they don’t put you in a strong position to take practical advantage of their AI-powered insights.
To do that, you need a way to operationalize AI, which means taking practical, impactful actions based on the information that AI tools generate for you. That’s what AI operations allows you to do.
Examples of AI Operations
AI operations could take multiple forms. They could involve automatically prioritizing different types of alerts so that your team knows which ones matter most. It’s probably less critical for your AI-powered fridge to tell you that you’ve run out of hot sauce that you only eat a few times a month than it is to know that you’re almost out of a staple like milk or eggs. Likewise, a team using an AI-powered monitoring solution needs to be able to distinguish clearly between the priority levels of a problem in a testing environment and a problem in production.
AI operations could also involve automatically remediating problems for you. Granted, your smart refrigerator may not be able to go out and buy more food for you. But in the software realm, AI-powered solutions can perform many remediation tasks automatically. Instead of simply generating alerts when a performance problem arises, they can actually fix them by, for example, changing resource allocations or restarting a failed application instance.
In complex scenarios, fully automated remediation may not be possible. But even then, AI operations can offer detailed recommendations and insights to help human engineers fix issues. If your monitoring software isn’t able to determine the exact root cause of a performance issue, for instance, it could at least use AI to rule out some possibilities, which will help engineers determine the root cause of the problem faster.
Or, if your tools determine that your application environment is running out of storage space or memory, they could make recommendations about how to assign more. In other words, they could tell engineers exactly how much additional storage to provision and how to allocate it across a distributed environment in order to optimize performance. That’s an example of taking things a step further than merely noting that a problem exists and recommending a generic solution.
These are all examples of ways that AI operations make it possible to leverage the full promise of AI tools. Without AI operations, even the most sophisticated AI engines lack the ability to make a meaningful impact in practice.
Conclusion
There’s no denying that AI has revolutionary potential. But merely implementing basic AI functionality that does things like generate alerts about anomalies is not revolutionary. Nor are AI-powered solutions that offer great insights, but lack a practical way of applying those insights.
To make practical use of AI, you need AI operations solutions that go beyond the basics. You need tools that proactively solve problems for you, or at least empower your team to take effective, efficient action based on the insights generated by AI.
