In the past couple of years, companies have typically viewed AI as a very usable tool—to help clean up spreadsheets, respond to emails, or possibly change the grammar in slide decks or emails. That is not, by itself, a bad thing—it was fun and novel until there was a name for it. Fun and novel aren’t the disruptor it could be, and should be, in the truest form.
Now, that being said… there is another story in the background. Quietly, and perhaps strategically, AI tasks are doing more than just responding to prompts. AI tasks are now beginning to step up; in some cases, they are acting autonomously—still early to be asked. This is where agents come into play. AI tasks are busy working behind the scenes to observe, decide, and act. This is a higher level of activity for AI tasks and is changing enterprise IT faster than many businesses could have imagined.
From Content Creation to Taking Charge
Lately, many companies have begun to use AI as a basic tool—maybe to help clean up spreadsheets, write email responses, or make an edit to grammatical conventions within a slide deck or email—and it is not necessarily bad; it was cool and novel until it received a label. Cool and novel will never be considered the disruptive feature in the most conventional sense of that term.
However, there is a different story when we dig a little deeper. On the fringe, the AI continues to perform. The AI task is now processing in a way that is potentially more than just making a response to a prompt. In some situations that are beginning to bubble to the front, the AI task is even able to take things to the next level—and in some cases is almost acting autonomously—or is at least acting in a way that does not require asking a human for more input or context. This is a world of agents. Tasks are being completed in real time in the background with AI that listens, sorts, and acts. This is next-level activity with AI, and the consumption rate is—and will continue to be—significantly faster than enterprise IT can digest, increasing at such a rate that the enterprise is unlikely to ever fully anticipate.
When AI Stops Waiting
For instance, let’s consider if you have trucks that are ultimately going to be late delivering packages for some factor(s). It could be rain, it could be traffic, or it could be something else entirely. Ordinarily, there would be someone who would know that the trucks were going to be late, how to reroute the drivers, and maybe even have alerted someone in the process. A normal human can do all of these things without fancy maths or knowing states and situations in real time (wherever that might be). It’s simple effort and simple stress on a person.
Or take the example of the customer experience. A chatbot can answer questions, react, and help based on most instances, most orders, routine questions, etc. But when there is a question or thought that is slightly more complex, the chatbot effectively pushes you off to someplace else (on the forms or websites or continuing around the alternate method or devoting agents to take the call).
Moving Past the Copilot Stage
For a while, the literature has referred to AI as a copilot, meaning that it works only alongside humans and does not replace humans. In that moment, it was not overly difficult to feel confident. In fact, at that point in time, taking leave was likely the best course of action. Humans simply wanted to see the technology successfully deploy itself before handing over the keys.
But here we are, starting this process—that wheel is already self-driving. These new systems don’t just ideate observations, but are starting to take it upon themselves not to hold the human hand to act out the tasks. The systems know what we are working on and just do it.
This will not happen overnight. More likely, organizations will just dip their toes in the water and automate much lower risk. But over time, the value perception of the stage and of the delivery of good development into two operations and to users—who sometimes have little time outside of panel—want the level items more, and more efficiently, differently. Here now, suddenly, a human is in cognitive interaction on all—or more—functions, while the agent is independently working on lower-order details!
Smarter Than They Look
Individuals are amazed at the possibilities of these agents’ flexibility. Agents do not always care about the instruction set they received. They learn. If something was not executed precisely the same way in the initial test experiment, the agent can adapt its behavior based upon that learning. The agent does not need to be instructed to exhibit behavior every single time or at all; it actually learns specific behaviors and, over time, can develop and alter its behavior reliably.
In healthcare, devices are currently doing just this, and for their patients, they are learning to recognize a pattern, detect that something is wrong, and alert their appropriate provider in real time. This is real-time decision-making, and it is happening all the time—not just collecting the data and reporting it.
And even in finance, they are collecting data from the news, determining to allocate, appropriate, or transfer funds, assessing risk, and it is all real time; no longer monthly, weekly, or even daily—it is happening while we speak. They are making those decisions faster and more accurately than humans could reliably make in hours, let alone throughout hours.
Automation? No. Autonomy
Most individuals do not fully master the difference between independent and automated operations. Even though it is rather different in definition to be operational from defining operations, putting this into simpler terms: automation is controlled; autonomy is self-directed. That distinction is important in IT.
In the realm of enterprise systems, where the operations are vast and complex, intelligent agents are working with the hundreds of things that you simply do not have the time to monitor. Services do not just run—they are identifying problems sooner rather than later, and are not losing anything in their processes. It is resemblant to having a young analyst on staff that you do not pay and do not provide benefits to—an analyst that cannot get bored, distracted, or fatigued!
Humans Still Lead — Just Not Every Task
Does this imply that humans are worthless? Absolutely not. Humans are still required to set expectations, evaluate exceptions, and change rules. Drudgery? That is probably gone forever. And (for the most part) people are happy it’s gone forever.
People no longer work from 100 browser tabs and converse about the same thing for the fifth time. People are really thinking now—thinking about strategy, thinking about improvement. It is not less work—it is just better work.
It’s Already Happening
This has already occurred in the past; this is happening in the present, not in the future. Companies are now beginning to use these agents for the entirety of their business sectors, and they are currently doing so unnoticed. It has not yet made the public headlines, but the benefits of these systems are already starting to show—they are faster to turnaround, mistakes are reduced, and we have fewer employees.
It is not about catchy headlines anymore; it is about systems that just work better… and that should be a concern for every business leader.
