For the past few years, much of the conversation around AI has centered on capability. We have watched it move from generating content and summarizing information to analyzing data, writing code and completing increasingly complex tasks. Now, with the rapid development of AI agents, we are beginning to hand over not just tasks, but portions of workflows and decisions as well.
That progression has understandably raised questions about what happens to people as AI becomes more autonomous, but we are looking at that question the wrong way. The bigger change will be in what we need people to do. When AI tools can handle more of the execution, people have to spend more time thinking about the system around it. We need to ask ourselves important questions to do this:
- What are we actually trying to accomplish?
- What information does the technology need to make a good decision?
- How should work move from one step to another?
- When can AI make a decision on its own, and when does someone need to get involved?
These are questions companies already deal with today, but AI raises the stakes because decisions and actions can happen much faster and at a much greater scale. Take a marketing organization as an example.
We are already at a point where AI can analyze an audience, develop content, adapt that content for different channels, check it against requirements and evaluate the results. It is not difficult to imagine those capabilities becoming connected so that one action automatically triggers the next.
The strategy comes in the coordination
The challenge is that every part of that process can work exactly as designed and still produce a bad result. An audience model could identify the people most likely to respond to an offer while missing the customers the business most wants to develop over time.
A content agent could produce something likely to generate clicks but inconsistent with where the brand wants to go. A system evaluating performance could make a perfectly reasonable recommendation based on the metric it was given, even though that metric does not reflect the larger business objective.
Those are not necessarily failures of the AI. In many cases, the technology has done exactly what it was asked to do. The failure happened earlier, when the objective, context or connections between decisions were not designed well enough. This is where the role of people becomes increasingly important.
Someone has to understand the broader business context and make sure the individual decisions being made across a workflow ultimately support the same goal.
Anyone who has spent time inside a large enterprise has seen a version of this before. Over the past two decades, companies bought specialized technology for almost every business need: CRM platforms, marketing automation systems, analytics tools, project management software, content platforms and countless point solutions. Each solved a real problem, but companies then had to figure out how to make all of those systems work together.
Teams built integrations, moved data between platforms, reconciled conflicting information and created processes to bridge gaps between systems that had never been designed to operate as one. In many organizations, employees effectively became the integration layer, carrying context from one platform or team to the next.
As companies adopt AI across functions, there is a very real possibility that we recreate the same problem, only much faster and with technology that can independently make decisions and take action.
It is relatively easy for a department to adopt an AI tool or build an agent that solves a particular problem. As that happens across an enterprise, however, organizations could quickly find themselves with large numbers of models and agents acting on different data, following different instructions and optimizing for different outcomes.
The individual components may be far more intelligent than the software systems that came before them, but that makes coordinating them even more consequential and increases the importance of having a strategy that extends beyond any individual agent or use case.
Finding the balance is key
Human strategy becomes critical in designing that larger system, but that should not be confused with putting a person behind every AI decision. Requiring someone to approve every output would defeat much of the purpose of creating autonomous systems in the first place.
Companies will have to determine where they are comfortable allowing AI to operate independently, where monitoring is sufficient and where the consequences of a decision justify direct human involvement.
That balance will probably look different across every organization and even across workflows within the same company. A routine decision with clear rules and limited consequences may require very little intervention, while a decision affecting a customer relationship, a brand, significant spending or strategic direction deserves a different level of scrutiny.
There will also be situations where the system simply needs to recognize that something unusual has happened and bring a person back into the process. Determining those boundaries and designing the workflow around them may ultimately matter more than the individual model or agent a company chooses.
Adapt and adjust, as technology changes
The technology underneath all of this will continue to change quickly. Models that seem dramatically better than their competitors today may be overtaken within months, and capabilities that currently feel differentiated will become widely available. Companies cannot assume that access to a particular model will provide a lasting advantage, especially as the gap between competing technologies continues to narrow.
What will be much harder to replicate is the thinking that surrounds the technology: an organization's understanding of its customers and operations, the way its workflows have been designed, the context it gives its AI systems and the judgment it applies to the decisions that matter most. Those things are specific to the business, and they become more valuable as the underlying technology becomes more widely accessible.
Greater AI autonomy therefore does not lead to a future with less need for human intelligence. It creates a greater need for people who can look across the entire system and understand what the technology is being asked to accomplish, whether the pieces are working together and whether the outcome actually makes sense for the business.
We are going to get very good at building AI that can do things on our behalf. The harder challenge will be making sure all of those things add up to something worth doing.
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