AI agents move far beyond everyday automation and are now shaping business management and leadership decisions.
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When it comes to the tasks that AI agents can perform, we focus on repetitive and everyday tasks. When a machine runs it over and over again, it's really good at the type of task the machine really gets.
Examples include writing and scheduling social media posts, solving everyday customer service issues, and labeling and classifying unstructured data.
If you're not speeding up, agents can be thought of as generative AI chatbots like ChatGpt, but have virtual arms, legs and the ability to take action on our behalf.
A slightly more technical definition is that it is a large language model (such as ChatGPT's GPT-5 engine) that can connect with other tools, including web browsers, to perform more complex and sophisticated tasks with minimal human intervention.
However, it would be wrong to think that Agent AI is only useful for low-level tactical decision-making on-site. What time should I send my email newsletter? Or which machinery maintenance work is required?
In fact, it is increasingly used by business leadership to make strategic or administrative decisions. McKinsey's study found that 53% of C-Suite executives and 44% of middle managers recently used Genai in their workplace, far exceeding the overall figures.
Here we look at some of the ways agents are already working on business management functions. We also provide some tips for those who want to make sure they are prepared for the convergence of agents and business leadership.
AI leadership?
No one expects the average CEO to be ready to hand over control of his company to the machine any time soon. However, there are many theoretical and many real-world use cases for agents in leadership and high-level decision-making.
One of the most visible is through integration with decision support systems often used to enhance human decision-making by ensuring the information and guidance needed.
For example, luxury goods manufacturer LVMH recently announced its decision to use agents to build a platform to monitor and surface signals so that decision makers can respond more quickly.
Asset Managers BlackRock has developed its own agent platform called Asimov. Asimov is ready to work all night, collect research data in real time, monitor market activity, scan company filings, and present executives with viable reports in the morning.
Citigroup also spoke about plans to divert a portion of its $12 billion technology budget to Agent AI. It can be used to connect a variety of business AI initiatives and improve strategic oversight.
Enterprise software vendors such as Microsoft and Salesforce are increasingly building agent capabilities into tools to help decision makers with strategic advice or deploy their own agents.
This means that as adoption grows among leadership across a range of industry leadership, agents are likely to be used in exciting new ways.
Healthcare, for example, can use agents to monitor and oversee an increasingly vast, connected health infrastructure. This helps leaders understand what generates value, what is lacking, and what is a waste of resources.
In manufacturing, agents can also track production operations, supply chain effectiveness, energy efficiency and create opportunities to play strategically.
Agents are still not getting business decisions, but they are increasingly in the passenger seat and ready to help them navigate or warn about upcoming troubles.
If you are a leader or decision maker, what steps should you take to ensure you are not left behind?
What should leaders and managers do now?
Here, business decision makers who already have experience in deploying AI will have an advantage. However, Agent AI requires a different approach and is more suited to the long-term strategic planning tasks on which the agent is being built.
Your agents are bigger, goal-oriented, and so should you think so. ChatGpt can draft emails, but agent platforms like Manus and Operator can create email marketing strategies and build infrastructure to deliver them.
Although, at least in theory, that can be done, it should be noted that early adopters of these tools frequently report that they are not always correct yet.
From a general usage perspective, it is important to remember that AI agents are not simply AI chatbots are evolving. They are not actually designed to chat or knock on ideas about the best way to create something. This is something ChatGpt special about.
Agent AI can be expensive as “thinking” chews many tokens. Therefore, it is best to clearly think about what you want and explicitly tell the agent how you want it to achieve it.
One of the great strengths of agents is that they can use digital tools whenever possible, from shopping online using a web browser to controlling complex industrial machines. Preparing for this involves allowing your own legacy system to convince you to play well with your agents. Or commit to open source technologies where standardization is common and popular tools are likely to be patched quickly to work in the agent ecosystem.
Next Tip: To get the most out of their potential, agents need to access high-value, timely streams of data through a well-documented API. Organizations that don't set up their homes in order here may miss out on the opportunity to catch up.
And absolutely critically, leaders should take steps to understand the principles of safe, responsible and ethical AI use. The need for guardrail, surveillance and accountability is more important than ever, as we take steps to empower AI to make more decisions on our behalf.
Autonomous organizations and the future of humanity in leadership
Machines are so good at predicting the right course of action that humans reach unneeded points? Can we even be responsible?
Well, maybe someday. It's pretty difficult to know where this technology is in two or three years, let alone ten years.
But one thing we know is that the human brain is much more complex than the best artificial brains. That additional complexity allows you to understand nuanced situations, navigate complex interpersonal relationships, and create complex, long-term strategic plans in ways that computers probably can't do for a while.
This means that human leaders are probably not yet ready to fully delegate their powers. Instead, they turn to their agents to help them become better leaders and become more effective decision makers.
