Companies have been touting the use of AI for text creation, data analysis, or providing answers to customers’ queries for years now.
It is about to change.
Next generation AI technologies are no longer limited to just reacting to prompts. AI agents can be trained to plan, utilize applications, access data and complete complex workflows.
Which means even greater opportunities for businesses.
From Responses to Actions: What AI Is Doing Now
Conventional AI often works on a prompt basis.
It will respond to your prompt with a relevant answer.
With an AI agent you can go further.
Provide it with a goal and it may determine all the actions that need to be taken to achieve this goal.
Instead of instructing a worker to gather the data on sales, process it, generate a report, and pass it on to their boss, an AI-powered workflow may handle all the actions automatically.
The key difference here is subtle:
Chatbots provide responses. Agents can help complete actions.
Why Businesses Care
Any company has repetitive processes.
People waste countless hours:
Transferring information across different platforms
Generating reports
Searching internal documents
Updating records
Scheduling meetings
Checking standard requests
Analyzing operational data
This work may not involve any creativity, but it takes a lot of time.
AI agents can help automate this work.
The Largest Potential Does Not Involve Fully Autonomous Operations
There is a belief that organizations will gradually fully automate all processes using AI agents.
This is likely to occur in some cases, but this is not required for AI to generate substantial value.
A business may choose to automate only one process within its workflows:
Customer request → AI classifies query → pulls relevant data → formulates response → human verifies response → customer gets answer
The person is still required in this process, but most of the repetitive activity goes away.
This approach is much more realistic than trying to build an autonomous agent right away.
An AI Agent Requires Much More Than a Strong AI Model
The latter is only a part of an agentic system.
Some other required components include:
Interactions tools to allow AI agent to communicate with the organization's systems.
Memory to keep relevant context.
Access to the data to get relevant information.
Security to restrict access to certain types of data.
Monitoring the behavior of the AI agent.
Performance evaluation.
Human involvement for certain sensitive decisions.
Without all of these components, an outstanding demo of an AI can become a fragile business system.
The Role of People Changes
AI does not necessarily make people unnecessary.
Instead, it changes what kind of activity people perform.
People may have more time for information interpretation rather than its retrieval.
Managers would spend less time producing reports and more time on decision-making.
Developers would spend less time writing trivial code and more time designing systems.
Customer service representatives would spend less time handling routine queries and more time dealing with complicated cases.
The aim of AI implementation here is not to remove people.
The aim is to increase the number of meaningful things humans can do.
The Impact Gets Even More Interesting When AI Agents Interact with the Physical World
Think of an industrial system that constantly gathers information from sensors.
An AI system can spot anomalous patterns, diagnose the cause, suggest an action, and start an appropriate workflow.
The link between AI and:
IoT sensors
Robotics
Manufacturing systems
Edge computing
Cloud platforms
Industrial databases
Automated workflows
Gives birth to something much more advanced than just chatbots.
It's an intelligent system capable of observing, reasoning, and acting upon.
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The New Competitive Advantage
As more and more AI tools become available, access to such tools will probably not give companies much of an advantage anymore.
The competitive advantage will be gained by those who can successfully integrate AI into their operations.
Let's take two companies.
Both apply the same AI model.
One uses it to produce content now and then.
Another one connects this model to its data, software, workflows, and systems.
The latter company will get more value for itself.
Thus, AI strategy will become more and more an infrastructure and workflow challenge, not a software one.
Start Small, Then Scale
Companies do not have to automate everything at once.
What they should do instead is choose one of repetitive processes.
Then automate it.
Measure the outcomes.
Optimize the process.
And move to another workflow.
It's less risky and proves the business value of the technology.
The companies that win with AI will probably not be the ones who have the largest number of agents.
They will be the ones who can figure out the processes worth automation.
Conclusions
AI agents signify a significant change in the way the business adopts artificial intelligence.
The technology moves from producing answers to executing actions.
However, there's more to success than autonomy in this case.
The companies will have to have reliable data sources, secured integration, proper governance, monitoring and human judgement.
Probably, the future will not be the man vs AI.
It will be humans who will guide intelligent machines to do some of the monotonous tasks.
And for those who figure out how to leverage these two together, there could be a lot of potential going forward.
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