Artificial intelligence has already changed how many people write, research, analyse information and communicate. But the next shift may be more significant: AI systems that do not simply answer questions, but can also complete tasks.
These systems are known as AI agents.
An AI assistant might help an employee draft a customer email. An AI agent could identify the appropriate customers, personalise the message, send it through an approved platform, record the activity and schedule follow-up actions.
That difference—from generating an answer to pursuing an outcome—is what makes AI agents particularly relevant to business leaders.
What is an AI agent?
An AI agent is software that can interpret a goal, decide what actions are needed and use available tools or information to work toward that goal.
Depending on how it is designed, an agent may be able to:
- Retrieve information from business systems
- Analyse documents or data
- Decide what to do next
- Use software applications and digital tools
- Communicate with customers or employees
- Complete multistep workflows
- Ask for human approval when necessary
- Learn from feedback and previous outcomes
Consider a conventional customer-service chatbot. It answers questions using a predefined knowledge base. An AI agent could go further by identifying the customer, reviewing their account, checking an order, determining whether they qualify for a refund, preparing the transaction and escalating unusual cases to an employee.
The agent is not merely providing information. It is helping move the work forward.
How are AI agents different from automation?
Businesses have used automation for decades. Traditional automation works well when a process follows consistent rules: when one event happens, the system performs a predetermined action.
For example, a company might automatically send a confirmation email whenever a customer submits an order.
AI agents can work with less predictable situations. They can interpret unstructured information, evaluate context and choose between different actions. This makes them potentially useful for work involving emails, conversations, documents and decisions that cannot be reduced to a simple set of fixed rules.
The distinction is not absolute. Most effective agent-based systems combine AI with conventional automation. The AI interprets the situation or recommends an action, while reliable software executes clearly defined steps.
Where can AI agents create business value?
The value of an AI agent does not come from its novelty. It comes from its ability to reduce delays, coordinate fragmented work and allow people to concentrate on decisions that require experience or judgement.
Several functions offer immediate possibilities.
Sales
Sales teams often spend significant time researching accounts, updating records and preparing for meetings. An AI agent could collect relevant company information, summarise previous interactions, identify potential needs and prepare a briefing before a salesperson contacts the prospect.
It could also monitor opportunities for inactivity, recommend the next action and draft personalised follow-up messages. The salesperson would retain control of important relationships while spending less time on administration.
Marketing
Marketing agents could help teams research audiences, repurpose content and coordinate campaigns across different channels. They might analyse performance data, identify unusual changes and suggest where budgets or messages need attention.
The greatest benefit may be speed. Rather than waiting for reports from several systems, a marketing leader could receive a concise explanation of what changed, why it may have happened and which actions deserve consideration.
Customer service
An AI agent can review a customer’s history, classify a request and retrieve the information needed to resolve it. For routine cases, it may be able to complete the process. For sensitive or complicated issues, it can prepare the context before transferring the customer to an employee.
This can shorten response times while reducing one of the most frustrating customer experiences: repeatedly explaining the same problem to different people.
Operations
Many operational processes depend on employees moving information between emails, spreadsheets and business systems. Agents can help monitor incoming work, extract relevant details, detect missing information and route tasks to the right person.
Procurement is one example. An agent could compare supplier quotes, flag unusual terms and prepare a recommendation. A manager would still make the decision, but much of the preparatory work could be completed automatically.
Finance
Finance teams could use agents to classify expenses, investigate discrepancies, prepare cash-flow summaries or follow up on overdue invoices. An agent might also monitor transactions and bring exceptions to an employee’s attention.
Because finance involves sensitive information and material consequences, these applications require particularly strong controls. Nevertheless, they show how agents can support professional judgement without replacing accountability.
Human resources
AI agents may assist with interview scheduling, employee questions, onboarding and internal policy searches. A new employee, for example, could use an agent to find relevant documents, complete administrative tasks and identify the people they need to meet.
Employment decisions should remain subject to careful human oversight. Agents are generally better suited to reducing administrative friction than making consequential decisions about people.
From individual tools to coordinated workflows
Many organisations already have employees using AI independently. One person uses it to write emails, another to summarise meetings and another to analyse a document.
These uses can save time, but their impact is often limited to individual tasks. AI agents create a larger opportunity because they can connect multiple steps in a workflow.
Imagine a potential customer submitting a website enquiry. An agent could:
- Check whether the record already exists.
- enrich the account with approved information.
- classify the enquiry by urgency and fit.
- route it to the appropriate salesperson.
- prepare a concise account briefing.
- draft a personalised response.
- create a follow-up task if no action is taken.
The result is not just faster writing. It is a faster and more consistent business process.
What are the risks?
Giving AI the ability to act also introduces risks that do not arise when it simply generates text.
An agent may misunderstand a request, rely on incorrect information or take an inappropriate action. It may expose confidential information if its access is too broad. It can also reproduce errors at scale: a flawed manual decision might affect one customer, while a flawed automated process could affect thousands.
The principal risks include:
- Inaccurate decisions or outputs
- Excessive access to sensitive systems
- Privacy and security failures
- Actions that are difficult to explain or audit
- Regulatory and contractual breaches
- Poor customer experiences
- Overreliance on automation
- Unclear responsibility when something goes wrong
These risks do not mean businesses should avoid agents. They mean agents should be deployed with controls proportionate to the consequences of their actions.
An agent that organises internal meeting notes requires different safeguards from one that approves refunds, changes prices or communicates legal commitments.
Why process design matters
AI cannot compensate for a process that lacks clear ownership, reliable information or an agreed definition of success.
Automating a poorly designed workflow may simply allow the organisation to make mistakes faster. Before introducing an agent, a business should understand how the work is currently completed, where delays occur, which decisions require judgement and who is accountable for the outcome.
This process often reveals that the greatest obstacle is not the technology. It is fragmented data, unnecessary approvals, conflicting policies or systems that do not communicate with each other.
Introducing an AI agent can therefore become an opportunity to redesign the work itself.
How should a business get started?
The most effective starting point is usually a narrow, frequent and measurable process—not an ambitious attempt to automate an entire department.
Look for work that:
- Happens frequently
- Consumes substantial employee time
- Depends on gathering information from several places
- Has a recognisable beginning and end
- Follows reasonably consistent policies
- Can be reviewed before consequential actions occur
- Produces an outcome that can be measured
Businesses should establish a baseline before implementation. How long does the process currently take? What does it cost? How often do errors occur? How do customers and employees experience it?
The initial agent can then operate with limited permissions and clear escalation rules. Its work should be monitored, and important actions should require human approval. Access can expand gradually as the organisation develops evidence that the system is reliable.
What role will people play?
AI agents are unlikely to remove the need for human judgement. They are more likely to change where that judgement is applied.
Employees may spend less time gathering information, moving data and completing routine coordination. They may spend more time handling exceptions, building relationships, resolving ambiguity and improving the systems through which work gets done.
This change will require more than technical training. Organisations will need to decide who supervises agents, who can change their instructions, how performance is assessed and who remains accountable for each outcome.
The businesses that benefit most will treat agents as part of the workforce design—not merely another software purchase.
The opportunity ahead
AI agents represent a shift from software that waits for instructions at every step to software that can help pursue a defined objective.
Their immediate value is practical: faster processes, lower administrative burdens, more consistent service and better access to information. Their longer-term significance may be more profound. As agents become embedded in everyday operations, businesses will be able to rethink how work moves between people, teams and systems.
The question is therefore not simply whether AI agents can perform particular tasks. It is where greater speed and autonomy would genuinely improve an outcome—and where human involvement remains indispensable.
Companies that answer those questions carefully will be better positioned to turn AI agents from an interesting experiment into meaningful business value.
If you’re looking to utilise an AI agent to enhance the speed and accuracy which your business can undertake a set of given tasks, feel free to reach out to the team at OSE.
