AI agents are becoming one of the most discussed technologies in business. Unlike conventional chatbots that simply answer questions, agents can gather information, make decisions within defined limits and complete actions across business systems.
An AI agent might qualify a sales enquiry, prepare a quotation, update a customer record and schedule a follow-up. Another could monitor inventory, identify unusual demand and notify the appropriate manager. The technology is promising—but installing an AI tool is not the same as improving a business.
The better question is not, “How can we use an AI agent?”
It is:
“Which repeated business process can we make faster, more reliable or less expensive?”
What is an AI agent?
A traditional chatbot waits for a question and returns an answer. An AI agent can be given an objective and authorised to complete multiple steps towards that objective.
Consider a customer-service enquiry.
A chatbot may explain your return policy. An appropriately designed agent could:
- Identify the customer.
- Retrieve the relevant order.
- Check whether it meets the return conditions.
- Prepare a return request.
- Send it to an employee for approval.
- Update the customer after approval.
This ability to move from conversation to action is why agents are attracting so much attention.
Microsoft’s 2026 Work Trend Index describes a shift in which agents take on more execution while people retain responsibility for direction, judgement and outcomes. The research covered 20,000 workers who use AI across ten markets. Its strongest lesson is not that businesses should remove people from work, but that they should redesign work so people and AI can contribute where each is most effective. Microsoft 2026 Work Trend Index
Where AI agents can create practical business value
The most valuable opportunities are often found in ordinary processes—not spectacular demonstrations.
Customer service
An agent can classify enquiries, retrieve approved information, prepare responses and route complex or sensitive matters to an employee.
This can reduce response times without allowing AI to make decisions that require empathy, negotiation or managerial authority.
Sales administration
Agents can capture leads, enrich contact information, draft follow-up messages and update customer relationship management systems.
Salespeople spend less time moving information between applications and more time speaking with potential customers.
Document processing
Businesses regularly receive invoices, application forms, reports, contracts and supporting documents. An agent can extract relevant information, validate required fields and direct exceptions to the right employee.
The objective is not simply to “read documents with AI.” It is to shorten the complete journey from receiving a document to making a decision.
Internal knowledge
An agent can help employees find policies, technical documentation, product information and approved procedures across an organisation’s knowledge base.
A secure internal agent can be more useful than a general chatbot because its answers are grounded in information relevant to the business.
Operational monitoring
Agents can monitor sales, inventory, service tickets or system performance and bring unusual conditions to human attention.
For example, an agent might identify declining stock levels, prepare a replenishment recommendation and wait for an authorised manager to approve the order.
Five questions to answer before building an AI agent
1. Is the process clearly defined?
AI cannot repair a process nobody understands.
Before introducing an agent, document:
- What starts the process?
- What information is required?
- Which systems are involved?
- Who can approve an action?
- What should happen when something goes wrong?
- How will success be measured?
If employees complete the same task in completely different ways, process design should come before automation.
2. Is the underlying information reliable?
An agent operating with inaccurate, incomplete or outdated information will produce unreliable results more quickly.
Review the quality of customer records, product information, internal documents and operational data before connecting them to AI. Define which source is authoritative and who is responsible for keeping it current.
AI readiness is often data readiness in disguise.
3. Does the agent need to recommend or act?
There is an important difference between an agent that recommends an action and one that performs it.
A recommendation might say:
“Inventory for Product A is likely to fall below the required level within five days.”
An action might automatically create and send a purchase order.
The second option carries greater operational and financial risk. Businesses should begin with assistance and approval-based workflows before granting greater autonomy.
4. What is the cost of an incorrect action?
Not every mistake has the same consequence.
An imperfect internal summary may be easy to correct. An incorrect payment, deleted customer record or unauthorised disclosure of personal information could be serious.
Use the potential impact to determine:
- Required human approvals
- Transaction limits
- Access permissions
- Logging and monitoring
- Testing requirements
- Conditions that immediately stop the agent
5. Can you measure the result?
A successful demonstration is not the same as a successful deployment.
Define a baseline before development. Depending on the process, useful measures might include:
- Average completion time
- Cost per transaction
- Response time
- Error or rework rate
- Percentage of cases requiring human intervention
- Customer satisfaction
- Revenue conversion
- Employee time recovered
If there is no meaningful way to measure improvement, reconsider the use case.
Security must be designed into the agent
An AI agent may need access to emails, documents, customer records, financial systems or operational platforms. That access creates risk.
In 2026, the US National Institute of Standards and Technology reported widespread agreement among industry respondents that AI agents introduce new security threats. NIST also emphasised that established cybersecurity principles remain relevant but must be adapted for agent-based systems. NIST analysis of AI-agent security
One particularly important issue is identity: organisations must be able to determine which agent performed an action, who authorised it and what permissions it had. NIST’s work on agent identity and authorisation highlights identification, auditing, non-repudiation and protection against prompt injection as important considerations. NIST agent identity and authorisation concept
A business-ready agent should therefore have:
- Its own identifiable account
- Only the minimum permissions it requires
- Approved sources of information
- Complete records of actions and approvals
- Human confirmation for sensitive operations
- Clearly defined transaction and authority limits
- Protection against malicious instructions in external content
- A reliable way to pause or disable it
Do not give an agent unrestricted access simply because connecting everything makes the demonstration easier.
A practical route from idea to implementation
Step 1: Discover
Identify repeated, time-consuming processes with clear rules and measurable outcomes. Select one valuable but manageable use case.
Step 2: Map
Document the current workflow, people, decisions, systems, data sources and exceptions. Establish baseline performance.
Step 3: Prototype
Build a limited version using representative information. At this stage, the agent should recommend or prepare actions rather than execute sensitive transactions independently.
Step 4: Validate
Test accuracy, security, exceptional cases and user experience. Confirm that employees can understand, review and correct the agent’s work.
Step 5: Integrate
Connect the validated agent to the required business systems using carefully restricted permissions and reliable interfaces.
Step 6: Monitor and improve
Track outcomes, costs, errors and human interventions. Agent behaviour should be reviewed continuously—not only when users report a problem.
The best first agent is usually a focused one
Businesses do not need an autonomous digital employee that understands the entire organisation on day one.
A better starting point is a focused agent that:
- Solves one well-understood problem
- Uses a limited set of trusted information
- Operates within clear authority boundaries
- Produces measurable value
- Escalates uncertainty to a person
Once the business has demonstrated value and developed the necessary governance, the agent can be extended responsibly.
Technology should strengthen the business—not complicate it
AI agents can reduce administrative work, improve response times and help organisations use their information more effectively. But the real value does not come from the model alone.
It comes from combining good process design, reliable data, secure integration, appropriate human oversight and continuous measurement.
At PBG Communications, we help organisations evaluate AI opportunities, improve the underlying workflows and develop secure solutions aligned with real business outcomes.
Before investing in an agent, start with one question:
What important work should become easier, faster or more reliable?
That answer—not the technology trend—should guide what you build.
