What are AI agents? Enterprise use cases and business impact

AI agents give enterprises a way to move work through a process instead of only generating a response. Within approved boundaries, they interpret a goal, retrieve relevant information, and trigger the next appropriate action in connected systems.

McKinsey & Company reports that 62% of respondents say their organizations are at least experimenting with AI agents. Its 2025 survey also found that 23% are scaling an agentic AI system somewhere in the enterprise.

For executive teams, the priority is converting experimentation into measurable operational performance. AI agents can reduce avoidable handoffs and help teams respond faster when each workflow has clear ownership and human intervention points.

Key takeaways 

• AI agents pursue defined business goals within approved operating boundaries

• Enterprise value comes from embedding AI agents in governed workflows

• Customer service, IT, and revenue operations offer practical starting points

• Human oversight is essential for sensitive decisions and material exceptions

• TP.ai framework connects AI agents with human expertise for measurable outcomes

What defines an AI agent?

AI agents are goal-oriented AI systems that can interpret context and complete approved actions across a workflow. They extend beyond a conversational interface by retrieving information and using connected tools. They escalate cases when a human decision is required.

AI agents in plain terms: An AI agent is a digital worker that can complete defined steps toward a business outcome, under rules set by the enterprise.

A large language model, or LLM, provides language and reasoning capabilities. An AI agent applies those capabilities to a specific workflow, such as reviewing a request or updating a record. It can route exceptions to the correct team when the decision exceeds its approved authority.

An AI assistant usually responds to a prompt within a conversation. An AI agent works across steps in a process when an enterprise connects it to approved data sources and systems.

How do AI agents work?

AI agents work by combining AI reasoning with access to approved information and tools. The enterprise defines the desired outcome, the data sources the AI agent may use, and the actions it may take without additional approval.

1. Receive a goal: The workflow starts with a request, event, or performance trigger

2. Interpret context: The AI agent reviews the relevant information and identifies the next task

3. Take an approved action: It retrieves knowledge, updates a system, or initiates a workflow step

4. Check the result: The AI agent assesses whether the action produced the intended outcome

5. Escalate when required: A human expert reviews exceptions, high-risk decisions, or sensitive cases

This design matters because agentic capability should not be treated as unrestricted autonomy. AI agents deliver more dependable results when they operate within a defined workflow and pass decisions to human experts at the right point.

Why are AI agents becoming operational priorities?

AI agents are becoming operational priorities because they can reduce friction in complex processes that depend on repeated handoffs and system updates. The strongest business case is a measurable improvement in resolution performance or cost-to-serve.

McKinsey’s survey indicates that adoption is moving, while enterprise scale remains limited. The gap between organizations experimenting and those scaling an agentic system demonstrates why leaders must focus on workflow design and operational controls.

Technology alone does not deliver an outcome. Leaders should begin with a specific performance issue, then identify where an AI agent can support a human team without creating uncontrolled decision-making.

Where can enterprises use AI agents?

AI agents are most effective in workflows that combine high volume with clear decision paths. They can handle routine activity and route complex work to human experts, improving responsiveness while protecting accountability.

The right use case begins with an outcome, not a technology feature. Enterprises should confirm that the workflow has reliable inputs and a performance measure that demonstrates business value. They should also define the escalation path before deployment.

What business impact can AI agents create?

AI agents can improve operational performance when they remove routine effort from workflows that already have clear rules and reliable information. The impact should be measured against a business outcome, rather than the volume of AI activity alone.

For customer service, a relevant measure may be resolution time or first-contact resolution. For IT operations, teams may track the time to triage recurring incidents. For revenue operations, the measure may be the speed at which a qualified prospect receives the next appropriate action.

Leaders should establish a baseline before deployment. They can then compare the AI-enabled workflow against the existing process, including quality, customer impact, and the rate of human escalation.

What challenges can enterprises face?

AI agent programs can underperform when organizations select a use case without clear process ownership or usable data. Weak system integration can also prevent the AI agent from accessing the information needed to complete an approved task.

Governance gaps present a material risk. If teams have not defined the actions an AI agent may take, the escalation triggers, and the method for reviewing performance, a promising pilot can create operational uncertainty at scale.

Change management is equally important. Employees need to understand how the AI agent supports their work and where human judgment remains essential. A clear operating model helps teams adopt the capability with confidence.

How should enterprises govern AI agents?

Enterprises should govern AI agents through clear boundaries and accountable owners. They should retain traceable decisions. Controlled autonomy lets an AI agent accelerate routine work while preserving the human authority needed for high-impact actions.

Before scaling an AI agent, leadership teams should answer the following questions:

1. Which business outcome are we improving? Identify a measurable result, such as a shorter resolution cycle or higher first-contact resolution

2. What actions can the AI agent take independently? Define the approved operating boundary for routine work

3. Which decisions require human approval? Establish clear escalation triggers for high-risk or sensitive cases

4. How will actions be logged? Retain the evidence needed for review and auditability

5. How will performance be monitored? Track quality and compliance alongside the targeted business outcome

Governance should sit inside the operating model, rather than operate as a separate review after deployment. This approach helps leaders detect weak performance early and refine the workflow without compromising control.

How can leaders select the right AI agent use case?

The right AI agent use case has a defined outcome and a workflow that can be understood before automation begins. Leaders should prioritize work that has repeatable steps, accessible data, and an identifiable human escalation point.

Early programs should focus on a manageable operational problem. A narrow initial scope allows the enterprise to test workflow design, governance, and measurement before it commits to broader change.

Once the model proves its value, teams can expand the AI agent’s role into adjacent processes. Integrating this expansion into a broader business transformation strategy helps the organization build confidence while maintaining visibility over commercial and customer impact.

How TP helps enterprises turn AI agents into measurable outcomes

TP approaches AI agents through an operating model where AI and human expertise work in sync. Through TP.ai Connect, AI agents and human expertise can be coordinated across workflows in real time.

The framework applies interaction intelligence to strengthen AI-supported operations. It routes work according to intent and complexity, allowing high-risk cases to move to TP experts when human judgment is essential.

This model gives leaders greater visibility across an AI-enabled workflow. It supports consistent execution and keeps governance connected to operational performance, helping organizations move from isolated pilots to durable business impact.

Schedule a conversation with TP to explore how AI agents and human expertise can improve a priority enterprise workflow.

Frequently asked questions

Sources:

6. McKinsey & Company, The State of AI in 2025: Agents, Innovation, and Transformation: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-2025