AI Agents for Businesses: Use Cases and Benefits

For businesses, an AI agent is a software system that carries out a task largely on its own: it plans the necessary steps, makes decisions, and uses connected tools, APIs, and data to do so. Unlike a chatbot, it does not follow a rigid script but works through multiple stages and adapts its approach to the situation.

This article explains what agentic AI sets apart from chatbots and RPA, where businesses use AI agents today, what benefits and risks they carry, and the steps for introducing AI automation.

What Is an AI Agent (Agentic AI), and What Sets It Apart From Chatbots and RPA?

According to IBM Think (accessed June 2026), an AI agent is a system that autonomously carries out tasks for a user or another system by designing its own workflow and using the tools available to it. The term agentic AI emphasizes autonomy: perceiving, reasoning, acting.

A classic chatbot, by contrast, follows scripts and simulates conversation for simple, repetitive tasks (IBM Think, accessed June 2026). An AI agent plans multi-step tasks, makes decisions, and adapts autonomously.

The distinction from RPA (Robotic Process Automation) matters too. RPA uses software bots to work through repetitive, structured tasks according to fixed, predefined rules (TechTarget, 2025). AI agents, on the other hand, learn from data, make judgments, and call tools without being explicitly programmed for every individual case.

Criterion	Chatbot	RPA	AI Agent
Control	Script / predefined dialogs	Fixed rules, structured tasks	Autonomous planning and decision-making
Adaptability	Low	Low (bound to fixed rules)	Adapts its own workflow
Tool use	Limited	UI/software automation	Tool/API calls (tool calling)
Typical use	Simple, repetitive responses	High-volume, repetitive processes	Multi-step tasks with decisions

The boundaries are fluid: many solutions combine RPA building blocks with agentic logic.

How Does an AI Agent Work? Perceiving, Planning, Using Tools, Acting

An AI agent typically runs through a cycle of perceiving, reasoning, acting, and remembering. IBM Think (accessed June 2026) describes three core components:

Tool Calling: calling external tools, APIs, or functions to carry out actions in real systems.
Memory: storing past interactions to retain context across multiple steps.
Agentic Reasoning: the decision-making that determines which step makes sense next.

These building blocks explain why an agent can independently break down a task like "check the outstanding invoice, pull customer data from the CRM, and draft a reply" into individual steps. Our AI development and automation page shows how we build such agents technically and integrate them into your systems.

Where Do Businesses Actually Use AI Agents Today?

Early use cases for AI agents concentrate on a small number of functions. Based on McKinsey (State of AI, 2025), market observation points mainly to these areas:

Customer service and support: understanding requests, looking up data, drafting responses.
IT and knowledge management: searching internal knowledge bases, pre-qualifying tickets.
Marketing and sales: research, drafts, lead preparation.
Back-office automation: recurring administrative and document processes.

Important context: agentic use is mostly limited to one or two functions. According to McKinsey (2025), no single function has more than around 10% of respondents with an agentic system at scaling stage. So AI agents are genuinely in use, but rarely widespread.

What Benefits Do AI Agents Bring, and How Widespread Are They Really?

AI agents promise above all to take over multi-step routine work and prepare decisions. Adoption is growing fast but is still early. As of June 2026, the studies paint this picture:

Metric	Value	Source (year)
Organizations using AI in at least one function	88% (previous year 78%)	McKinsey (2025)
Organizations scaling an agentic AI system	23%	McKinsey (2025)
Organizations at least experimenting with AI agents (incl. scalers)	62% (of which 23% at scaling stage, plus 39% experimenting)	McKinsey (2025)
German companies engaging with AI	57% (20% use AI, 9% generative AI)	Bitkom (2024)

According to McKinsey (State of AI, published Nov. 2025, n = 1,993 from 105 countries), 88% of organizations use AI in at least one business function. 62% engage with AI agents in some form, of which 23% are already scaling an agentic system, and a further 39% are experimenting with them. The 62% therefore includes the 23% of scalers and is not a separate cohort.

In Germany, more than half of companies engaged with AI for the first time in 2024: 57% according to Bitkom (October 16, 2024, based on 602 companies with 20 or more employees); 20% were already using AI, 9% generative AI.

Gartner expects a sharp rise: by 2026, 40% of enterprise apps are expected to include task-specific AI agents, up from under 5% in 2025 (Gartner, August 26, 2025). By 2028, 33% of enterprise software is expected to include agentic AI (up from under 1% in 2024), and at least 15% of everyday work decisions are expected to be made autonomously (up from 0% in 2024), according to Gartner (2025).

What Risks and Limitations Do AI Agents Have in Enterprise Use?

AI agents fail in practice more often than the hype suggests. Gartner predicts that by the end of 2027, more than 40% of agentic AI projects will be canceled, due to rising costs, unclear business value, or insufficient risk controls (Gartner, June 25, 2025).

This results in clear limits for deployment:

Unclear business case: without measurable value, the investment fizzles out.
Cost control: tool calls and model usage can scale expensively.
Risk governance: autonomous actions need guardrails, approvals, and logging.
Data quality and access: an agent is only as good as the tools and data it can access.

The consequence: AI agents belong where their value is clearly measurable and human control (human-in-the-loop) remains guaranteed.

What Does the EU AI Act Require for AI Literacy (Article 4)?

The EU AI Act requires AI literacy, not a certificate. Article 4 obliges providers and deployers to promote, "to their best efforts," the AI literacy of their staff and of other persons dealing with the operation and use of AI systems on their behalf, taking into account prior knowledge, experience, education, and the context of use (Regulation (EU) 2024/1689, Art. 4). A mandatory certificate is not required there.

According to Art. 3 No. 56, AI literacy means the skills, knowledge, and understanding that allow for an informed deployment of AI, as well as awareness of the opportunities and risks involved.

The obligation under Article 4 has applied since February 2, 2025; oversight and enforcement begin on August 2, 2026. The European Commission stresses that there is no mandatory certificate and "no one-size-fits-all solution" for AI literacy (European Commission, AI literacy FAQ, 2025).

Important context for the fines often cited: penalties of up to EUR 35,000,000 or 7% of global annual turnover apply exclusively to violations of the prohibited practices under Article 5, not to Article 4. Other breaches of obligations carry fines of up to EUR 15 million / 3%, and supplying incorrect information to authorities up to EUR 7.5 million / 1% (Regulation (EU) 2024/1689, Art. 99). Our AI certificate for employees page shows how to build practical AI literacy in your team.

What Steps Should You Follow to Introduce AI Agents and AI Automation?

A pragmatic rollout starts small and measurable, not with the biggest process. This approach has proven effective:

Choose a use case: a process with clear, measurable value (e.g., support pre-qualification).
Clarify data and tools: which systems does the agent need access to, and what data is available?
Pilot with guardrails: start with approvals, logging, and human-in-the-loop.
Measure and decide: check value, cost, and quality against the baseline.
Scale or stop: only scale if the business case holds up; otherwise end it deliberately.
Build AI literacy: train the team so deployment happens in an informed and safe way.

This path addresses exactly the reasons Gartner cites for why many projects fail: unclear value, cost, and missing risk controls. Our team for AI agents and process automation supports you with design, implementation, and integration.

Frequently Asked Questions About AI Agents for Businesses (FAQ)

What Is the Difference Between an AI Agent and a Chatbot?

A chatbot follows scripts and simulates conversation for simple, repetitive tasks. An AI agent plans multi-step tasks, makes decisions, and adapts its workflow autonomously (IBM Think, accessed June 2026).

How Do AI Agents Differ From RPA?

RPA uses software bots to work through repetitive, structured tasks according to fixed rules. AI agents learn from data, make judgments, and call tools without being explicitly programmed for every individual case (TechTarget, 2025).

How Widespread Are AI Agents in Businesses?

According to McKinsey (2025), 62% of organizations engage with AI agents in some form, of which 23% are already scaling an agentic system and a further 39% are experimenting. In Germany, 57% of companies were already engaging with AI in 2024 (Bitkom, 2024).

Why Do Many AI Agent Projects Fail?

Gartner predicts that by the end of 2027, more than 40% of agentic AI projects will be canceled, due to rising costs, unclear business value, or insufficient risk controls (Gartner, 2025).

Does the EU AI Act Require an AI Certificate?

No. Article 4 of Regulation (EU) 2024/1689 requires ensuring sufficient AI literacy but does not mandate a certificate; the European Commission stresses there is "no one-size-fits-all solution" (European Commission, 2025).

Do the EUR 35 Million Fines Apply to the AI Literacy Obligation?

No. Fines of up to EUR 35,000,000 or 7% of annual turnover apply exclusively to prohibited practices under Article 5, not to Article 4 (Regulation (EU) 2024/1689, Art. 99).

Where Should Businesses Start With AI Agents?

With a clearly scoped use case that has measurable value, such as customer service, IT/knowledge management, or back office, run as a pilot with guardrails and human-in-the-loop before scaling broadly (McKinsey, 2025).

Sources

Author: Alexander Weipprecht. As of June 2026. This article is general information, not legal advice; for the specific application of the EU AI Act in your company, please consult qualified counsel.

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AI Agents for Businesses: Use Cases, Benefits, and Implementation