AI Agents, Explained: What They Are and Why Businesses Can’t Afford to Ignore Them

Companies have been experimenting with enterprise AI solutions for the past few years, primarily to predict outcomes, automate repetitive tasks, or generate content. But now, something more potent is taking shape: agentic AI.

AI agents, in contrast to traditional AI, do not wait for human input. They act on their own, making decisions, completing tasks, and coordinating processes based on the goals you provide.

According to Capgemini, this shift could unlock $450 billion in economic value by streamlining operations, accelerating workflows, and cutting costs. Yet many leaders still feel overwhelmed or unclear about what this means for their business.

This article is here to help you.

What Exactly Is an AI Agent?

Let's break it down:

An AI agent is a software solution that is capable of thinking, making decisions, and taking actions independently, requiring minimal human intervention.

Consider a virtual assistant that not just suggests a supplier, but is able to contact the supplier, compare prices, create a contract, and notify the legal department. That is an AI agent.

AI Agent use cases in business operations:

• Procurement: Automating supplier selection and purchase orders.
• Finance: Managing invoices, expense approvals, and even fraud detection.
• Customer Service: Resolving simple tickets and capable of routing complex ones.
• HR: Resume screening, interview scheduling, and candidate engagement prompts.

Unlike standard automations based on static business rules, AI agents are capable of advanced automation, system integration, adaptation, learning, and they can react to changes in the live business environment.

Why Leaders Should Pay Attention Now

AI agents are moving from labs to boardrooms. As per a McKinsey Report published in 2025, while 80% of companies have piloted GenAI, the majority are not realizing any profit because companies are overly focusing on peripheral applications.

The difference with AI agents? They’re embedded into the core of how your business runs.

Let’s look at the data:

• Only 2% of companies have fully scaled AI agents today—but those that have are seeing 5× more ROI than early-stage users (Capgemini, McKinsey).
• Capgemini estimates that agentic AI could generate up to $450B in economic value globally—mainly by reducing costs and increasing productivity in workflows that still depend heavily on human coordination.
• Enterprises that scale AI agents across departments see results like: 25–40% cost reduction in financial operations (McKinsey) 30% faster support response times (Bain) Millions in efficiency gains by removing workflow bottlenecks (Capgemini)

To sum up: AI agents are not a novelty—they bring along unprecedented operational leverage.

What's Holding Organizations Back?

Despite the upside, most leaders are cautious. And for good reason.

Our recent LinkedIn poll results echo the same concerns surfaced in top industry reports:

1. Trust and Control

“Can I really let AI act without human oversight?”

You don’t have to. The smartest AI setups come with built-in safeguards—clear rules, escalation paths, and human oversight when it matters. Think of AI agents like a new teammate: they can take initiative, but they still report back and follow the playbook.

2. Lack of Strategy

“Where should I even start?”

Many companies try AI in siloed projects—without a clear business goal. That’s why ROI stalls. McKinsey recommends picking 2–3 high-value, repeatable workflows where AI agents can make an immediate difference.

3. Unprepared Infrastructure

“Our systems weren’t built for this.”

True—but that’s fixable. You don’t need a tech overhaul. Partnering with teams like Applaudo helps you integrate agents into what you already use (like Salesforce, Microsoft 365, or internal dashboards).

What Leaders Can Do Now

We have put out a simple 4-step framework to help you move forward—with confidence, not confusion.

• Start Small, But High-Impact

Pick a business area where:

• The process is well-defined
• There's volume (repetition)
• There’s pain (manual or slow)

Example: automating expense approvals in finance or triaging support tickets in CX.

• Embed Agents Into Real Workflows

Avoid “bolt-on bots.” Instead, look for integration. Agents work best when embedded into existing platforms and data flows—not just as isolated experiments.

• Build in Human Oversight

Define when the agent should act vs. when it should escalate. This maintains trust and avoids risk.

• Track ROI From Day One

Use metrics that matter:

1. Time saved per transaction
2. Cost per ticket
3. Cycle time reduction
4. Net Promoter Score (NPS) improvement
5. Employee bandwidth unlocked

How Applaudo Uses AI Agents

At Applaudo, we’ve been helping companies move beyond experimentation—and into execution—by embedding AI agents directly into their workflows. Our approach is to help companies move from AI curiosity to real, operational change by building AI agents that plug into the way they already work.

Here’s how we do it:

Automating End-to-End Processes

For instance, in back-office operations, we’ve implemented AI agents that:

• Monitor incoming documents,
• Classify and extract key data using NLP,
• Trigger workflows in real time (like updating records or notifying stakeholders),
• And continuously learn from each interaction.

This isn’t RPA 2.0—these agents adapt, escalate when needed, and integrate with tools like CRMs, ticketing platforms, and document systems.

Smarter Customer Support

In customer service, we deploy agents that:

• Read and understand support tickets,
• Solve repeatable requests autonomously,
• Escalate edge cases to humans with contextual recommendations,
• And learn from resolution outcomes to improve over time.

These agents reduce load on human teams while improving time-to-resolution and NPS scores.

AI Agents for Decision Intelligence

We’ve also built AI agents for internal teams—like finance and procurement—that:

• Analyze large volumes of data,
• Surface anomalies or opportunities,
• Recommend decisions,
• And even initiate next steps like creating reports or contacting vendors.

What makes these agents different is that they’re not just assistants—they act, based on business rules, context, and data confidence thresholds.

Lead with Vision, Not Hype

Agentic AI isn’t science fiction. It’s a business reality.

Like any transformation, it requires more than tools—it demands vision, alignment, and execution. But those who move first will build a serious competitive edge. Not because they adopted “AI,” but because they reimagined how their business works.

As McKinsey puts it: “AI agents aren't just an efficiency play—they're a new model for enterprise performance.”

The question is: Are you ready to lead it?

Let’s Continue the Conversation

If you’re curious about how agentic AI could reshape a workflow in your company, we’d love to hear from you.

Explore our expertise and follow Applaudo for more insights on building meaningful, scalable tech.

We partner with you to move from idea to implementation—step by step.