AI Agents For Business: The Real Value

AI agents are real, and they are starting to split businesses into two camps. A small group getting serious value. A large group getting almost none.

The difference is not the tools they buy. It is how they build and run them. Here is the data, and what the leaders do differently.

The value is real, but uneven

BCG put numbers on this in The Widening AI Value Gap. Two findings stand out.

First, agents are growing fast as a share of AI value. About 17% in 2025, set to nearly double to 29% by 2028. Agents are becoming the engine, not a side feature.

Second, and this is the sharp bit, only about 5% of companies get substantial value from AI, while around 60% lag. The value is real. Most are not capturing it.

So the headline is not "AI works" or "AI is hype." It is "AI works for the few who build it right." For the deeper numbers on this, see what the 2026 agentic AI ROI data actually shows.

What it looks like when it works

The clearest public example is Klarna. Their own press release lays out the numbers.

In its first month, Klarna's AI assistant:

• Handled 2.3 million conversations, two-thirds of all customer service chats.
• Did the work equal to 700 full-time agents.
• Cut resolution time from 11 minutes to under 2.
• Drove about $40 million USD in profit improvement for 2024.

One honest caveat, because accuracy matters. The "700 agents" is hiring Klarna avoided, not staff it sacked. And by 2025 the company said it had leaned too far on AI for support and reopened some human hiring. The lesson is not "replace everyone." It is "agents handle volume, humans handle judgement."

Where to point agents in your business

The wins are not flashy. They are the repetitive, high-volume jobs that quietly eat your week:

• Customer support and the same questions, answered well every time.
• Sales follow-up that never lets a lead go cold.
• Content and repurposing from one brief.
• Research and reporting, built once and run again.
• Onboarding and the back-office admin nobody enjoys.

Start where the hours are, not where the buzz is.

Why most businesses miss it

If the value is real, why do 60% lag? Because they buy a tool and stop there.

A generic tool gives generic output. The leaders go further. They give the agent their method, their data, and a memory so it acts on what the business knows. They add a check so nothing wrong slips out, and a human on the trigger for anything that ships.

That is the difference between "we tried AI" and "AI runs part of our business." It is not a tool. It is a system, run properly. We call it orchestration. Our build studio DevWiz covers the practical build side in AI agents for business: what they are and how to build them.

How to start without betting the business

You do not go all-in. You prove it on one job.

Pick your most repetitive task. Give an agent a clear goal, your method, and the tools it needs. Keep a human approving the output. Measure the hours saved. Once it pays, add the next job.

For the full picture of running AI this way, see how we run AI as an operating system, or read Agentic AI for business for where it pays and how to begin.