AI agents in 2026, Gartner predicts 40% of projects scrapped by 2027, yesfor

In June 2025, Gartner published a prediction that shook the AI market: 40% of agentic AI projects will be scrapped by the end of 2027. Based on a survey of 3,400 organizations actively investing in the technology.

This is not pessimism. It is math.

What AI agents are

A traditional AI chatbot: answers questions. Receives input, generates output. Static.

An AI agent: performs tasks. Receives a goal, decides on the steps by itself, uses tools (APIs, databases, other systems), iterates, corrects, reports.

A practical example:

Chatbot: "How do I change a delivery address?", "Go to your account, My Orders, Edit"
Agent: "Change the delivery address of order 123 to 100 Marszalkowska Street", the agent logs into the system, finds the order, checks its status, edits the address, sends a confirmation, reports the result.

An agent is active, autonomous, multi-step. That sounds revolutionary.

Why 40% scrapped by 2027

Anushree Verma, senior director analyst at Gartner: "Most agentic AI projects are early-stage experiments or proofs of concept, driven mostly by hype and often misapplied."

Three main reasons for failure:

1. Agent washing

Of the thousands of vendors claiming "agentic AI capabilities" in 2025, Gartner estimates that only around 130 actually have genuine agentic functions. The rest are:

Chatbots rebranded as "agents"
Workflow automation under a new name
RPA (Robotic Process Automation) with a layer of GPT added on top
A marketing buzzword with no technical substance

A company buys an "agent" for PLN 200,000. After 6 months it turns out to be the same chatbot it could have bought a year earlier for PLN 20,000.

2. FOMO from the board

"The competition is deploying AI agents", "We need AI agents too", "We are deploying", "What exactly should the agent do?", "We will see".

This is not a strategy. It is panic.

Gartner: "Companies deploy agents not because they have a strategy, but because they cannot afford to be the last ones."

The result: an agent built on broken workflows. An agent fed bad data. An agent running without governance.

3. No foundation

An AI agent multiplies what a company already does. If the company does it well, the agent does it better and faster. If the company does it badly, the agent does it worse and faster.

Concrete cases:

An e-commerce company deploys an agent to handle complaints. The complaint processes are undefined. The agent escalates everything to the team. The team is flooded with tickets. After 3 months: scrapped.
A B2B company deploys an agent to qualify leads. The definition of a "qualified lead" changed 4 times in the past year. The agent qualifies based on the oldest definition. Sales gets lists of cold leads marked "hot". No accountability after 2 months.
A financial company deploys an agent to generate quotes. There is no quote governance. The agent generates quotes that do not match the company's current pricing policy. A client asks "why a different price than before". After the first internal audit: scrapped.

What works instead

The same Gartner data shows that 60% of agentic AI projects succeed. What sets them apart?

All five success factors:

01, A specific use case, not "agents for everything" One business problem. A clear definition of success. Measurable KPIs.

02, Processes mapped before the agent The agent automates an existing process. It does not invent a new one.

03, Governance before deployment Who approves the agent's decisions? Who responds when the agent is wrong? Who monitors?

04, A small-scale pilot, then scaling Do not deploy an agent across 1,000 processes. Deploy it across 5. Measure. Scale if it works.

05, A vendor with real capabilities Check whether the "agent" is genuinely an agent. The test: can it do multi-step planning? Does it use external tools? Does it iterate based on results?

A practical "agent washing" test

Before you buy an "AI agent" for PLN 200,000, check:

Does it use tools (tool use)? A real agent calls APIs, reads databases, writes to systems. If the "agent" only generates text, it is a chatbot.
Does it carry out multi-step plans? An agent should break a goal into steps, execute them, check the results, and correct course. Not just answer questions.
Does it have observability? Every agent decision must be logged. You should be able to reconstruct "why the agent did X".
Does it have guardrails? What can the agent not do? Without that, it is dangerous.
Does it iterate? After deployment the agent should improve. Without a feedback loop, it degrades.

If the vendor cannot answer "yes" to 4 out of 5, it is not an agent. It is a chatbot with a brand.

What this means for Polish companies

Poland usually runs 12-18 months behind US AI trends. The boom in AI agents in Poland is only starting in 2026.

That is good news. You can watch the mistakes US companies make (which are already documented). And avoid them.

A practical recommendation for Polish companies of 200-5,000 people considering AI agents in 2026:

Q1-Q2 2026: A readiness audit. Are the processes mapped? Is the data in one place? Does governance exist? If not, start by putting your house in order.
Q3 2026: A pilot agent on one specific process. Measure everything.
Q4 2026 / Q1 2027: Scale if the pilot showed ROI. Scrap it if it did not.

Do not: deploy 10 agents at once in 2026. Do not buy an "agent platform" for PLN 500,000 without a pilot.

Closing

AI agents are not a scam. They are a real technology with real capabilities.

But they are also in a hype phase. Most companies deploying agents in 2025-2026 do not yet have a foundation, and those projects will be scrapped.

The decision for a board in 2026: Do we want to be in the 40% that scrap the project, or the 60% that succeed?

The first answer requires budget. The second requires order before investment.

The same question as a year ago around GenAI. The same statistics. The same causes of failure.

Order first. Then AI. And then agents. In that order.