What is agentic AI and how it differs from ChatGPT

Common generative tools like ChatGPT or Gemini work reactively — you write a prompt, you get an answer. Agentic AI goes a step further. These are systems that can independently plan, make decisions, use tools, and perform multi-step tasks without constant human guidance.

Imagine the difference between a calculator and an accountant. A calculator performs the calculation you give it. But an accountant knows on their own what needs to be calculated, where to get the data, how to interpret it, and what to do with the result next. Agentic AI is closer to that accountant.

Deloitte, in its report Turn AI adoption into AI advantage (April 2026), describes agentic AI as a technology that requires a completely new approach to organizing work. It is no longer just about "deploying AI," but about rebuilding business processes so that autonomous agents can function as full-fledged digital coworkers.

The growing gap between investment and results

Deloitte's main warning is clear: the gap between what companies invest in AI and the value they actually get from it is widening. According to report authors Sam Aling and Mark Gustafson, organizations often buy AI tools but don't adjust their workflows, team structures, or decision-making processes.

The result is "pilot purgatory" — companies have dozens of AI experiments, but none of them turn into measurable business benefits. Deloitte therefore emphasizes that success doesn't depend on how many AI tools a company buys, but how radically it changes its organizational DNA.

Specifically, the report identifies several key barriers:

• Data fragmentation — agents need access to connected, high-quality data across systems
• Missing governance — without clear rules for agent decision-making, security and regulatory risks arise
• Organizational resistance — introducing autonomous agents faces entrenched hierarchies and fear of job losses
• Insufficient competencies — companies often lack people who understand both business and AI

Agentic AI in practice: finance, insurance, and manufacturing

In its analysis, Deloitte shows concrete examples of agentic AI deployment. It pays the most attention to wealth management and financial services. In a separate material Ambient Agents in Financial Services, it describes nine models of so-called ambient agents — digital assistants that work in the background of corporate systems and require human input only in exceptional situations, such as final approval or anomaly resolution.

Examples from the financial sector:

• Portfolio management — agents continuously analyze market data, identify risks, and propose adjustments to investment strategies. The human advisor only confirms the final decisions.
• Regulatory reporting — agents automatically collect data from various systems, check it against current regulations, and compile mandatory reports.
• Client onboarding — agents perform KYC (Know Your Customer) checks, verify documents, and assess risk factors without involving the back-office team.

Beyond finance, Deloitte also sees potential in insurance, where according to related analysis agentic AI can bring up to a 90% increase in productivity when modernizing core systems, or in industrial manufacturing, where autonomous agents optimize supply chains in real time.

Google Cloud bets on the agentic era

Part of the strategy that Deloitte and Google Cloud are promoting includes massive infrastructure investment. In April 2026, Google Cloud announced a $750 million investment in developing agentic AI for its partners. At the same time, it introduced the eighth generation of TPU chips designed specifically for the requirements of agentic systems that need to process long sequences of operations in real time.

Deloitte is also expanding its alliance with Google Cloud and ServiceNow to help clients truly scale agentic platforms. Together they are building a dedicated transformation practice that helps companies not only with technical implementation, but primarily with organizational change.

What this means for Czech and European companies

For the Czech market, the rise of agentic AI brings several specifics. On one hand, Czech companies rank among above-average AI tool users in the EU — according to European Commission data from 2025, approximately 12% of Czech businesses use AI, which is slightly above the European average. On the other hand, this mostly involves basic generative tools, not complex agentic systems.

A key role will be played by the EU AI Act, which introduces full effectiveness for high-risk AI systems from August 2026. Agentic AI in finance or insurance will fall at least into the "limited risk" category, which means a transparency obligation — the customer must know they are communicating with an AI agent, not a human.

For companies in the Czech Republic, a clear recommendation follows: don't wait for agentic AI to arrive as a finished product, and start building internal competencies now. Deloitte emphasizes in its analysis that organizations that begin the transformation earlier will gain a head start that competitors will find hard to catch up with — because it's not primarily about technology, but about a completely new way of running a company.

From pilot projects to real value: three steps according to Deloitte

The report formulates concrete recommendations for business leaders who want to truly leverage agentic AI:

1. Redesign work processes — don't automate old processes. Redesign them with agents as their full-fledged component, not just an add-on.
2. Invest in data infrastructure — without high-quality, accessible, and connected data, even the smartest agents remain powerless.
3. Change corporate culture — the transition from human teams to hybrid teams (human + agent) requires new skills, new performance metrics, and above all trust in autonomous systems.

Source: Deloitte & Google Cloud — Turn AI adoption into AI advantage, April 20, 2026