Why AI agents are fundamentally transforming marketing

Generative AI has noticeably transformed marketing over the last two years: faster content creation, personalised campaigns, smarter chatbots. Our white paper “GenAI in the Customer Journey – now is the time” has analysed these developments and provided an outlook: The next step moves away from AI as a tool towards AI as an independent actor – so-called AI agents.

This step is no longer a distant dream. It is happening right now.

Most companies have already experienced the first wave – ChatGPT, Copilot, AI-generated text and images. Now the second wave is beginning: AI agents that no longer just respond, but act independently.

The difference? A chatbot answers customer enquiries. An AI agent handles the entire ticketing process: it identifies the issue, prioritises it by urgency, forwards it to the right department, formulates an initial response and proactively escalates if a customer threatens to leave. And it does so round the clock, across all channels.

Just how real this already is is demonstrated by the open-source platform OpenClaw: launched at the end of 2025, it now has hundreds of thousands of users worldwide. NVIDIA CEO Jensen Huang explained at GTC 2026 that every company needs an agent strategy. It is the fastest-growing open-source project in software history.

• Campaigns that optimise themselves. Agents don’t just analyse performance data – they act on it. Instead of weekly reports and manual adjustments, agents independently adjust budgets, channel mix and creatives based on real-time data.
• Customer service without queues. What an intelligent chatbot does today will be taken over tomorrow by an agent who manages complete service processes end-to-end: from initial contact through to finding a solution and proactive follow-up – personalised and context-sensitive.
• Real-time hyper-personalisation. Agents combine purchase history, behavioural data and external signals (weather, time of day, season) to tailor content, offers and messaging individually and in real time – without a marketing team having to manually maintain segments.
• Content production on an assembly line. Newsletters, product descriptions, social media posts, landing pages – agents not only produce individual texts but orchestrate entire content workflows: from research and creation to channel-specific delivery.

For all the enthusiasm – and it is justified – marketing managers must not ignore the risks:

• Brand safety. If an agent communicates independently, it must be ensured that tone, brand values and corporate wording are adhered to. In the worst-case scenario, a poorly configured agent can produce or send content that is harmful to the brand – without a human having reviewed it beforehand.
• Data protection and compliance. Agents process customer data on a large scale. The GDPR sets strict limits here. Where is data stored? Who has access? Is customer data used for AI training? These are not technical details – they are strategic decisions.
• Shadow AI. When marketing teams deploy AI agents independently, without involving IT security or legal departments, uncontrollable risks arise. Security researchers are already warning about unsecured agent installations that can become a gateway for attackers.

The most strategically important question for 2026 is not “Which AI tool do we use?”, but: Do we own our AI infrastructure – or are we merely renting it?

Those who rely entirely on external platforms relinquish control over customer data, process logic and, ultimately, customer access. The better approach: building your own platform ecosystems in which various AI models can be flexibly integrated – with clear governance, internal security standards and full data sovereignty.

That sounds like a lot of work. But it doesn’t have to be. What matters is a structured approach in clearly defined phases – exactly as described in our white paper:

1. Identification: Which marketing and CX processes are suitable for agents?

2. Proof of Concept: A controlled test with clear KPIs – not gut feeling.

3. Integration: Embedding into existing systems (CRM, marketing automation, analytics).

4. Scaling: Expansion to further use cases and teams.