We constantly track and evaluate new trends and technologies in the digital industry. In our blog posts, we delve into these topics and provide insights into our agency's everyday life.
For a long time, software projects were mainly developed as monoliths. With the rise of microservices, a paradigm shift began: Large development teams in particular relied on cutting complexity into many independent services, microservice-based architectures were on the rise. But with the advent of AI-driven development, we might be witnessing a turning point. Why modular monoliths are the better foundation for most projects and when microservices still remain the right choice.
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AI recommendations often have an impact beyond the direct click. A Similarweb study shows that brands recommended by AI are 2.5 times more likely to receive a website visit than brands that were not recommended. 55.9 percent of AI-influenced visits come through search engines, while these users also engage much more deeply with the content on the websites they visit. This article shows what these findings mean for the customer journey, SEO, content, and performance measurement.
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For 25 years, every Google search began the same way: a white input field, a blinking cursor, and the silent prompt to condense your question into three keywords. At the Google I/O 2026, the annual developer conference held in May, the company declared this era itself over. Replacing the search box is an expandable input area based on Gemini 3.5 Flash, which accepts text, images, files, videos, and even open Chrome tabs. Even more importantly, users can create so-called Information Agents in the future—AI agents that continuously track a topic in the background and notify themselves when there's something new. So far, only users with a Google AI Ultra plan can enjoy these features. Starting in the summer of 2026, the broader rollout is expected to begin for more users initially in the USA. This makes Agentic Search a product reality from a futuristic scenario at the world's largest search provider. It's time to properly categorize the concept.
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Until a few years ago, I experienced many conversations about the architectures of Martech stacks like this: Best-of-breed architectures stood for agility and the desire to modernize or adapt the stack step by step. DXPs stood for ready-made integrations, unified user interfaces, and shared data, but also came with stronger vendor lock-in and dependency on the product roadmap and priorities of the manufacturer. AI changes this consideration significantly from my perspective. Many discussions today are less about feature comparisons of individual components. Increasingly, the central question is: How do I ensure that fragmented AI functions pursue a common goal across all touchpoints and share insights with each other? My thesis is therefore: Integrated DXPs with a shared AI layer, shared context, and shared knowledge base have an architectural advantage over fragmented best-of-breed stacks.
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