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How Content Production Is Evolving Again with GenAI

From Tedious Research to Instant Insights

The way we research and produce content has undergone multiple transformations due to Generative AI (GenAI) in recent years. vireo2b helps companies navigate this complexity and bring clarity to their content strategy – with innovative, strategic approaches to AI-powered research and production. What was once a time-consuming process involving manual research, source analysis, and content creation has been automated by AI agents. Today, we take it a step further with deep research technologies – and this has a massive impact on content teams and B2B marketers.

The Evolution of AI-Powered Content Production

1. The Era of Manual Research

In the past, writers had to spend hours or even days gathering information from academic articles, whitepapers, or expert interviews. Producing high-quality content required deep expertise but also a significant investment of time.

2. The Rise of AI Agents

With the advancement of Large Language Models (LLMs) and web-scraping technologies, AI agents entered the scene. Companies sought to accelerate the research process by automating web scraping and using LLMs for content summarization. The result? Faster content production – but often with quality issues and potential copyright risks.

3. The Revolution Through Deep Research

The latest developments are changing the game once again. Deep research tools, like those recently introduced by Perplexity.ai, enable the rapid and reliable collection of relevant information from trusted sources – without the need for complex AI agents.

A game-changer: While OpenAI currently limits its deep research feature to Pro users in the U.S., Perplexity.ai makes it accessible to all users. This allows B2B marketing teams to make strategic decisions more quickly and confidently by accessing high-quality, curated information – without the hassle of manual or agent-driven research. This means:

  • Higher content quality since information comes directly from validated sources
  • Less effort for content teams as complex AI agent configurations are no longer required
  • More efficient content production as research and text generation seamlessly integrate

Practical Example: Content Production with Deep Research

An efficient approach using deep research could look like this:

  1. Topic Selection & Deep Research: A marketing team selects a relevant topic, e.g., "The Future of AI in B2B Marketing," and uses Perplexity.ai to gather high-quality, up-to-date information from reliable sources.
  2. Structured Summary: The collected content is summarized to extract key statements and relevant insights.
  3. Content Generation with LLMs: The summary is fed into a custom GPT trained to produce brand-compliant, high-quality texts. Based on the researched information, this model creates an initial draft of the article.
  4. Human Review & Refinement: An experienced editor reviews the text, refines the tone, ensures brand alignment, and adds a strategic perspective.
This approach saves time, ensures top quality, and strengthens the brand’s thought leadership in the market.

What Does This Mean for B2B Marketers?

Faster, More Informed Decisions: Marketing and sales teams can access relevant insights without having to sift through months of studies.

Greater Strategic Clarity: Deep research ensures more precise content that reinforces thought leadership – rather than producing generic texts.

Less Technical Overhead: Companies can eliminate the need for building complex AI agents and focus directly on content creation.

Conclusion: The Future of Content Production Is More Efficient Than Ever

The era of tedious B2B content research is coming to an end – ushering in a new age where deep research technologies not only save time but also enable content teams to establish true thought leadership with strategic precision and data-driven insights. With deep research technologies like those from Perplexity.ai, the standards of quality and speed in content production are being redefined.

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