Inside the AI Products Accelerator: How BurdaForward Innovates Responsibly

This explosive progress is reshaping how we interact with technology, and even how businesses run. At BurdaForward, we see this AI revolution not as a threat, but as a wave of opportunity to serve our users better—faster, smarter, and more personally than ever before. It’s against this backdrop that we’ve launched our AI Products Accelerator team at the end of 2023. This special unit is on a mission to turbocharge product development and innovation using the latest in generative AI. In this article, we’ll introduce the team, explain why it was founded, and share how it works to bring cutting-edge AI into BurdaForward’s products in record time.

Why We Started an AI Products Accelerator Team

Innovation has always been part of BurdaForward’s DNA. But as AI technology started evolving at breakneck speed, we realized we needed a dedicated task force to move fast and experiment boldly. Large, established companies are often structured for stability, not speed. Legacy processes, risk aversion, and cross-team dependencies can slow down experimentation and hinder breakthrough ideas. That’s where the Accelerator model comes in: a small, agile group with the freedom to explore bold ideas quickly, iterate on them, and determine viability without the friction of traditional structures.

We now work with two main experiment cohorts: external users, who help us test and shape new product experiences, and internal teams, who collaborate with us to evolve how we build, scale, and improve our processes. With generative AI, the game has changed for both audiences—reshaping not just what we create, but how we create it. This fundamental shift is exactly why we launched the AI Products Accelerator: to explore what’s possible when experimentation meets responsible innovation on both fronts.

By assembling top internal talent – engineers, data scientists, and product thinkers who are passionate about AI – we combine deep domain knowledge of our brands with strong technical expertise. This team leverages proprietary data loops that only BurdaForward has: decades of editorial excellence, rich content, user behavior insights, and feedback from millions of readers. In practical terms, this means we can build smarter AI-powered products on top of frontier models that truly understand our users’ needs. 

The goal isn’t experimentation for its own sake—it’s about building things real people need, and finding new ways to serve them at scale

How We Build AI-Powered Products

Our team is hands-on with the latest AI technologies, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Text-to-Speech (TTS), and more. With RAG, we can plug our editorial content into LLMs to summarize key information, offering users a faster way to consume news when they have limited time. Importantly, this is always an optional layer – we consistently provide the full, unaltered version of our content alongside AI-generated summaries. You can read more about how we approach scaling real-time RAG solutions in our detailed blog post here. 

We focus on use cases where AI can reduce friction, save users time, or unlock new ways to engage with the content they care about.

TTS lets us offer our content in audio form – a more accessible experience for users on the go. For example, we're experimenting with a product that delivers real-time news bulletins to drivers in their cars, like a dynamic radio stream powered by AI, delivering real-time updates.

In addition, we're piloting the use of AI to generate short-form videos that summarize key news topics for social media platforms. These videos help reach audiences who prefer visual formats and support fast, engaging content discovery.

We operate on an AWS-based architecture, using scalable cloud services for data storage, deployment, and integration. Here we leverage the vast amount of data we have within the company to power intelligent, user-focused experiences.

Rather than relying on assumptions, we evaluate the usefulness and impact of each product through practical experiments and insights from real-world use. This helps us make data-informed decisions and continuously improve our offerings in a way that aligns with what our users actually need.

Every prototype starts with a user problem and ends with user feedback—real needs drive what we build and how we improve it.

AI as a Driver of Product and Process Innovation

AI doesn’t just help us build better tools—it challenges us to rethink the entire scope of what we offer and how we reach our users:

• Product Innovation: AI is a catalyst for re-thinking our products to boost user loyalty. So far, we have mostly operated within the boundaries of text, but AI allows us to think beyond—into visuals, audio, and even interactive experiences. This opens up entirely new ways of serving our users, giving users more ways to interact with our content—whether they prefer reading, listening, or exploring ideas visually—and deepening their connection with our platforms.
• Process Innovation: AI enables us to expand our reach and relevance by identifying emerging areas of interest and surfacing content in ways that better reflect what our users care about. This goes beyond broad topics—it’s about uncovering niche themes, trending ideas, and underrepresented subjects that might otherwise stay hidden. In addition, we're exploring how AI can support more meaningful dialogue by analyzing user comments and reactions. This allows us to surface a variety of perspectives on each article, highlighting the diversity of thought among our readers. By turning user input into structured insights, we can present different sides of a debate and invite deeper engagement, ultimately fostering a richer and more inclusive reading experience. This allows us to meet users where their curiosity lives—whether they’re diving deep into a niche topic or exploring new ideas through different lenses and perspectives. 

Responsible AI

With great power comes great responsibility. As we integrate AI into more of our products, we stay focused on doing what’s right for our readers and users. That means:

• Including a “journalist-in-the-loop“ curation step, where our journalists review and guide AI-assisted output in key areas to ensure quality, relevance, and editorial integrity.
• Avoiding bias through diverse training data and rigorous evaluation methods
• Flagging and mitigating potential misinformation
• Running continuous quality checks to ensure reliability and relevance
• Clearly labeling all AI-generated elements and making them optional for users

Responsible AI is more than a principle—it's a practice deeply woven into our product development approach. Every idea we bring to life is subject to real-world validation and editorial oversight. Because trust is not something we’re willing to compromise—our commitment is to enhance the user experience, never undermine it. 

Above all, we build AI features that respect the user—by giving them control, maintaining transparency, and never compromising on trust.

Our Unique Strategy: Three Paths to Innovation

The AI Products Accelerator has three main engagement models:

• Path 1: Build In-House Prototypes. We identify opportunities and create MVPs from scratch.
• Path 2: Collaborate with Product Teams. We co-create AI features with existing teams across BurdaForward.
• Path 3: Enable and Empower. We share tools, APIs, and best practices to help other teams adopt AI.

Every successful prototype is handed off to the most relevant product team for scaling. This ensures long-term maintenance and allows the Accelerator to refocus on the next big thing. This model keeps us nimble and impact-driven.

Fail Fast, Learn Faster

Working with frontier technologies means we often chart unknown territory. We embrace a "fail fast, learn faster" mindset. Instead of perfecting an idea in isolation, we build light prototypes, get them in front of users, and measure impact. If something doesn’t resonate or deliver results, we move on quickly. If it works, we double down.

This agile loop – Build → Measure → Learn – accelerates both learning and delivery. It allows us to experiment with high-risk, high-reward ideas that might not fit traditional development pipelines.

Thanks to our focus and agility, we’re able to iterate at a speed rarely seen in traditional product development. A typical Accelerator project moves from prototype to tested outcome in weeks, not months - allowing us to act quickly, adapt as we go, and stay ahead of the curve.

With both external and internal testing loops, we accelerate feedback cycles—turning every failed experiment into fuel for the next hypothesis.

Key Takeaways from Our Journey

The AI Products Accelerator at BurdaForward is not just about innovation for innovation’s sake—it’s about delivering real value to users, fast. As we continue our journey, here are a few guiding principles and lessons that shape our approach:

• Centered on user value – From discovery to deployment, our work begins and ends with what’s best for the user.
• Human oversight is essential – Journalistic curation and quality control ensures that every AI-enhanced feature upholds our editorial standards.
• Grounded in product reality – We closely collaborate with other teams from the start to make sure our ideas solve real problems and stay aligned with actual user needs.
• Built on cooperation – We rely heavily on other teams for core components, like data access, editorial input, and platform integration. Collaboration is key to our speed and success.
• Speed is our superpower – We move from idea to prototype in weeks, not months, which helps us stay ahead of the curve.
• Small teams make big waves – By keeping the Accelerator lean and focused, we maintain momentum and flexibility.
• Responsible AI is non-negotiable – From transparency to bias mitigation, we build with care and ethics at the core.

The road ahead is full of potential, and we’re always open to new ideas and collaboration. Whether you’re part of BurdaForward or outside looking in, we’d love to hear from you. Let’s shape the future of AI in media—together.