GoodData.AI Czech Republic

🧑‍💻 Writing the best code used to be the priority. But not so much anymore. AI has changed what good engineering actually looks like.

So, what's taken its place? According to GoodData.AI CTO, Jan Soubusta: language skills, deep product understanding, broad architectural knowledge, the ability to direct AI well, and to recognise good output when it comes back.

For Jan, AI shows up across everything he does — software development, architecture design, maintenance, learning, presentations. If there's a task, AI is part of how he approaches it.

The old constraints are mostly gone. Slow onboarding, boilerplate code, and limited parallelism are all handled. Prototyping, large redesigns, and technical debt removal are now faster. He can run five times more tasks in parallel than before.

The parallelism is real. But so is the exhaustion. The human brain wasn't built for this, and Jan is finding that hitting the right balance takes time.

On the product side, the impact is visible. More features, faster. And room to expand into agentic analytics alongside the BI capabilities GoodData.AI has always delivered.

At GoodData.AI, this is what building with AI actually looks like. Not just faster ex*****on but a fundamentally different way of working. Stay tuned for more behind-the-scenes stories from our team.

Big Data & AI World was two days of sharp conversations, big questions, and a few strong coffees.

We met engineers rethinking how AI fits into their data stack, product teams figuring out governance at scale, and everyone asking some version of the same question: how do we make this actually work?

Good to be in the room for it. See you next time! 👋

GoodData.AI is a direct reflection of what we’ve been building: the analytics foundation serious enterprise AI deployments need

Every decision has been heading here.

Welcome to GoodData.AI: https://hubs.ly/Q04fp-Yv0

GoodData is now GoodData.AI

Our name has changed, and the platform behind it has been building toward this for years — a semantic layer, agentic frameworks, context management, analytics-as-code. Each one a step toward the same foundation: AI-native analytics enterprises can actually trust and govern at scale.

Read the full announcement:

GoodData is Now GoodData.AI, Reflecting Its AI-First Direction The brand update marks the company's continued shift toward intelligence-driven analytics.

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Tomorrow: a practical session on building agentic analytics that actually scales.

Peter Fedorocko and Rosta Striz will cover how to separate MCP tools, skills, and agents, how to design reusable skills that work across both deterministic workflows and dynamic assistants, and how governance fits into the architecture from day one.

April 28 | 9:00 AM PT | 6:00 PM CET

Save your seat: https://hubs.ly/Q04cYCzQ0

Skills, Agents, and Workflows: The Architecture Behind Reliable, Governed Agentic AI In this live session, you'll learn about the architecture needed to build governed, production-ready AI that lasts.

🤿 Want to dive deeper into GoodData? Join us on Slack to hear directly from our Product and Engineering teams about new features and updates. And stay in the loop for all upcoming GoodData events!

👋 See you on Slack! https://hubs.ly/Q04d0JHP0

AI makes ex*****on faster. And it makes good judgment more important.

That's what Matyas Kandl, Senior Principal Software Engineer at GoodData, has found after integrating AI into every part of his workflow.

AI agents now handle search, brainstorming, analysis, planning, and implementation. He runs multiple in parallel across different tasks. His role has shifted toward assigning, reviewing, and monitoring — and getting better at directing them effectively.

The speed is real. Faster iterations, faster feedback loops. Work that would have been deprioritised for taking too long is now worth attempting.

👉 But that's exactly where it gets harder.

When ex*****on is no longer the bottleneck, the decisions about what to build — and what not to build — carry more weight. You can do things you couldn't before. That doesn't mean you should. Decision-making has higher impact than ever, and the engineers who get the most out of AI are the ones who recognise that.

On quality, Matyas is clear-eyed. AI doesn't deliver high quality code by default. You have to push it, iterate, and stay involved. A human working with AI can outperform a human working without it — but it takes real effort and judgment to get there.

At GoodData, our engineers aren't just using AI to move faster. They're using it to make better calls about where to move at all.

Stay tuned for more behind-the-scenes stories from our team.

Proof of concept: done. Production: still waiting. Sound familiar?

We'll be at Big Data & AI World Frankfurt, May 6–7 (Booth E120), and on May 6 at 14:20, our team is presenting in the Keynote Theater on exactly this challenge: how regulated industries move AI from experimentation to production without sacrificing auditability, security, or control.

If you're in financial services, healthcare, insurance, or the public sector, this session was written for you.

Get your free ticket here: https://hubs.ly/Q04d17pS0