In our latest article, I shared a diagnosis: Poland is standing at a technological crossroads. But to move from passive observation to active AI implementation with tangible outcomes, leadership teams need to overcome the first — and most critical — barrier: the language barrier.

In the world of Artificial Intelligence, imprecise terminology isn’t just a communication issue — it’s a strategic risk. Below is an excerpt from our “AI Radar Trendbook”, designed to help you navigate the dense jungle of AI terminology.

Today, the acronym “AI” has become a bit of a master key – used to describe everything from simple automation to breakthrough autonomous systems. But for any leader serious about building real value, vague labels won’t cut it. How can you have a meaningful conversation with IT about security if you can’t tell Data Poisoning from hallucinations? How do you plan logistics without understanding the potential of Swarm Intelligence?

In the AI Radar Trendbook, we created a unique chapter: a Dictionary of AI Technologies and Concepts, featuring 73 terms across the broad AI landscape. I selected five terms that show just how complex – and diverse – the world of AI really is.

Swarm Intelligence – strength in numbers

Imagine an ant colony. Each ant behaves – carrying food, following pheromone trails, avoiding obstacles. But together, this seemingly basic community solves complex problems: it builds structures, finds the shortest paths to food sources, and responds to threats. That’s the essence of swarm intelligence – an approach where many simple agents (e.g., drones, robots, vehicles) cooperate locally without central control, producing globally optimised outcomes.

No science fiction here – it’s happening now. In Amazon warehouses, for instance, and during rescue missions where fleets of drones autonomously search disaster zones. Swarm Intelligence underpins a new operational paradigm: distributed, flexible, self-organising intelligence.

Synthetic Data – data that doesn’t exist, yet still teaches.

Data is the fuel of AI development – but not all data is easy to obtain. Sometimes it’s sensitive, regulated, complex to access, or simply too expensive. That’s where synthetic data generation comes in: a technique that creates artificial datasets that are almost indistinguishable from real ones.

Autonomous vehicles can train on millions of virtual driving scenarios before they ever hit the road. A medical start-up can develop diagnostic models without touching real patient data. It’s not just a workaround – it’s a new way to accelerate innovation while maintaining privacy and regulatory compliance.

Data Poisoning – sabotage for a new era

Now imagine someone contaminating the data your AI learns from. Subtly, step by step, they inject errors that distort the model’s behaviour. That is data poisoning – an increasingly common type of attack where malicious actors manipulate training data to reduce model accuracy, steer it towards false conclusions, or create chaos.

Example? An anti-spam filter starts accepting unwanted messages because it was “taught” that spam looks different. For businesses, this means you need models that are not only efficient, but also resilient – with built-in vigilance against data manipulation.

Edge AI – intelligence closer to reality

Until recently, AI mostly lived in the cloud: devices collected data, sent it to servers for processing, and then received a response. But the world has moved on – we need instant responses, and data is often too sensitive to be shipped off anywhere.

That’s where Edge AI comes in: bringing AI directly to the “edge of the network” – into cameras, smartphones, drones, or sensors. Devices can make decisions themselves, in fractions of a second, without needing an internet connection.

Example? A warehouse camera that detects a safety risk and reacts automatically. Direct. Private. Lightning-fast.

Emergent Behaviour – AI that surprises us

Sometimes an AI system starts doing something… that nobody explicitly taught it to do. A chatbot suddenly develops a sense of humour. A language model solves a logic task it has never seen before. These are emergent behaviours – new, unexpected capabilities that appear when a system reaches a certain level of complexity and scale.

For business leaders, it’s a signal that AI is no longer just a tool – it’s becoming a partner in discovery, adaptation, and sometimes… creative surprise. The challenge? Managing something you can’t fully predict. The upside? Models that can do far more than we thought possible.

Why is it worth knowing the complete list of 73 terms?

These five concepts are only the tip of the iceberg. In the AI Radar Trendbook, we’ve compiled 73 key terms that will help you:

• Understand the AI horizon and use its terminology with confidence.
• Hold a peer-level dialogue with technology providers and R&D teams.
• Spot new market opportunities where others see only “noise”.
• Reduce the security and ethical risks of AI implementation.
• Get inspired by a term and develop a fresh AI use case inside your company.

Don’t let a lack of terminology slow your organisation down. Building strategic advantage starts with speaking the same language.

👉 Download the full AI Radar Trendbook and get access to the complete dictionary plus market readiness analysis: