The AI terms you really need to know

Before diving into the hype, it helps to understand the foundations.

• Artificial Intelligence (AI): At its core, AI means computers performing tasks that normally require human intelligence, like recognizing patterns, learning from data, or making decisions.
• Machine Learning (ML): ML is the engine that powers most AI. Instead of following explicit rules, the system identifies patterns in data and improves its performance over time.
• Algorithms: Think of algorithms as the recipes behind AI: step-by-step instructions or rules designed to perform a specific task or solve a problem, often forming the foundation of software and AI systems.
• Neural Networks & Deep Learning: Inspired by the human brain, neural networks are layers of nodes that process data and identify complex patterns. Deep learning takes this further, enabling advanced capabilities like speech recognition or image analysis.
• AI models: An AI model is the trained system that applies what it has learned to new data. For example, a forecasting model can predict demand based on historical sales.

Other important building blocks:

• Classification & Clustering: grouping data into categories or patterns.
• Computer Vision: a form of AI that teaches machines to ‘see’ by interpreting images and video.
• Natural Language Processing (NLP): enabling AI to understand and generate human language.
• Python: the most widely used programming language for data analysis, automation, and AI development.
• Inference: the moment when a trained model applies its knowledge to new data to make a real-world prediction.

Why this matters for your business?

These fundamentals explain how AI can automate processes, speed up decisions, and uncover insights, whether it’s predicting equipment failures, analyzing customer feedback, or optimizing supply chains.

2023 was the year when Generative AI went mainstream, thanks to tools like ChatGPT. But what’s really behind the buzz?

• Generative AI: This is AI that doesn’t just analyze data, but creates something new: text, images, audio, even software code.
• ChatGPT: The most famous example, capable of drafting emails, summarizing reports, or answering questions in natural language.
• Large Language Models (LLMs): The technology behind AI tools like ChatGPT: advanced AI trained on massive amounts of text, enabling it to generate human-like responses.
• GPT (Generative Pre-trained Transformer): A specific type of LLM designed to understand and produce natural language at scale.
  

Other useful concepts:

• Prompt Engineering: the art of asking and refining the right questions to get better AI outputs.
• Hallucination: when AI generates content that looks plausible but is factually incorrect.

Why this matters for your business:

Generative AI is powerful for drafting content, brainstorming ideas, or automating routine writing. But human oversight remains crucial to avoid errors and build trust.

For many organizations, the most tangible AI experience today comes from Copilot, Microsoft’s family of AI assistants.

• Copilot: An embedded AI assistant that helps you complete tasks directly in the tools you already use.
• Microsoft 365 Copilot: Available in Outlook, Word, Excel, and Teams, used for drafting an email, summarizing a meeting, generating a presentation outline, and more.
• Dynamics 365 Copilot: Built into CRM and ERP processes, assisting sales reps, service agents, and finance teams with tasks like follow-up emails or invoice checks.
• Copilot Studio: A low-code environment where you can build your own copilots tailored to internal needs, for example an HR chatbot or a service desk assistant.
• Low Code AI: The broader trend of building AI-powered solutions without deep programming skills, enabling business users to innovate faster.
• Power Platform AI: a set of AI capabilities integrated into Microsoft Power Platform, enabling users to build low-code apps, workflows, chatbots, and more.

Why this matters for your business:

Copilot shows how AI moves from theory to daily practice. Instead of abstract models, employees experience concrete productivity gains in the tools they already know.

Once the basics are clear and the first copilots are in place, organizations face the bigger challenge: scaling AI responsibly.

• AI Adoption: The structured rollout of AI across an organization’s processes, products, and services, from pilot projects to enterprise-wide use.
• AI Agents: In today’s generative AI context, AI Agents are autonomous systems that can plan and execute multi-step tasks by combining reasoning, tools, and data access.
• AI Strategy: A plan that aligns AI initiatives with business goals, ensuring investments deliver real value.
• AI Governance: The framework of policies, processes, and controls that keep AI compliant, transparent, and trustworthy.
• Responsible AI: The ethical side of AI: ensuring fairness, accountability, and respect for privacy.

Other crucial terms here:

• Azure AI Foundry: Microsoft’s secure and scalable platform to develop and manage AI applications.
• Human in the Loop: keeping human oversight in critical decisions made by AI.
• Bias in AI: the risk of unfair outcomes caused by flawed data or models.
• MLOps: practices to monitor and maintain AI models in production environments, covering deployment, versioning and retraining pipelines.

Why this matters for your business:

Successful AI is not just technology. It’s a mix of tools, governance, and people. Companies that get this right will unlock efficiency, innovation, and competitive advantage, and keep risks under control.