It’s safe to say that Artificial Intelligence (AI) technologies have revolutionized how businesses operate, offering capabilities ranging from predictive analytics to autonomous decision-making. But understanding the various flavors of AI and the problems they are best suited to solve is essential to delivering the outcomes and ROI you need. That’s why we’ve created this executive cheat sheet on the three major types of AI—machine learning (ML), generative AI (GenAI), and agentic AI—to clarify their unique strengths, data requirements, and ideal business use cases.
What is ML?
Machine learning is a sub-category of AI that employs algorithms that learn from historical data to generate accurate predictions or classifications. ML models identify patterns and relationships within then data, enabling them to provide predictive insights about unknown or future events.
Key points about ML
Specific: ML models are trained to solve particular problems and can’t simply be switched to different tasks
Static: ML models learn patterns in data, but once they learn, the models won’t adapt without being continually trained.
What data works best with ML?
ML excels with structured data—any information that fits neatly into an Excel spreadsheet, a database, or a numerical vector. Although traditionally challenging, unstructured data like text can now be processed effectively through advancements in GenAI-driven encoding. Typically, ML solutions work best with labeled data—data where you know the answers to the questions.
Risk factor
Choosing the wrong model size or type for a given problem and “overfitting” your training data could give you false confidence that your solution works. That said, most data science professionals know how to identify this issue before a model goes into production.
Common ML business use cases
Forecasting: Predicting revenue, inventory demand, sales, or customer churn.
Recommendations: Personalized product suggestions to enhance customer engagement and product sales.
Customer segmentation: Grouping customers by behaviors or demographics for targeted marketing.
Fraud detection: Identifying anomalous transactions or behaviors to mitigate risks of financial crime, cyber-attacks and other fraudulent acts.
Quality control: Predicting manufacturing defects, process inefficiencies, and maintenance requirements.
What is GenAI?
GenAI, a specialized subset of ML, goes beyond the predictive capabilities of ML to actually generate content – anything from text to images to software code, based on patterns it’s learned from massive datasets. GenAI models act as a function that can turn text, images, or audio into data that the rest of your system can work with based on rules that you give it.
Key points about GenAI
Flexible: GenAI functions like highly adaptable code components, seamlessly integrating into existing workflows.
Advanced: GenAI harnesses vast world knowledge, simplifying complex tasks like data interpretation without explicit rules or extensive preprocessing.
What data works best for GenAI?
GenAI shines brightest with unstructured data, including text, images, audio, PDFs, and multimedia content. While structured data applications exist, GenAI’s distinct advantage is in interpreting and transforming complex, irregular data forms.
Risk factor
While prompt and context engineering has the potential to boost accuracy, GenAI models are inherently probabilistic and may not always give the right answer. You must be careful to build guardrails and other checks to ensure accuracy remains as high as possible.
Common GenAI business use cases
Data extraction: Automatically converting PDFs, documents, or images into structured, searchable databases
Data quality checks: Evaluating and verifying data against quality standards or compliance requirements.
Content generation: Producing reports, articles, marketing copy, or legal documents.
Software decision points: Performing various checks with greater ease and flexibility than traditional coding methods.
Creative assistance: Supporting content creation in design, advertising, and creative industries.
What is agentic AI?
Agentic AI takes generative AI to a whole new level with its unique ability to take action on a user’s behalf. AI agents autonomously interact with data sources and tools to plan, coordinate, and execute tasks in a workflow. They can even learn from past outcomes to adapt and improve their approaches.
Key points about agentic AI
Powerful: Capable of executing complex workflows autonomously, leveraging available tools effectively.
Best paired with human oversight: While agents are capable of autonomous action and decision-making, in most cases, they will still require a human in the loop at some point it their workflow for error check, quality reviews, final approval, etc.
What data and tools work best for agentic AI?
Rather than data alone, agentic AI’s effectiveness relies heavily on the capabilities of the tools and APIs available to it and how well the AI can understand how to use those tools.
Semantic clarity: Clear tool naming and descriptions help agents understand appropriate contexts for use.
Reliable APIs and MCP servers: Access to well-maintained Model Context Protocol (MCP) servers and quality APIs ensures reliable agent functionality. If tools or APIs fail, there is often nothing the agent can do to fix them at run time, potentially causing many downstream issues in your application.
Risk factor
Security: Bad actors can manipulate AI agents to execute unintended and/or harmful actions, For example, injection attacks add malicious instructions into the prompt or the data the model receives, essentially tricking the AI agent to take rogue actions.
Common agentic AI business use cases
Dynamic workflow automation: Managing complex business processes that demand adaptive logic and contextual awareness, such as supply chain management, financial forecasting, or data entry tasks.
Customer support automation: Interactively handling complex customer inquiries that require multi-step problem-solving. For example, an agent could read help desk emails, determine the best way to solve the customer’s problem, ask employees for confirmation, and execute the solution.
Integrated decision-making: Collecting and synthesizing data from multiple sources to support decisions in real-time, such as checking multiple websites and internal resources to find data to support a decision.
Choose your AI approach wisely
Machine learning, generative AI, and agentic AI each have distinct capabilities and optimal use cases. ML provides precise predictive capabilities, GenAI excels in content generation and handling unstructured data, and agentic AI thrives in scenarios requiring autonomous interactions and sophisticated decision-making. Choosing the right AI approach depends on understanding these strengths and aligning them with specific business needs.
