Traditional AI vs. generative AI: A guide for IT leaders

Artificial intelligence is a broad term for technologies that enable computers to simulate human-like reasoning, communication, and decision making. It uses machine learning (ML), natural language processing (NLP), deep learning, neural networks, and large language models (LLMs) to analyze data, identify patterns, and generate insights. The goal of AI is to enhance automation and problem solving across various domains. Within artificial intelligence, several types of AI exist, including agentic AI. However, the two main players in the AI arena are traditional AI and generative AI.

Traditional AI is rule-based or deterministic AI, meaning AI that is trained to complete a preprogrammed task or set of tasks. Also known as narrow AI, traditional AI is designed to solve well-defined problems and automate repetitive tasks by analyzing historical data and identifying patterns to make accurate predictions and decisions.

Traditional AI can be used to automate triage and investigation in the security operations center (SOC), allowing teams to summarize collections of security events, automate complex analytical and management tasks, and rank suggested courses of action. 

For example, Randstad Netherlands uses AI in its SOC to quickly ingest massive volumes of data from dozens of security, cloud, and other applications with only two and a half full-time employees for detections, engineering, and triaging of alerts.

Today, decision trees, neural networks, logistic regression, supervised learning, and reinforcement learning work with data stores and knowledge bases to help traditional AI systems identify patterns, classify data, and make predictions.

Generative AI is a type of artificial intelligence that creates content. It’s trained on vast datasets, from which it learns to recognize data patterns and relationships. This enables it to create new, original content by extrapolating from learned structures and generating outputs that mimic human creativity. In other words, generative AI can create images, video, code, and music and translate languages or answer questions. 

For example, the Elastic Support Assistant is a chat experience powered by generative AI  and designed to answer a wide range of product questions and uses retrieval augmented generation (RAG) to refine search effectiveness.

Several types of generative AI models exist. For instance, applications like Gemini, Grok, and Claude are generative pretrained transformer (GPT) models. Transformer models rely on attention mechanisms to map the relationships between different elements. GANs, or generative adversarial networks, are made up of two neural networks: a generator and a discriminator. They work together to create content through a process of generating and refining.  

As a result, generative AI represents a more advanced form of artificial intelligence that goes beyond traditional AI's capabilities and supercharges innovation potential.

Traditional AI and generative AI differ in key ways, particularly in their capabilities, their applications, and the ways in which they learn. Let’s compare. 

Traditional AI models are more rigid, whereas generative AI models are adaptable to new problems. This is the case because traditional AI models need explicit rules to function and manual intervention in new scenarios. Generative AI models, on the other hand, are trained on vast amounts of data from which they learn patterns and relationships. Where traditional AI models learn by repetition, generative AI models learn to learn. As a result, traditional AI and generative AI differ significantly in their capabilities:

• Traditional AI: Analyzes data, makes predictions, and automates rule-based tasks

• Generative AI: Generates original content and adapts to dynamic inputs

As the capabilities of traditional and generative AI differ, so do the contexts in which they are most useful. 

• Traditional AI: Ideal for intricate analytics tasks, such as fraud detection, recommendation systems, and process automation

• Generative AI: Goes beyond the “analyze and predict” capabilities of traditional AI to create content, such as text, video, sound, code, and images; synthesize data like summarizing documents; and serve as an assistant to security analysts and SREs

Traditional AI use cases

Anytime you say “Hey Siri,” you’re calling on traditional AI to help you find the answer to a question or perform a predefined task like setting an alarm. Other use cases include:

• Fraud detection: Traditional AI algorithms are used to analyze banking and ecommerce transaction data to identify when a transaction falls outside a pattern, possibly indicating fraudulent activities. 

• Predictive analytics: In healthcare, finance, and marketing, AI models analyze historical data to predict future trends, helping manage disease outbreaks, guide economic decision making, and shape campaigns.

Generative AI use cases

Because generative AI represents a much more intuitive way to communicate and access data, it is quickly being embraced by businesses worldwide, looking to improve their productivity, efficiency, and working conditions.   

• Content generation: Generative AI is revolutionizing content generation with its ability to analyze huge datasets and derive new content from inputs. For example, ElasticGPT helps Elastic employees quickly find relevant information and boost workforce productivity.

• Personalized recommendations: Generative AI enhances user experiences by generating personalized content and suggestions in streaming services and ecommerce platforms.

• AI-powered design: Generative AI assists designers by creating new product concepts, digital art, and marketing materials.

• Customer service bots: AI-powered chatbots provide automated customer support, resolving common queries quickly and efficiently while helping organizations improve their customer service. Generative AI-enabled chatbots can help resolve issues quickly by speaking to customers in natural language and using personalized question answering.

Traditional and generative AI also require individual implementation approaches that depend on different data, expertise, and infrastructure requirements. 

• Traditional AI: Relies on structured data, predefined algorithms, and rule-based logic. It is easier to implement in specific business processes.

• Generative AI: Demands large datasets, extensive computational power, and sophisticated deep learning models, making implementation more complex and resource-intensive.

The age of AI is here. But what does the future of AI look like? Certainly, generative AI adoption is on the rise in organizations globally with 93% of C-suite executives already invested or planning to invest in GenAI — the appetite for AI assistants and the speed and efficiency they promise is high. But the move to embrace the technology is sounding some alarms around the world as AI presents a number of both ethical and security-related challenges, and regulators are struggling to keep pace. In the US, the move to deregulate favors innovation. But in the EU, the AI Act looks to guarantee safety, fundamental rights, and human-centric AI. 

Concerns over AI bias have sometimes slowed enterprise adoption. After all, AI is only as good as the data it is trained on, and data often contains inherent biases that are ultimately reinforced through AI. The reliability of AI is also in question; generative AI can hallucinate. Other concerns are environmental. AI requires a lot of computing power, meaning it is a very resource-intensive technology. 

However, in spite of these challenges, AI represents a transformative technological opportunity, especially if it’s developed and used responsibly. The latest wave of AI models refine their ability to self-correct and make independent decisions — this is called agentic AI. 

Search is at the heart of AI, and Elastic is the Search AI Company. With advanced search capabilities, real-time analytics, and machine learning integration, Elastic enables businesses to harness AI's power efficiently. See how we did it with customer support, employee efficiency, and security operations. 

Deep dive into Elastic generative AI tools and capabilities.