Traditional AI uses predefined rules and algorithms to analyse data and predict outcomes, functioning best when applied to very specific tasks. Generative AI learns from data patterns to create new content, such as text or images, making it better suited to more innovative and creative applications.
Although artificial intelligence may seem like a straightforward concept—designing machines that can simulate human intelligence—the term itself has expanded to include a growing range of technologies. One of the most noteworthy and widely used variations is generative AI (GenAI). Thanks to the rising popularity of generative pre-trained transformer (GPT) tools and backed by advancements in recurrent neural networks (RNNs) and large language models (LLMs), GenAI is redefining how the world uses intelligent systems.
This begs the question: What is it that makes GenAI unique? How is traditional AI different from generative AI? And perhaps most importantly, how can a business apply AI and GenAI to drive efficiency, enhance innovation and gain a clear competitive advantage? The first step is in understanding the similarities and differences associated with these technologies.
AI can mean different things depending on the context. At its most broad, 'artificial intelligence' is an umbrella term that encompasses all the tools, technologies, processes and fields of study that have to do with designing, implementing and supporting machines in performing tasks that have historically required human intelligence. As such, AI can refer to everything from self-driving vehicles and predictive analytics to autonomous processes and computer-science research.
That said, when people discuss traditional AI as a technology, they are generally discussing a subset of AI that relies on clear rules to analyse historical data and make predictions about future outcomes. Also known as 'predictive AI' (or 'weak AI' or 'narrow AI'), traditional AI is highly specialised; it operates within the somewhat restrictive limits of its algorithm and is limited in scope to the specific applications it is designed for.
Despite the pejorative terminology of weak AI, this clearly defined approach delivers several benefits. When employed correctly and applied to the right tasks, traditional AI demonstrates:
- High accuracy in specific tasks
Traditional AI is excellent at performing specific, well-defined tasks with high accuracy. This includes such tasks as data analysis and automation.
- Scalability
These systems can be scaled effectively in stable environments, such as finance and manufacturing, where processes are standardised. Provided the tasks are well-defined, even extremely large volumes of data and complex processes can be incorporated into the AI without any substantial increase in costs.
- Transparency
The rule-based nature of traditional AI makes it easier to understand and validate its decision-making processes. Users can easily interpret the processes that are occurring.
Not every tool is the right one for every job. Traditional AI has its limitations, including:
- Limited flexibility
These systems cannot generate innovative solutions beyond what they were explicitly programmed to do. When faced with something outside not fully addressed in their training, these AIs will almost certainly provide inaccurate, incomplete or irrelevant outputs, highlighting their limitations in handling unforeseen scenarios.
- Ethical issues
Predictive AI is fully dependent on its training data. When that data includes biases, prejudices or inaccuracies, the AI's outputs can reflect and perpetuate these issues, leading to unfair or erroneous predictions and decisions.
Generative AI represents a significant shift from traditional AI. While still governed by rules and algorithms, these rules make it possible for the GenAI tool to apply deep understanding to unfamiliar problems. This allows it to focus on creating new content—including text, images and music—by learning the relationships between data points within vast datasets. The technology has gained traction with the development of advanced machine learning techniques, including neural networks and deep learning models.
Generative AI leverages technologies like generative adversarial networks (GANs) and variational autoencoders (VAEs). These models can learn to generate new data by identifying underlying patterns in the training data. The end result is something that has seemed impossible until only relatively recently: machines capable of creating original visual images, pieces of music, written content and even videos, all with little or no human oversight; most generative AI tools require only a text-based prompt to tell them what they should produce.
Unlike traditional AI, which is deterministic (meaning that the same conditions will always lead to the same outcomes), generative AI is probabilistic, capable of generating a wide range of outputs from any given set of inputs. This, and other factors, makes it a valuable technology that delivers several clear benefits:
- Creativity and innovation
Generative AI can produce novel and diverse content, opening new possibilities in fields like art, design and entertainment. For more traditional business applications, it is capable of creating innovative solutions to enhance customer engagement, optimise marketing strategies, streamline content creation and personalise user experiences. - Versatility
These models can adapt to various tasks, beyond the kinds of restrictions that would confine traditional AI. GenAI can dynamically respond to new data and evolving requirements, enabling businesses to innovate and remain competitive even in rapidly changing environments. - Handling ambiguity
Generative AI excels at dealing with uncertainty and complexity, making it suitable for applications where narrow AI falls short.
As with traditional AI, generative AI also presents certain challenges. These include:
- Increased resource requirements
Training generative AI models requires substantial computational power and data, which can be costly. The increased energy usage associated with GenAI may have consequences in terms of sustainability and carbon emissions. - Ethical concerns
The ability to create realistic content raises issues around authenticity, copyright and the potential for misuse. Generative AI is not naturally ethical; it follows the prompts it is given. Even when ethical rules have been implemented it may still be possible to circumvent these blocks to create content that is harmful, misleading, inappropriate or illegal.
- Traditional AI
Traditional AI focuses on analysing data and providing insights based on predefined rules. This approach ensures outputs are predictable, aligning with logical frameworks set during the programming phase. Its primary goal is to recognise patterns and generate insights that assist in decision-making and problem-solving within established parameters. - Generative AI
Generative AI creates new data and content by learning patterns from data. Unlike traditional AI, its outputs are varied and can include various media highlighting its ability to innovate and generate novel content.
- Traditional AI
The decision-making processes in traditional AI are explicit, making it easier to understand and validate how conclusions are reached. This transparency is important in applications where understanding the rationale behind decisions is essential. - Generative AI
Processes within GenAI are not nearly as transparent, operating as a 'black box', making it difficult to interpret how decisions are reached. Its complex algorithms, particularly in deep learning models, can obscure the reasoning behind specific outputs.
Uses
- Traditional AI
Traditional AI is applied in environments where tasks are well-defined, such as predictive maintenance, recommendation engines and data analysis. These applications benefit from the AI's ability to process large datasets and make accurate predictions based on predefined rules. It excels in automating routine tasks and improving operational efficiency in structured settings. - Generative AI
Generative AI breaks from structured settings to define itself in fields and applications that require original content generation, such as design and natural language processing (NLP). Its ability to produce original media makes it a powerful tool for creative industries. Additionally, it can assist in generating synthetic data for training other AI models, enhancing their capabilities without relying exclusively on authentic datasets.
The differences between traditional AI and generative AI make each technology uniquely suited for various applications:
- Spam filtering
Traditional AI can analyse email patterns and content to accurately identify and filter out spam messages, protecting users from unwanted emails and potential malicious attacks.
- Fraud detection
In financial services, traditional AI can detect fraudulent activities by analysing transaction patterns and identifying anomalies, helping to prevent financial losses.
- Recommendation systems
E-commerce, streaming services and other customer-facing businesses use traditional AI to analyse user behaviour and preferences to align product and service recommendations with users' interests.
- Predictive maintenance
In manufacturing, traditional AI can predict equipment failures by analysing historical data and usage patterns, thereby reducing downtime and maintenance costs.
- Customer segmentation
Marketing teams utilise traditional AI to segment customers based on purchasing behaviour, demographics and other data points, enabling marketing strategies that are more targeted and effective.
- Content creation
Generative AI can produce high-quality text, images, music and videos, making it valuable for creative industries including marketing, advertising and entertainment. - Customer interactions
AI chatbots powered by generative AI can provide personalised and dynamic responses to customer inquiries, enhancing customer service and engagement. - Code generation
Generative AI is used to assist software developers by generating code snippets, translating programming languages and automating code completion. This helps speed up the development process while expanding on the capabilities of less experienced coders. - Healthcare
Generative AI can create synthetic medical images for research, design personalised treatment plans and generate new drug compounds, revolutionising medical research and patient care.
At its heart, generative AI is all about learning — it learns to recognise patterns so it can then replicate the relationship in those patterns to create something new. To do this, it relies heavily on deep learning.
Diffusion models and transformer models are key components of generative AI. Diffusion models enable the generation of realistic images, while transformer models have advanced text generation capabilities. Together, these techniques have expanded the possibilities for creating synthetic media.
Transformers are a powerful deep learning architecture that have revolutionised natural language processing. They are trained on large internet datasets to predict the next token in a sequence, developing a deep understanding of language that can be fine-tuned for various tasks. Transformers' attention mechanism allows them to capture long-range dependencies and contextual information effectively. This has enabled major breakthroughs in areas like generation, translation and summarisation, making transformers a cornerstone of modern GenAI systems.
Artificial intelligence—traditional, generative and other forms—is changing the way organisations do business. ServiceNow is at the forefront of this digital transformation, optimising business processes through the advanced AI capabilities of the Now Platform®.
The Now Platform seamlessly incorporates AI to support the full range of business functions, offering intelligent solutions to automate tasks, enhance predictive maintenance and optimise operations. And with the new Generative AI Controller, organisations can integrate leading LLMs into ServiceNow services, bringing the power of generative AI into their existing workflows. Create more meaningful customer connections, enhance search functionality and improve experiences for users internal and external, with GenAI from ServiceNow. And GenAI capabilities don't stop there; ServiceNow's partnership with NVIDIA further expands its generative AI capabilities, offering innovative applications for IT departments, customer service teams and developers.
Both traditional AI and generative AI have the power to improve your business, and both are available through ServiceNow. Schedule a demo today!