What's the Difference Between Traditional and Generative AI?

Artificial intelligence (AI) has actually been around since the 1950s, but only recently have businesses of all sizes been able to take advantage of this technology. AI has evolved to become relevant in virtually every sector. In fact, AI use among small businesses has more than doubled compared to 2023, with almost 2 in 5 business owners reporting they use AI.

As you explore the various ways to integrate AI, you may hear the terms “generative AI” and “traditional AI.” Here’s what these types of AI can do and how to determine what your business needs.

What is traditional AI?

Traditional AI is a subset of artificial intelligence that focuses on performing preset tasks using predetermined algorithms and rules. These AI applications are designed to excel in a single activity or a restricted set of tasks, such as playing chess, diagnosing diseases, or translating languages.

Traditional AI is smart, but it’s not necessarily responsive. “Other examples of traditional AIs are voice assistants like Siri or Alexa, recommendation engines on Netflix or Amazon, or Google's search algorithm. These AIs have been trained to follow specific rules, do a particular job, and do it well, but they don’t create anything new,” wrote Forbes.

There are plenty of useful applications for traditional AI. Some artificial intelligence spam filters, for instance, use predefined rules to isolate spam emails from your main inbox. Ultimately, however, traditional AI is only as effective as the data used to train the algorithm. It has limited efficacy in streamlining and optimizing your business.

How does traditional AI work?

Traditional AI systems are typically trained on large datasets of labeled data. The system learns to identify the patterns in the data and uses them to make predictions or generate outputs.

Here are some examples of traditional AI:

• Expert systems: These systems are designed to emulate the knowledge and expertise of humans skilled in a specific field. For example, an expert system could be used to diagnose diseases, troubleshoot technical problems, or provide financial advice.
• Decision trees: These systems are used to make decisions based on a set of rules. For example, a decision tree could be used to decide whether or not to approve a loan application or to recommend a product to a customer.
• Natural language processing (NLP): NLP systems are used to understand and generate human language. For example, NLP systems are used in search engines, chatbots, and machine translation systems.

While traditional AI is still widely used, generative AI is rapidly becoming the preferred technology for business owners across industries.

[Read more: 4 Effective Ways Small Businesses Can Leverage AI]

What is generative AI?

Generative AI produces text, video, images, and other types of content. Popular tools like ChatGPT, Gemini, and DALL-E are all examples of generative AI. Put simply, the key difference between traditional and generative AI is that generative AI can create something new.

“[G]enerative AI models are fed vast quantities of existing content to train the models to produce new content. They learn to identify underlying patterns in the data set based on a probability distribution and, when given a prompt, create similar patterns (or outputs based on these patterns),” wrote Investopedia.

Generative AI relies on machine learning to understand, predict, and create content from data. It takes a massive amount of data for generative AI to function, and machine learning provides the training that fuels the AI to produce its results.

Which type of AI is right for your business?

Traditional and generative AI both have a role in helping your business run more efficiently.

“While traditional AI and generative AI have distinct functionalities, they are not mutually exclusive,” wrote Forbes. “Generative AI could work in tandem with traditional AI to provide even more powerful solutions. For instance, a traditional AI could analyze user behavior data, and a generative AI could use this analysis to create personalized content.”

Look for repetitive and routine tasks that could be outsourced to AI to take full advantage of these technologies. Sales, marketing, recruiting, and customer service all have activities that could be improved through traditional and generative AI tools.

Risks and limitations of generative AI tools

Although generative AI tools have exploded in popularity, there are some drawbacks to using these tools that all small businesses need to consider. “Generative AI can help us achieve great things, but it comes at an ethical, social, environmental, and human cost,” wrote the University of Leeds. “It is important to consider these factors and support ethical frameworks around the use of generative and other forms of AI. It is also very important to always check if the content produced is accurate and not an AI hallucination.”

An AI hallucination is information that appears factual but is often inaccurate. AI models can make mistakes. In fact, experts estimate that AI models hallucinate anywhere from 3% to 27% of the time. Therefore, fact-check everything that comes from a generative AI model before you share it with customers or employees.

Generative AI also has creative limitations. It can only generate ideas, images, or copy in response to the prompt you give it. If your prompt is vague or general, expect a somewhat generic response. You will have to edit content, images, and other assets to be unique and reflective of your brand. Harvard’s Information Technology Department has helpful tips on how to write better AI prompts.

Business use cases for traditional vs. generative AI

Traditional and generative AI can help you run your small business more efficiently.

Traditional AI helps analyze information, speed workflows, and improve decision-making. “Traditional AI’s power lies in its ability to automate routine tasks, which can enhance decision-making processes and improve predictive capabilities,” wrote Presidio, a global provider of digital services.

For instance, traditional AI can be found in fraud detection solutions in which it scans thousands of transactions, identifies patterns of normal activity, and flags any anomalies. Traditional AI is often used in project management software and scheduling tools to send reminders, route documents for signatures, and set deadlines. You’ll also see AI in many financial tools to assist with forecasting and budgeting.

Generative AI can help with creative tasks, especially those related to customer outreach. “Generative AI can aid sales and marketing efforts by helping to accelerate research and summarize lengthy information about audiences and markets,” wrote Google.

Generative AI can perform basic writing and editing, brainstorm content ideas, analyze subject lines and email content, and help sales teams hone their pitches for different groups of leads. Generative AI can also be found in tools that help build websites and design visual elements. For instance, Wix’s Artificial Design Intelligence can generate royalty-free images that you can add to your website to attract viewers.

How to get started with AI in your small business

As you figure out how to integrate AI, start by looking for simple, routine tasks that could benefit from automation. Scheduling, social media posting, and customer service are a few business areas ripe for AI integration.

Then look for a specific tool or platform that offers the type of AI you need to solve your business problem. “Comparative research on features and pricing can ensure you choose the right AI tool for the application and outcome,” wrote Investopedia. “Check to make sure that the tool can be integrated with current systems and scaled according to the needs of the business as it grows.”

Provide the appropriate training to team members who will be using the AI software the most, and monitor its performance to make sure you’ve found the right solution. You might need to offer advanced training to help your staff create the write prompts for a generative AI tool. Alternately, you might need to feed traditional AI the right data for it to produce the desired results.

“Make time to reflect and solicit feedback on how the tool is working for the business and its employees, and take advantage of any machine learning capabilities that the tool can provide over time,” wrote Investopedia.

CO— aims to bring you inspiration from leading respected experts. However, before making any business decision, you should consult a professional who can advise you based on your individual situation.

Your hard work deserves to be rewarded! Apply for our premier small business awards program, the CO—100, to earn national media attention, get VIP access to premium networking events, and potentially be awarded $25,000! Apply and learn more, here.