The Gist
- Generative AI vs. traditional AI. Learn how traditional AI handles tasks like predictions and automation, while generative AI excels in creative content and campaign strategies.
- Marketer’s knowledge. Discover why marketers need to better understand generative AI to leverage its potential and address concerns around data security and ethics.
If you ask the average person to provide one example of a generative AI use case and one example of a traditional AI use case, they probably couldn’t do it — at least not the latter. Startlingly many marketers probably wouldn’t be able to either.
Why is this the case?
While generative AI has been all over the news, there is generally a lack of education or understanding about it. This is clear from the findings of a recent study from Coleman Parkes about generative AI usage in marketing. Ninety-five percent of those in senior marketing management don’t understand generative AI or its potential impact on the organization.
Let’s declutter the hype and delve into the differences of generative AI vs. traditional AI and some of the things that can be accomplished with both within marketing.
Generative AI vs. Traditional AI: Key Differences for CX Leaders
What are the main differences of generative AI vs. traditional AI, and why should organizations have a strong handle on both? Traditional artificial intelligence is, simply put, the broad field of creating machines that can perform tasks that typically require human intelligence.
Components of Traditional AI
Traditional AI contains three major components:
View all
- Machine Learning (ML): These models and algorithms use data to learn and understand patterns and behaviors, allowing them to make predictions or decisions.
- Natural Language: This is the ability to process (natural language processing or NLP), understand (natural language understanding or NLU) and generate (natural language generation or NLG) human language that is provided in text or audio string form.
- Computer Vision: This is the ability for computers to collect, process and understand visual information (i.e., images and photos) from external environments.
Generative AI, meanwhile, is a subset of traditional AI that focuses on generating new content, rather than just analyzing data. While generative AI is most commonly known for content creation in marketing, it has other uses as well, including data exploration and summarization, marketing campaign and journey creation, dynamic pricing optimization, conversational marketing, customer journey mapping, market research, trend analysis and search engine optimization.
Components of Generative AI
Generative AI as a technology has two main components:
- Generative Adversarial Networks (GANs): These are frameworks where two neural networks compete to generate new content.
- Transformers: These are models that process words in relation to all other words in a sentence, improving understanding of context and generating text.
While traditional AI focuses on completing tasks, making predictions and informing decisions using data and analytics, generative AI focuses on creativity, summation and content generation. AI uses algorithms to process data, whereas generative AI employs neural networks for creative output.
As a result of their differences, we see AI being used broadly in automation, analytics, decisioning, learning and process optimization, while generative AI is primarily applied in art, media generation and creative industries — at least for now.
Related Article: AI in Marketing: Genius or Disaster?
Shaping the Future of Customer Engagement with Traditional and Generative AI
As both traditional and generative AI become more advanced and widespread in society, they will have a greater impact on everyday customer engagement practices. From a martech perspective, software will likely advance to the point where a campaign brief is the only human-generated input — and the combination of AI and generative AI will be able to take that and create the strategy, audience, journey, content and activation rules needed to do the rest.
However, before we can get to this point, marketers need to dive into educating themselves on generative AI. The Coleman Parkes study mentioned earlier found that marketers’ key concerns around generative AI usage are data security and privacy, followed closely by ethics, bias, accuracy, consumer trust and internal trust. Training will help mitigate some of these concerns as marketers learn to use generative AI responsibly.
In the question of generative AI vs. traditional AI, there’s no contest. Both are winners. It’s exciting to envision a world where both traditional and generative AI are more tightly integrated to enhance both creativity and functionality in various applications and use cases!
