The essentials
- Generative AI vs Traditional AI. Learn how traditional AI handles tasks like predictions and automation, while generative AI excels at creative content and campaign strategies.
- Marketer knowledge. Learn why marketers need to better understand generative AI to harness its potential and address concerns about data security and ethics.
If you ask the average person to give you an example of a use case for generative AI and an example of a use case for traditional AI, they probably won’t be able to do it, at least not the latter. Surprisingly, many marketers probably wouldn’t be able to do it either.
Why is this the case?
Although generative AI is in the news, there is generally a lack of education or understanding about it. This is evident from the findings of a recent study by Coleman Parkes on Using Generative AI in Marketing. Ninety-five percent of senior marketing executives don’t understand generative AI and its potential impact on the organization.
Let’s debunk the hype and look at the differences between generative AI and traditional AI and some of the things that can be accomplished with both in the marketing space.
Generative AI vs. Traditional AI: Key Differences for Customer Experience Leaders
What are the key differences between generative AI and traditional AI, and why should organizations master both? Traditional AI 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 main components:
- 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 provided as text or audio string.
- Computer Vision: It is the ability of computers to collect, process and understand visual information (i.e. images and pictures) from external environments.
Generative AI, on the other hand, is a subset of traditional AI that focuses on generating new content, rather than simply analyzing data. While generative AI is best known for content creation in marketingIt also has other uses including data mining and synthesis, campaign and marketing journey creation, dynamic price optimization, conversational marketing, customer journey mappingmarket 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 settings in which two neural networks compete to generate new content.
- Transformers: These are models that process words relative to all other words in a sentence, thereby improving contextual understanding and generating text.
While traditional AI focuses on completing tasks, making predictions, and making informed decisions using data and analytics, generative AI focuses on creativity, summation, and content generation. AI uses algorithms to process data, while generative AI uses neural networks for creative output.
Due to their differences, we see AI being widely used in automation, analytics, decision-making, learning, and process optimization, while generative AI is mainly 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 traditional and generative AI become more advanced and prevalent in society, they will have a greater impact on daily life. customer engagement From a martech perspective, software will likely evolve 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 get to that point, marketers need to delve into generative AI education. The Coleman Parkes study mentioned earlier found that marketers’ top concerns about using generative AI include: data security and privacy, followed closely by ethics, bias, accuracy, consumer trust, and internal trust. Training will help alleviate some of these concerns as marketers learn how to use generative AI responsibly.
In the question of generative AI versus traditional AI, there is no contest. Both are winners. It is exciting to imagine a world where traditional AI and generative AI are more tightly integrated to enhance both creativity and functionality in various applications and use cases!
