Artificial Intelligence Basics a Primer for Marketing Ops Professionals

While AI and marketing may seem like science fiction, there are already many products and organizations using AI in different ways. As a marketing ops professional it’s critical you have a strong understanding of the applications of AI marketing so you can set your organization up for the future.

What is Artificial Intelligence Marketing?

Marketing Ops defines Artificial Intelligence Marketing as:

The science of using smart machines to perform marketing tasks.

Within AI there are several different sub-categories such as Machine Learning (ML) and Deep Learning. Both have different applications for how they can be used in marketing.

Machine Learning (ML)

Machine learning is the use and development of computer systems that are able to learn and adapt without following explicit instructions. Some examples of ML marketing include using algorithms and statistical models to either gain marketing insights or improve customer targeting.

Due to the nature of ML using algorithms and modeling, this skill set tends to sit within an organization’s data team.

Deep Learning and Artificial Neural Networks (ANNs)

Deep learning is a subset of machine learning that focuses on artificial neural networks (ANNs).

All artificial neural networks rely on an input, a process or a ‘hidden layer’, and then an output. The process part consists of different layers and nodes that allow for sophisticated logic. It’s these series of layers in artificial neural networks that give the term ‘deep learning’.

Once these artificial neural networks are designed, they can perform sophisticated tasks such as image identification, navigation, generating content and more.

AI Research Labs

These are organizations that focus on the research and development of AI technologies. Research labs are funded by grants as opposed to selling products, however, some research labs allow the use of their technology via APIs and have a pricing structure.

How to use artificial intelligence in marketing?

Marketing Ops identifies several different ways of using AI within a marketing context.

Generative AI

A key part of being a marketer is managing and developing creative content and as such marketers tend to be experts in developing creative work (either in-house or with external help). For many marketers, this creative element of the role is one of the reasons they love working in marketing. Developing videos, imagery, and copy is part of the marketing development process.

However, there are now many AI products available that write copy, and generate videos and imagery causing much disruption to the creative industries.

There are many applications of generative AI from a creative development perspective, including creative concept development, graphic design, brand design, web/app design, and more.

AI communication and chatbots

Natural Language Processing (NLP) focuses on computers and human language. In a marketing context, this includes generating copy, text-to-speech, speech recognition (transcripts), chatbots, grammar correction, summarising text, conversations with humans, and much more.

We have seen a rapid advancement in this space, with there being many AI-powered chatbots available to implement as part of servicing customers.

An advantage of a bespoke AI chatbot is the depth of knowledge it can access such as case numbers, history of communications, escalation after certain criteria, and even identifying customer moods/tones. However, like all AI it also requires access to accurate source data and thorough testing for it to be able to assist the majority of customer inquiries.

Video communication platforms and avatars allow for quick video creation that delivers messages and in multiple languages. The application for these AI videos ranges everything from explainer videos, event management, news, user experience, tutorials, and more.

Example of AI using avatars in video content.

AI service improvement

Irrespective of content development, marketers also strive to improve their product services and experiences. This also includes automating marketing processes and tasks whether this is using models to trigger email sends, personalization, research, identifying insights, collection and cleaning of data, improved analytics, algorithms to improve targeting, and so on. These AI platforms assist in the back-end processes as opposed to creative development.

End-to-end AI marketing

End-to-end AI marketing is using an independent AI to manage the overall processes, optimization and performance of marketing initiatives. Currently, this has not yet been achieved by any organization, however, there are steps to take to make sure your organization continues to innovate for AI.

End-to-end AI marketing requires the below foundational steps to achieve an effective result.

datamarketinghierachy2

Note that digital assets also fall within data (stored, labeled, and accessible) – this is otherwise known as the ‘content layer’.

Pros 👍

Note that there are different applications of AI, so some of these examples may be very specific to generative AI, end-to-end AI, or other areas.

  • Speed – One of the advantages of using generative AI is that you can create designs in seconds. Your creative brief is simply a prompt, which can be changed, refined, and developed very quickly. You also have the advantage of not being restricted to working hours. The benefits of speed are well-known in marketing, allowing for ideas based on recent trends and news to hit home with your desired marketing target.
  • Unlimited creative versions – Traditional creative concept development usually works by having several options (usually 2 or 3), determining a route, and then finessing the concept. However, with generative AI you could have potential thousands of different ideas, layouts, and designs at your fingertips to explore, experiment and test with your target.
  • Creative customization – While traditional creative and design do have a level of flexibility, generative AI allows you to change the style, color, and content which may not be possible in traditional concept development. An example is changing a photograph that you’ve already paid a photographer for, you could pay a retoucher to customize the image, however with generative AI you can simply re-prompt the AI to adjust key visual elements.
  • Cost savings – While the initial setup of AI is expensive, the potential to save costs on manual tasks will be significant as these tools can remove the need for some creative development and certain analytics.
  • Ongoing optimization – AI models use machine learning and are capable of continually improving their output as new information is provided through their source data.

Cons 👎

  • Navigating ethical marketing – There is a range of ethical considerations that your organization needs a policy on before rolling out any sort of AI. These include image usage rights with generative AI, letting customers know they are speaking with AI, giving people choice in who and how they are communicated to, how customer data is used by machine learning models, and even monitoring the behavior of AI to ensure it’s appropriate communication.
  • Relies on accurate source data – Regardless of the AI models being used, they ultimately rely on very accurate and structured data. Both Midjourney and DALL-E provide great output imagery due to their comprehensive libraries and labeling systems. An organization must have structured data for enterprise AI to be effective.
  • How AI can interpret tasks – One of the challenges with giving instructions to AI is that it accomplishes the task but can have negative impacts on its method or technical result. For example, if you ask an AI car to drive from A to B and it drives off the road and through a house, reaching its destination quickly. It technically achieved its task but not in the method that was acceptable.  There is a great Ted Ed video that articulates this challenge.
  • Bases its decisions on historical data – Regardless of the creativity and ideation that comes from AI, it still relies on historical data. This means it’s limited in its ability to construct images, ideas, coding, and much more.
  • Organizational buy-in – Due to the hype that surrounds AI and more recently the hype around non-critical business technologies such as crypto and NFTs, management leaders are skeptical of implementing technology without a strong business case and alignment.
  • Significant testing – AI requires testing, experimenting, and fine-tuning over time. This is not a set-and-forget marketing technology and most likely never will be.
  • Lack of human touch – Ultimately humans are better at communicating and dealing with people than robots. AI will never replace human communications, however, it can help people and organizations be even better at their communicators.

AI Platforms

There are several categories of platforms within the artificial intelligence marketing space.

  1. Building and deployment: Data science platforms, ML frameworks, ANN software, and libraries – These platforms are used primarily by technical specialists including data scientists, data engineers, and modelers. These platforms are generally not considered martech software, however, it’s useful to understand the tools required for AI marketing. provide users with the libraries to store information, and tools to build, deploy, and monitor machine learning algorithms.
  2. Monitoring: Machine Learning Operations (MLOps) platforms are more focused on the operational side, and allow you to manage and monitor machine learning models as they are integrated.
  3. Content and information: Generative AI platforms focus on the generation of content, such as information, video, imagery, and copy.
  4. Operations: AI business improvement platforms focus on the automation or the improvement of processes and tasks, such as improved search, coding, or monitoring.
  5. Communication: AI Natural Language Processing, conversation and chatbots focus on the service of customers, such as chatbots and conversational AI.

Data science platforms, ML frameworks, ANN software, and libraries (development, models, and deployment)

Generative AI platforms

AI business improvement platforms

AI Natural Language Processing, conversation, and chatbots platforms

Don’t see your AI platform here? Get in touch and we’ll add it to the list.

image 1
Skynet is another company advancing its AI.

Marketing Ops recommendation

When electricity was first invented, the applications of electricity were to replicate existing heat and light-based products. Lightbulbs replaced oil lamps, the oven replaced fire-based stoves, and so on. While these products were impressive, they didn’t actually capture the full potential of what electricity was capable of. Electricity was also not easy to adopt because of how houses were built. AI is also not easy to adopt because of how business technology is structured. This doesn’t mean we should not experiment with AI, we will just need time to truly understand the best marketing applications and innovations.

AI is a long-term marketing capability, not a short-term one, so treat it as such. Organizations should definitely start investing in a strong data strategy now so that they are prepared for artificial intelligence that will rely on accurate data.

There is also ethical marketing work required prior to adopting AI and should be applied with the right values to always be beneficial to customers, their product experience and communication with organizations.

Ensure your leadership team is aligned on a universal data and AI strategy, as well as keep up to date with ethical AI policies as they develop.

Additional Resources

About The Author — Jimmy Hilvert-Bruce
JimmyHilvertBruce

Jimmy Hilvert-Bruce is a commercially focused Senior Marketer with over 10 years of marketing and martech experience. Jimmy is also the founder of martechplaybooks.com, an educational site for marketers to learn technology.

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