generative ai and content marketing

The ultimate guide to using generative AI in content marketing

In today’s digital landscape, where consumers are constantly bombarded with advertisements and information, content marketing has become a vital strategy for businesses to stand out from the noise. 

Content marketing is the practice of creating and distributing valuable, relevant, and consistent content to attract and engage a specific target audience. Instead of directly promoting a product or service, content marketing focuses on providing valuable information and insights that are helpful to the target audience.

This approach not only builds trust with consumers but also establishes the business as a credible and knowledgeable authority in its industry.

Today, if your business doesn’t have a content marketing strategy then it stands little chance of attracting a loyal following, increasing brand awareness, and driving customer engagement. In a world where traditional advertising methods are losing effectiveness, content marketing provides a cost-effective way for businesses to cut through the clutter and connect with their target audience. 

Mentions of artificial intelligence in mainstream news

Recently, the content marketing industry has encountered a significant shift due to the rise of generative artificial intelligence (AI).

Like many other industries, the content marketing industry is set to become massively disrupted by AI or, more specifically, generative AI. When ChatGPT was launched on 30 November last year, it sent shockwaves across the marketing industry and the wider world.

It’s a disruption so meaningful and so fundamental it could have a greater impact on the marketing industry than the internet has over the last twenty or so years.

The rise of generative AI and its ability to not only generate high-quality content for marketing campaigns but to automate the entire process could potentially eradicate the need for some of the content creation skills we depend on humans today. 

The more you know about generative AI, the better position you’ll be in to leverage it for your business, clients and customers while futureproofing yourself in the process. 

While generative AI poses a challenge to content marketers it also presents opportunities to create consistent high-quality and engaging content by those who embrace and use generative AI tools. The AI and marketing data show that most professionals in the industry see generative AI as a positive tool to help them in their roles.

What is generative AI and content marketing?

Generative AI, at its most basic level, is an artificial intelligence model that has been trained to create new content. This content could range from text, images, music, and even code. It’s a powerful tool that has the capacity to revolutionise several industries but content marketing especially given its ability to create many content formats.

It enables marketers to create original and unique content without the need for human intervention. It uses algorithms to analyse data, learn patterns, and generate text that mimics human writing style and tone. Essentially, it removes some of the tasks associated with content creation. 

Not only this but generative AI can automate many of the repetitive or ‘low hanging’ tasks in the day-to-day role of a content marketer such as administerial, reporting, researching and so on. 

Generative AI is growing fast. In Gartner’s annual Hype Cycle for Emerging Technologies, which identifies 2,000 technologies that will bring transformational benefits within 10 years, generative AI was the leading technology in the 2023 cycle.

Gartner has placed generative AI at the “Peak of Inflated Expectations” on the Hype Cycle in 2023. This positioning suggests that it’s projected to achieve transformational benefits within two to five years and is part of the larger trend of emergent AI, which is creating new opportunities for innovation.

gartner hype cycle 2023

There are two kinds of generative AI models that are important for content marketing. 

Large Language Models (LLMs) – Sophisticated AI systems, such as GPT, that undergo extensive training in next-word prediction using massive datasets. This training enables them to grasp and generate language that closely resembles human-like communication.

Generative Adversarial Networks (GANs) – A model in which two neural networks compete with each other by using deep learning methods to become more accurate in creating multimedia. It does this using input from both imagery and text. See the evolution of the quality output of the popular GAN, Midjourney below.

In simple terms, LLMs are known for their ability to understand and generate human language and GANs are known for their ability to generate realistic images.

midjourney versions compared

LLMs are the base layer for AI content marketing applications

Many of the generative AI tools available for content marketers today are applications built on top of an LLM or a ‘general purpose AI’. These new content marketing tools use the API provided by the LLM to create a bespoke service for marketers. The most popular today is OpenAI’s ChatGPT but it is by no means the only one. 

We’ve seen in the past that dependency on a third-party API has often been risky for companies that build on top of it because the API can change on a whim or indeed be revoked altogether. For now, however, everyone is playing nice.  

In recent months, Google, Meta and a host of other companies have launched their own LLM and while OpenAI’s flagship GPT still leads for now the landscape is changing rapidly. 

Certainly! The five major categories of multimedia content that generative AI is impacting include Language, Image, Video, Audio, and Code. Here’s a brief elaboration on each:

The five major content categories generative AI will impact the most

1. Language:

Generative AI models will change the way we interact with and generate text. They can write essays, create poetry, generate conversational agents, translate languages, and even mimic specific writing styles. These models are being used in various applications like chatbots, content creation, and educational tools, making human-like text generation more accessible and efficient.

2. Image:

Generative models like GANs have already made significant strides in image generation. They can create realistic images, modify existing ones, or even generate art. Applications include virtual fashion design, facial recognition, medical imaging, and more. Deepfakes, where the likeness of one person is superimposed onto another in photos or videos, are also a product of this technology, raising many ethical considerations.

3. Video:

Video generation and manipulation through AI have opened doors to new forms of entertainment, education, and advertising. AI can create realistic animations, enhance video quality, generate 3D models from 2D images, and even create entire scenes or movies. This technology has applications in filmmaking, video games, virtual reality, and more. Like with images, deepfake technology can also be applied to videos, leading to potential misuse.

4. Audio:

Generative AI in the audio domain has led to advancements in speech synthesis, music composition, sound effects, and more. Text-to-speech technologies can create lifelike voices for virtual assistants or audiobooks. AI-driven music generators can compose original scores or mimic specific styles. These technologies are being used in entertainment, accessibility tools for the visually impaired, and other areas where sound plays a crucial role.

5. Code:

AI-driven code generation is a growing field that can assist developers in writing, debugging, and optimising code. Models like OpenAI’s Codex can understand programming languages and generate code snippets, automate repetitive tasks, and even build entire applications. This not only speeds up the development process but also makes programming more accessible to those without formal training. It’s being used in software development, data analysis, and more.

The benefits of generative AI in content marketing 

When integrated thoughtfully, generative AI enables content teams to tap new levels of productivity, cost savings, creativity, personalisation and data-driven insight. From small brands to Fortune 500 companies, organisations across industries stand to enhance nearly every aspect of their content operations by leveraging AI. Some of the most game-changing benefits of generative AI for supercharging content marketing include:

  • Increased output – Generative AI can draft, rewrite, and optimise content at scale far faster than human creators, increasing total content production. Research by Spiceworks found 66% of marketers using generative AI have witnessed positive ROI
  • Consistent quality – AI tools generate content reliably on-brand, on-message, and with consistent formatting/style. Research by Botco.ai found marketing professionals using generative AI to develop content have seen positive results.
  • Cost efficiency – Automation reduces the time and labour costs associated with manual content creation. Salesforce research found AI can save marketers hours a week.
  • Customisation – Generative content can be tailored for different audiences, formats, and platforms based on data.
  • Data-driven insights – AI analysis uncovers insights about optimal messaging, topics, and timing to resonate.
  • 24/7 productivity – Intelligent content creation and optimization does not stop outside business hours.
  • Fresh ideas – Creative AI tapping data and patterns generates novel, outside-the-box ideas and angles. Research by Adobe found over 95% of marketing professionals believe Generative AI will enhance creativity.
  • Higher relevance – Personalised content resonates more deeply with each customer.
  • Omnichannel leverage – Efficiently repurpose and localise content across platforms and languages.
  • Focus on strategy – Automating repetitive tasks allows humans to focus on high-value strategic work.

An overview of generative AI use cases in content marketing

Generative AI can be a valuable tool during the content creation process and can also help support day-to-day administerial tasks. Here are some of the generative AI use cases in content marketing. 

  • Ideation – LLMs can help generate ideas and expand on creative prompts for new campaigns, taglines, product names, etc. Their ability to make novel associations can spur outside-the-box thinking.
  • Writing – Generate initial drafts of articles, social media posts, emails, advertisements etc. to kickstart the writing process.
  • Editing – Proofread, format, analyse tone and make suggestions on how to improve pieces of writing
  • Outlines – Gather, structure and refine data to feed a compelling article outline.
  • Transcription – AI speech recognition tools can efficiently transcribe audio interviews, focus groups, conference talks etc. into text and summarise them in an easily understood format.
  • Summarising – Automatically distil insights from lengthy analytical and research reports by identifying and extracting key facts, figures, and takeaways from articles to highlight.
  • Simplifying – It can refine content for easier consumption by target audiences while preserving core information
  • Note-taking – Simplifying and enhancing note-taking with transcription combined with data extraction and organisation helping to capture and connect insights from meetings and discussions.
  • Multimedia generation – From audio to images to video to audio, AI tools can automate multidimensional media production.
  • Data analysis – AI’s pattern recognition capabilities can unlock deeper insights from customer and marketing data that can drive more informed business decisions.
  • Data visualisation – Support throughout the data visualisation process from data stories, chart creation, recommendations etc.
  • Workflow automation – Automating repetitive tasks to allow content marketers to focus on high-level strategy and creativity.
  • Coding – From writing, reviewing and fixing code, AI can automate lower-level coding tasks to boost efficiency for developers and marketers.

We are only beginning to scratch the surface in terms of the use cases for generative AI in content marketing and as the technology becomes more capable and proficient new use cases will become possible. 

Types of generative AI tools for content marketing 

generative ai and content marketing

Now that large language models are available for third-party companies to build on top of, we are beginning to see a swath of new tools and services that help generate content quickly and efficiently. The marketing team that begins to implement generative AI earlier will develop a first-mover advantage. Here is a non-exhaustive list of generative AI tools that can support the content creation process. 

ChatGPT – Serves as the application layer for numerous new services. It can be used to perform numerous tasks from writing to data analysis.

AIPRM – A pre-programmed ChatGPT prompt engineering tool which can write all kinds of marketing material including SEO content, persona development, social posts, paid advertising etc.

Jasper – A writing service. that marketing copy, social posts etc.

Content at Scale – All-in-SEO writer that provides SEO planning, SEO writing, keyword research etc.

Editby – Multipurpose content curation, email newsletter writing, article summaries etc.

Wondercraft – Create AI-written and narrated podcasts using advanced audio capabilities.

Supernormal – Produces transcripts, notes and action points from online meetings. After a meeting it produces a summary of key points discussed and next steps.

Chatbase – Create a bespoke LLM trained on your data. You provide Chatbase with your content (website, articles, business plans, brochures etc) and you can search it and ask questions.

Monica – An AI assistant that supports with research, summarisation, responding to emails etc.

Storyd – An AI presentation generator that supports building pitch decks and other types of presentations.

Github Co-Pilot – An AI code assistant that supports developers in creating and fixing code.

Copyright issues surrounding generative AI 

While generative AI offers exciting creative potential, it also raises unsettled questions around copyright law that create risks for marketers exploring these technologies. Prudent brands are closely tracking developments in this area. As we figure out the copyright issues surrounding generative AI, a recent Drum article summed up the situation well.

Generative AI models like ChatGPT and DALL-E are trained on vast amounts of data scraped from the internet, including copyrighted material. This is raising concerns about copyright infringement. Recent lawsuits have accused companies like OpenAI and Meta of illegally copying authors’ work without permission to train their AI models.

The legal status remains ambiguous, but critics argue using copyrighted data to train AI constitutes unauthorised copying and distribution. Defenders contend it may qualify as fair use. Courts will likely take a nuanced view as cases unfold.

It’s unclear if AI-generated content itself can be copyrighted since US law protects only “original works of authorship” created by humans. For now, marketers leveraging generative AI should monitor legal developments closely and limit training models on copyrighted data if clients are risk-averse. The content they create with AI may not yet be copyrightable.

The legal landscape is still fluid and complex. It will take time to resolve the intellectual property and copyright issues surrounding this technology. In the interim, marketers should maintain connections with legal teams for ongoing guidance.

Limitations of the application of generative AI in content marketing  

While generative AI provides a number of benefits to content marketing, its current state does include a number of limitations. These include:

Accuracy – Generative content tools can sometimes include factual errors or present misinformation without proper training data and oversight. Human review is critical.

Originality – AI-generated content risks repetition and lacks true creativity without human direction. Unique angles and storytelling require human ingenuity.

Strategic focus – AI tools may ramble or lack a central narrative without an understanding of the strategic goals only humans possess. People provide core focus.

Subject matter experience – Current AI lacks real-world expertise and wisdom that allows the nuanced perspectives humans offer. Reliance solely on data has limits.

Ethical risks – Generative content could produce harmful, biased, or misleading messaging without oversight and governance. People provide moral guidance.

Emotional appeal – Content that resonates requires human judgment of tone, wit, and empathy that AI cannot authentically emulate yet. People connect ideas and evoke emotion.

Attribution concerns – Audiences expect authenticity and may be wary of fully automated content creation lacking human transparency. Clear authorship and attribution matters.

The core argument is that while the capabilities of generative AI for content are powerful and transformative, blind reliance without human creativity, strategy, ethics and oversight poses risks. The synergy of human plus machine is ideal.

The future of generative AI and content marketing 

The capabilities of generative AI will soon revolutionise most of the content creation process. Take video, for example, where new startups are pioneering automated video creation using just text prompts. This allows for easy customisation of product explainers, data summaries through motion graphics, and tailored video ads for different audiences.

Rather than rely on expensive manual video production, generative AI enables marketers to efficiently scale high-quality video content. As the technology advances, auto-generated video may reach Hollywood-caliber production values and be indistinguishable from human-made films and commercials.

Generative AI also unlocks new potential for automated audio content creation. Advanced text-to-speech technology empowers the effortless development of podcasts, audio ads, and other audio formats. This allows audio content to be customised and delivered at scale on platforms like Spotify to better engage consumers. Generative audio even improves accessibility through automated closed captioning abilities.

Importantly, generative AI will also transform content insight and strategy through powerful analytics. Rather than intensive manual analysis, generative algorithms can rapidly uncover buried patterns and insights from customer data. Startup companies are pioneering this smart data analysis to optimise content strategy based on consumer behaviour and preferences. This data-driven approach revolutionises how organizations create content that truly resonates.

Best practices for integrating generative AI into a content marketing team 

Integrating generative AI for content marketing will be an ongoing and iterative process as the implementation of it will continue to change as the technology advances.

We are still in the early days of this new technology and as we’ve witnessed over the last 15 years, new tools, methods and platforms continue to evolve and AI will be no different. Here are some steps for incorporating generative AI into the content process:

Content creation process audit

  1. Map out current content creation process: Assess end-to-end procedures, including tasks, roles, formats, tools, and pain points.

AI integration assessment

  1. Identify AI opportunities: Investigate areas where AI can assist with ideation, drafting, editing, formatting, repurposing, translating, and analysing data.
  2. Focus on high-value tasks: Prioritise integrating AI in high-value, repetitive tasks and for creative ideation support.

Governance and quality control

  1. AI usage guidelines: Develop clear guidelines on when and how to use AI, with a focus on human oversight for accuracy and quality control.
  2. Implement a multi-stage review process: Ensure AI-generated content undergoes rigorous checks before publishing.

Tools and services

  1. Leverage in-house and external AI tools: Use a combination of in-house AI utilities and external content intelligence services.
  2. Frictionless AI adoption: Make the AI integration seamless by incorporating it into existing platforms and software.

Team training and adaptation

  1. Offer training and support: Educate the team to build their capabilities and comfort level with AI technologies.

Continuous improvement

  1. Refine AI strategy: Continuously update the AI integration strategy based on impact, user feedback, and evolving AI capabilities.

Human-AI synergy

  1. Maintain human creativity: Always prioritise human creativity and use AI as an augmentation, not a replacement.
  2. Be transparent and accountable: Clearly state when and how automation is being used and maintain accountability.
  3. Foster a hybrid model: Develop a working model that optimises the synergy between human teams and AI augmentation.

How content marketing teams can use generative AI to their advantage

As generative AI continues advancing at a rapid pace, it is crucial for content marketing teams to closely track developments and understand how this technology can transform their strategies and workflows. Integrating solutions like advanced content writing and data-driven analytics tools have the potential to greatly augment their productivity.

Blind adoption without ongoing education around capabilities, limitations and responsible implementation can pose risks. Content teams need to take a proactive approach to leveraging AI as an enhancement that works synergistically with human creativity – not a replacement for roles.

By continuously expanding their AI literacy and integrating it thoughtfully at the right stages of ideation, creation and distribution, content marketers can unlock substantial value. The teams that embrace AI as an optimisation tool while prioritising their innate human skills will gain a distinct competitive advantage.

The goal is to develop an informed understanding of generative AI’s strengths and weaknesses. This empowers teams to best leverage AI as a tool integrated within creative workflows rather than a black box. 

Conclusion 

As explored throughout this guide, generative AI looks like it will be a seminal shift in how marketing content is produced by the immense potential it has to transform content creation, strategy, and marketing operations.

When applied judiciously, generative tools can enhance ideation, drafting, editing, data analysis, and more to unlock unprecedented efficiency, personalisation, and insight. However, prudent integration requires ongoing education, human oversight, and iterative refinement to mitigate risks around inaccuracy, bias, and other limitations.

Though the technology is still evolving rapidly, brands proactive in building their AI literacy and thoughtfully leveraging its strengths in synergy with human teams will gain a distinct competitive advantage.

With diligent strategy tailored to objectives, generative AI can propel marketing content to new levels of impact by augmenting human creativity. The key is to remain striking the optimal balance between machine capabilities and human judgment.

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