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From Storytelling for Humans to AI Structured Data for Machines

Updated: Nov 22, 2023

Caution: From Storytelling for Humans  to Structured Data for Machines

I'm captivated by the candid discussions that many people are engaging in regarding artificial intelligence.

We can't ignore the discourse ignited by major publications, like the Wall Street Journal with Should AI Write Doctoral Dissertations?, Le Monde with Which is better, human or machine?, The Washington Post with The debate over whether AI will destroy us is dividing Silicon Valley and the Economist with How to worry wisely about artificial intelligence.

Despite the prevalent negative sentiment in most discussions, I’m truly confident that AI revolutionizing my role and rendering me entirely OBSOLETE is a good thing.

As a Fractional Chief Marketing Officer, I always find immense joy in the art of storytelling, and I excel at creating impactful and emotionally resonant narratives for my clients. However, I sense a rebirth, similar to a newborn exploring an unknown language: AI.

Today, I'm transitioning from excelling at the art of storytelling for humans to mastering structured data for machines.

Here Is The Synopsis of My Discovery

In the past, marketing efforts primarily focused on creating compelling narratives and emotional connections with human audiences.

For instance, a clothing brand might run an ad campaign telling a heartwarming story about a family passing down their favorite jeans through generations. This approach aimed to resonate with people's emotions and create brand loyalty.

As technology advances, marketing strategies are shifting towards structured data for machines. This means optimizing content and data for algorithms and AI systems.

For instance, the same clothing brand might use data-driven marketing by ensuring all product information, such as size, color, and availability, is well-structured and easily accessible for search engines and chatbots.

Why The Evolution is Necessary

In the past, compelling narratives and messaging were meant to persuade consumers. Beautiful advertisements in magazines, powerful Super Bowl commercials, and targeted emails sought to tug at people's emotions and values to influence purchasing.

However, as AI, machine learning, and automation expand, there is a shift underway. Marketing now needs to prioritize organizing information in structured, machine-readable formats. Instead of targeting human emotions, data must be optimized for algorithms.

Depending on where you are in your marketing AI journey, you may want to ensure that:

  1. Natural language copy on websites, blogs and social media have semantic markups like metadata. This annotates content to identify key entities, relationships and details in a systematic way. AI needs these clues for understanding unstructured text.

  2. Imagery include detailed captioning and alt text so visual elements can be parsed. Catalogs must have product attributes tagged with taxonomies.

  3. Customers' profile leverages more psychographic segmentation models.

The goal of these tactics is transforming persuasive stories into categorizable, computable data. This allows automated systems like recommendation engines, chatbots and dynamic content platforms to digest marketing content.

In other words, software makes the emotive connections once left to humans.

While storytelling craft remains important, marketing now has two audiences - people and machines. Formats, frameworks and infrastructure must facilitate systematic information for algorithms to complement narrative for consumers.

As a result, machines and humans each require tailored content.

What To Focus On

AI: what to focus on

Search Engine Optimization (SEO)

With the growing importance of online presence, structured data helps companies rank higher in search engine results. Machines need structured data to understand and index web content effectively.


AI-driven personalization relies on structured data to understand user behavior and preferences. By providing structured data, companies can offer tailored product recommendations and content to individual users.

Voice Search

As voice-activated devices become more common, structured data helps these devices understand and provide answers to user queries accurately.


Structured data enables marketing automation and chatbots to provide quick and precise responses to customer inquiries, improving efficiency and user experience.

How the Transition Occurs

Data Structuring

Companies need to structure their data in a format that is machine-readable. This includes using schema markup to provide structured information about products, services, and content.

Content Optimization

Content should be created with SEO in mind. Keywords, meta-data, and other elements must be carefully optimized to be easily understood by search engine algorithms.

AI Integration

Businesses can leverage AI tools for data analysis, user behavior tracking, and personalization. These systems rely on structured data to make sense of the vast amount of information available.

AI ChatGPT for Marketing: Practical Implementations

For Marketing department, AI can be used at least in 6 different capacity.

The Marketing department can harness AI with a minimum of six distinct roles as described by Trust insights.

The Marketing department can harness AI in a minimum of six distinct roles as described by Trust insights.

Here are some examples of marketing tactics using AI:

  • Content creation: add context, share your values, beliefs and approach to ensure that language models align with your team and model your perspective and be true to your voice.

  • Persona development: Leverage AI tools to analyze customer data, synthesize insights, and identify nuanced audience segments and personas.

  • Campaign targeting: Use machine learning to optimize ad targeting and predict which customer groups will respond best to messaging.

  • Sentiment analysis: Apply natural language processing to assess emotional sentiment, spot trends/patterns, and gauge reactions to campaigns.

  • Predictive analytics: Develop propensity models that predict customer behavior using techniques like machine learning and AI algorithms.

  • Dynamic content: Personalize and tailor content in real-time through AI-enabled content management and delivery systems.

  • Conversation AI: Implement chatbots and voice assistants to engage customers through natural conversations and automate interactions.

  • Process automation: Streamline repetitive marketing workflows using robotic process automation and AI-based tools.

Take the Next Step

If you’re unsure on how to take the next step, follow Open AI CEO footsteps.

To embrace AI, you must start with a positive perspective, seeing it as a valuable tool that can improve both your work and your overall experience.

If you’re unsure on how to take the next step, follow Open AI CEO footsteps.

He uses ChatPT as a super cognitive assistant.

A super cognitive assistant has applications in a wide range of fields, including customer service, healthcare, finance, and personal productivity. They offer advanced problem-solving and decision-making capabilities beyond what traditional digital assistants or chatbots can provide.

Here is how a CEO can use ChatGPT as his super cognitive assistant to enhance their personal productivity and decision-making:

  1. Natural Language Processing (NLP): you can use ChatGPT for drafting and refining important emails and communications.

  2. Machine Learning: Use ChatGPT to analyze market trends and competitors, providing insights for strategic decisions.

  3. Problem Solving: You can use ChatGPT to brainstorm solutions to complex business challenges or to develop strategies.

  4. Context Awareness: During a busy day, use ChatGPT to keep track of important tasks and deadlines.

  5. Multi-Modal Capabilities: When reviewing project progress, use ChatGPT to summarize data from text reports, charts, and graphs.

  6. Autonomous Decision-Making: Delegate routine administrative tasks to ChatGPT, such as scheduling meetings or managing travel itineraries.

Last Thoughts

In summary, marketing's evolution from persuasive storytelling for humans to structured data for machines is driven by the need to adapt to the digital landscape, improve search visibility, enhance personalization, and efficiently engage with audiences through AI and automation.

This is a game changer for companies to remain competitive and relevant in an increasingly digital and data-driven marketing environment.

If you are curious and would like to have a conversation, we’re opening our deep dive monthly conversations to non-members. You are welcome to join us for a discussion on Web3 and AI for Marketing.

Register here.

About the Author

Virginie Glaenzer, Fractional Chief Marketing Officer

Virginie Glaenzer Fractional Chief Marketing Officer and Chief Executive Officer

Virginie, a conscious leader and accomplished serial entrepreneur, leverages her expertise to design digital marketing and sales funnel strategies that prioritize sustainability. Additionally, she empowers leaders and teams by assisting them in confidently navigating complex situations with a clear and thoughtful approach. She dedicates her work to improving people's lives by maximizing their efforts and making their intentions a reality. Promoting emerging leadership trends and evolving leaders' relationships with others is what Virginie excels at. Read Virginie's bio. Secure your fractional executive today!

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