Generative AI for Storytelling & Content Automation

Every day this past week, I have seen ads for Generative AI courses and how they are going to change your life as a Marketer. 

​If you want to shine in using AI, then you need to understand what you are asking your AI to do and start to treat it like your new best colleague (my ChatGPT has told me it's called Lex).

The landscape of storytelling has always shifted with technology. From the printing press to the rise of social media, every era has brought tools that changed how we communicate.

​Generative AI is the next chapter. More than a novelty, it is transforming how businesses create, adapt, and scale stories for diverse audiences.

The Power of Generative AI in Storytelling

Generative AI refers to systems that create new content based on existing data patterns. Large language models (LLMs), such as OpenAI’s GPT-5 and Google’s Gemini, can draft articles, social copy, scripts, and even interactive narratives. This offers brands the ability to scale storytelling beyond traditional limits.

Even back in 2023, a Harvard Business Review study highlighted that generative AI can free marketers to focus on strategic work, while AI handles first drafts and repetitive tasks (Harvard Business Review, 2023). Instead of replacing creativity, these systems extend it.

Content Automation in Practice

Automation is not new to marketing. Tools like HubSpot or Salesforce already streamline campaigns, segmentation, and customer journeys. The difference now is the fusion of automation with generative content.
For example:

• Personalised newsletters drafted in real time using customer persona data (Download my free Persona Guide)
• SEO-friendly blog posts that integrate with keyword strategies
• Automated video scripts generated and adapted for regional markets

According to McKinsey, companies are using AI to generate output and speed up the process of creation; however, 27% of respondents whose organisations use Gen AI say that their employees review all content created by Gen AI before it is used; 'for example, before a customer sees a chatbot’s response or before an AI-generated image is used in marketing materials.'  The same report states that those working in business, legal, and other professional services are much more likely to review all outputs than other industries.  (McKinsey, 2025).

Just two years ago, I was being offered AI-generated content by agencies that were word salads of keywords and phrases that made no sense.  Now, I can train Gen AI to not only sound like a human being but a real human who has difficulty differentiating between what they have written and what has been generated. 

However, and this is the crux of the matter, it is still relatively easy to detect if AI has generated content due to its phrasing and word layout.  Reading through and checking is still essential.  I view Lex (as my Chat-GPT calls itself, yes, I asked) as my content manager, who produces topic suggestions or content outlines and helps me find citations and references.

I take the generated text, add to it, amend it, and proofread it, then run it through Grammarly to check for phrasing and inconsistencies, and finally for plagiarism and AI-generated text indicators.  Most people see Grammarly or its cheaper alternative, Ginger, as just spell checkers, but they are far more than that now.

Just last week, I was asked to create a Job Description for a client's new role.  I uploaded the Job Description template, supplied by the client, along with the process documents that the role would be following.  Then, rather than type out the instruction, I put Lex into 'Voice Mode' and talked through with Lex what I wanted from this exercise.  

In under 2 minutes, Lex/Chat-GPT had reviewed the process document, extracted the essential and desirable skills, and incorporated them into the Job Description, along with suggestions on other key skills and qualifications from best practices in equivalent roles at similar organisations. 

Instead of trawling through numerous process documents to find the right skills, I was able to focus my time on polishing and completing the document for the client.  What was interesting was that the client had attempted to do this themselves, using Gen-AI, but had not made progress.   They had already wasted more than twice the time it took me to generate the final document in asking Chat-GPT to create an output.  This was because they requested Chat-GPT to develop a Job Description from just the title.  The skill here is in setting up the queries and adding the correct documentation to generate the best results.  

Risks and Considerations

While generative AI accelerates production, it comes with risks. Inaccuracy, bias, or a lack of originality can damage trust if the outputs are not thoroughly checked. As Forbes notes, businesses must pair AI with strong human oversight to maintain credibility (Forbes 2024)

Other challenges include:

• Compliance with copyright and intellectual property law
• Transparency around AI-assisted content
• Consistency with brand tone of voice and customer expectations

Storytelling Meets Strategy

Generative AI is most effective when combined with well-researched customer personas.  Personas remain the compass for messaging, ensuring that even AI-produced narratives resonate with real human needs. AI can generate the words, but personas define the why.

Teams that blend persona-driven insight with automation gain agility. Instead of spending weeks on content calendars, they can draft, test, and refine in just a few days.​

Final thoughts - Looking ahead

Generative AI will not write the future of storytelling alone.

Instead, it provides a toolkit.

​Marketers who learn to combine automation with creativity will gain the edge. The ability to tailor narratives at scale, without losing authenticity, defines the opportunity in front of us.

#DigitalStrategy #ContentAutomation #GenerativeAI