As communicators, we naturally gravitate toward content generation use cases of gen AI like writing social posts, blog outlines, video scripts, newsletter headlines and beyond. However, these applications are only the tip of the iceberg.
Note: In this context, “gen AI” refers to large language models (LLMs) like ChatGPT, Gemini, Copilot and Claude. While other text-to-image gen AI tools exist like DALL-E and Midjourney, this post specifically explores the language-related applications of such AI. For a deeper dive into AI’s creative potential, check out our recent post.
While LLMs are powerful tools for generating text, their applications go well beyond simply creating content. Like using a Swiss Army knife solely as a bottle opener, while effective, it overlooks a vast range of tools at your disposal.
Trust Insights summarizes and categorizes Gen AI use cases in six core categories:
Generation: Most commonly thought of application – creating text
Extraction: Pulling specific insights or information from text
Summarization: Condensing text or making it easier to understand
Rewriting: Transforming existing text into another format or style
Classification: Organizing or categorizing text
Question answering: Providing insight based on text
While these use cases show the breadth of gen AI’s capabilities, by applying them to solve real challenges, communicators can turn these potential applications into practical solutions that were once impossible.
This is where user stories come in.
User Stories vs. Use Cases – Why Communicators Need Both
Unlike use cases, which are broad and technology-focused, user stories are centered on people and their specific needs. Commonly used in commonly used in agile software development and product management, user stories are gaining popularity with communicators as an effective way to align actions to our goals – particularly when choosing platforms or tools.
By first defining who will use AI and what they want to achieve, we can focus on how the AI can solve the particular challenges we face, rather than just exploring its general capabilities.
For example, a user story might be, “As an integrated communications specialist, I want to streamline the creation of social media posts so that I can maintain a consistent posting schedule without sacrificing quality.”
This user story doesn’t include a specific use case. Although generation – writing social media posts – is certainly one applicable use case, other use cases could also help improve the efficiency of the process. Extraction could be used to pull key insights, quotes or statistics from a larger piece of content, such as a blog post or report, helping inform engaging copy or graphics to accompany a post.
Classification could help by organizing and categorizing posts by themes, topics or audiences, making it easier to plan and execute a content calendar that remains consistent and aligned with ongoing campaigns.
Knowing social media audiences have different expectations across platforms, rewriting can help tailor your posts to fit the tone and style required by each platform. This ensures that your content resonates with the unique audience on each platform while maintaining consistent messaging.
By considering multiple use cases in relation to a single user story, communicators can fully leverage AI to address a broader range of challenges, making their work more efficient and effective.
How to Write Effective User Stories
An effective user story helps connect our actions to our purpose.
“As a [role], I want [action], so [desired outcome].”
These simple statements should focus on the user’s needs rather than the application. Meaning, they should not reference AI nor any specific use cases. This approach enables teams to explore potential opportunities without being bogged down by preconceived notions about what the AI should do.
For example:
❌“As a social media manager, I want AI to generate posts for me, so that I can save time.”
- Narrowly defines the approach rather than focusing on the actual problem.
- Lacks clarity on what “saving time” really means or how it impacts the overall goal.
- Ignores the bigger objective of maintaining content quality and consistency across platforms.
✅“As a social media manager, I want to streamline post creation, so that I can maintain a consistent posting schedule without sacrificing quality.”
- Focuses on the real need rather than the solution.
- Clearly defines the desired outcome.
- Addresses the ultimate objective: Ensuring content consistency and quality across platforms while improving efficiency.
Just as a communicator’s job is much more expansive than writing, LLMs can do much more than generate text.
While gen AI is incredibly powerful for a variety of use cases including (but not limited to) content generation, the true value of these tools lies in how they can be applied to solve specific challenges in our work.
The key to unlocking the full potential of gen AI lies in identifying the specific pain points and challenges within our work processes – and then creatively applying these technologies to address those issues. This approach enables communicators to unlock the full spectrum of gen AI’s potential, transforming LLMs from mere content generators into multifaceted tools that expand our capabilities and open new strategic possibilities.
Lexi Trimpe is an Integrated Communications Manager – Digital at Franco. Connect with her on LinkedIn.
