Four generative AI use cases for marketing operations

Since ChatGPT debuted in 2022, marketers have primarily used it for customer engagement, thanks to its effectiveness as an automated copywriter. However, the capabilities of large language models (LLMs) are broader than content production. LLMs can streamline workflows and refine processes by tapping into a deep understanding of a company's internal operations. They can address bottlenecks, free up human brain power, and point teams in a data-backed direction.

1. Simplifying data for stakeholder updates

ChatGPT is a natural language filter that transforms complex data into clear, concise insights. By feeding it detailed performance data, it extracts and presents the key points, making it easier for stakeholders to grasp essential insights without getting lost in the details.

Implementing this process is straightforward with no-code tools like Zapier, which can connect data sources such as Airtable to OpenAI. This setup automates the delivery of weekly headlines directly to Slack, providing visibility without the need for deep dives or extensive email exchanges.

2. Enhancing data privacy in communications

Understanding the importance of data privacy, we've engineered a workflow that integrates website form submissions with Slack, our primary internal communication tool, while maintaining confidentiality. This system uses OpenAI to detect and redact personal information, safeguarding against unintended data exposure and aligning with our high standards for privacy and security. Importantly, OpenAI doesn’t use this data for training. 

After the redaction process, the cleaned data is shared with our team on Slack, allowing for informed discussions and feedback without compromising privacy. This approach adheres to Slack's privacy policies and reinforces our commitment to secure and responsible data handling.

3. Categorising and filtering inbound form submissions

Our contact page is often the first touchpoint with potential clients, partners, or employees. However, sifting through the messages can be overwhelming, especially when relevant queries get lost in spam. Generative AI can read these faster than any human. It smartly categorises each submission, quickly identifying and prioritising genuine inquiries, which speeds up our response times and enhances our overall communication strategy.

4. Replacing intake documents with interviews

Traditionally, gathering information for case studies and blog posts has relied heavily on intake forms, requiring stakeholders to complete detailed documents. Generative AI has allowed us to take a more dynamic approach, replacing these forms with 30-minute interviews. This method streamlines the information collection process and allows for a deeper, more nuanced understanding of the subject matter.

ChatGPT can extract key points and themes from transcripts. The LLM can further analyse the content to identify insights, quotes, and data most relevant to the intended output, like a case study, blog post, or other content format. This shift towards interactive interviews facilitated by AI transcription and analysis tools saves time. It enhances the quality of the content produced by capturing the essence of the stakeholder's input more effectively.

This approach fosters a more personal and engaging process for stakeholders, encouraging open communication and a richer exchange of ideas. It demonstrates an innovative use of generative AI in marketing operations, moving beyond traditional methods to improve output quality and turnaround while making the process more engaging for all participants.

AI is a team player, not a team replacer

Through strategic implementation, generative AI can augment human capabilities, enhance operational efficiency, and foster dynamic stakeholder engagement. By automating routine tasks, simplifying complex data analysis, and enabling more personalised communication methods, generative AI empowers marketing teams to allocate their creativity and strategic insights where they matter most. Beyond's experiences highlight the importance of viewing generative AI not as a replacement for human talent but as a powerful tool that complements and enhances our existing skills and processes. As marketing operations continue to advance, embracing generative AI in this manner will be necessary for organisations seeking to stay ahead in an increasingly competitive landscape.