Artificial Intelligence (AI), and particularly generative AI (GenAI), is exciting brands around the world with new business opportunities as well as time and cost savings that the technology promises.

In a recent 2024 analysis article McKinsey revealed that in Australia, 62 percent of existing task hours could be automated using GenAI, with the potential to rise between 79 and 98 percent by 2030. 

For marketers specifically, the technology presents many additional opportunities: from chatbots and automated content generation to the identification of new niche markets and the distribution of hyper-personalised contextual communications. Some even predict that AI-generated content will make up half of business social media by 2026. 

But before diving head first, marketers need to understand that using AI without a certain understanding of how the technology works and without safeguards can lead to significant inaccuracy and security issues. 

Many have recently fallen victim to the famous ‘AI hallucinations’ – what starts as a promise of a huge benefit for the brand quickly turns into a massive failure, sometimes putting an entire brand and its customers at risk. 

With so many AI tools available right now, there is also the question of which specific generative AI use cases can deliver tangible business value. 

Navigating GenAI’s hallucinations, reliability and security

Let’s look at a very popular use of GenAI by marketers: content creation and optimisation. 

Generative AI is known for its ability to create new content based on the patterns it has learned from vast datasets. 

In order to do that, the technology needs to access the company and its customers’ data, securely feeding it to the GenAI and fine-tuning various parameters so the results obtained from the marketers’ prompts are accurate and reliable.

One of the challenges with this tool is that it can sometimes produce information that seems plausible but is incorrect or nonsensical. This is the phenomenon known as a ‘hallucination’.

Hallucinations occur when the AI generates responses that are not grounded in the data on which it was trained or on any logical basis. 

These can be minor inaccuracies or completely fabricated facts, which can be really problematic as they can lead to misinformation, erroneous decisions and a lack of trust in the AI system.

What is RAG?

Retrieval Augmented Generation (RAG) combines GenAI with detailed, relevant data to deliver accurate, reliable and useful insights. 

By grounding AI responses in a structured knowledge graph and validating them against a comprehensive knowledge model, RAG significantly reduces the chances of hallucinations. 

RAG finds relationships and connections within the data, providing a strong framework for accurate response generation. This leads to more accurate, trustworthy and actionable insights which are crucial for decision-making.

Here are some RAG benefits that marketers should consider as they establish or deepen their AI exploration journey: 

Speed of change

In today’s business environment where brands collect more and more information and data is continually evolving, AI models need regular updates to stay relevant and accurate. RAG’s framework is inherently flexible and model-agnostic – allowing businesses to quickly adapt to these improved models, new releases or changes to the underlying data without significant downtime or reconfiguration.

Improved accuracy and trust

As mentioned earlier, RAG significantly improves the accuracy of AI-generated responses. Customers have reported accuracy rates in the high 90s and even 100 percent. This accuracy builds trust in the AI system as users can see where the AI got its answers and verify the information.

Cost savings

RAG optimises the use of processing power, mostly because the prompts to the AI are smaller and more focused. This reduces the computational load and associated costs which makes it more efficient than traditional AI methods.

Scalability and security

Marketers should prioritise enterprise-grade RAG solutions that are designed to be scalable and secure. They must handle large volumes of data and queries while maintaining strict security protocols to protect sensitive business and customer information. 

Three ways marketers can use RAG-based GenAI

1. Dynamic content publishing

With a RAG-based generative AI, marketers can transform the way they serve content to end users and consumers. As users interact with the brand’s platform, the GenAI captures their prompts and responses, using this information to serve content most relevant to their needs. 

For example, it can dynamically enhance a FAQ page based on the most popular queries or populate web pages with content tailored to user interests. 

This not only improves the user experience but also lowers the barrier to entry for customers, making a brand’s products and services more accessible and appealing, ultimately significantly boosting user engagement and satisfaction.

2. Smarter content authoring

Marketers can transform how they create content by using a generative AI that leverages RAG. As they type, the generative AI can find connections between different parts of their work, enhancing the overall consistency and depth of the content they produce. 

It can pull in more insights and context relevant to a specific topic – enriching the content and making it more informative and engaging. 

This can be particularly useful for technical documentation, blog posts, reports and other forms of written communication where depth and detail are crucial.

3. Enhanced search Q&A and chatbot applications

RAG-based generative AI can significantly enhance the capabilities of search Q&A and chatbot applications. It can retrieve content more relevant to the user’s query about a product or service, improving the user experience and increasing customer satisfaction. 

For instance, if a user asks a complex question about a product, the generative AI can understand the context, retrieve the most relevant information and provide a precise and comprehensive answer. 

This can reduce the time users spend searching for information and increase their trust in your product or service.

Generative AI and more specifically, RAG-based generative AI is poised to revolutionise how marketers interact with data and generate content. It holds great promise for the brands willing to invest in the technology. It can, in the long-term, be a determining factor in a successful AI-driven marketing strategy, versus one that is led by inaccuracies, security issues and inefficiencies.

Philip Miller is the senior product marketing manager and Al strategist at Progress, a leading software company that enables enterprises to accelerate the creation and delivery of strategic business applications.

Also, delve into an extensive guide on the future of AI in marketing.