Artificial intelligence (AI) has become a significant player in marketing, and it's likely you’ve interacted with tools like Grammarly, ChatGPT, or Google Gemini in your daily tasks. Whether you're actively using these tools or simply aware of them, there's no doubt that AI has made a lasting impact on how we approach digital marketing.
However, despite the clear benefits AI brings, 56% of organisations are concerned about issues related to AI accuracy, particularly around the mis-generation of data or content, as revealed in a 2024 McKinsey report. This concern highlights the risks associated with AI models producing inaccurate or misleading results, which can significantly impact decision-making and trust in AI systems.
Despite the clear benefits AI brings, a significant number of organisations are expressing concerns about the technology. According to a 2024 survey by Deloitte, 56% of organisations are particularly worried about the accuracy and reliability of AI, especially the risk of AI mis-generation—instances where AI produces incorrect or misleading content(Deloitte United States). This concern underscores the need for robust data management and oversight to mitigate the risks of mis-generation and ensure AI systems are both reliable and trustworthy.
One key reason for this worry is the ongoing issue of AI mis-generation—situations where AI generates incorrect or misleading content. In this article, we'll explore what AI mis-generation is, why it occurs, the challenges it presents, and how marketers can continue to leverage AI effectively while mitigating these issues.
What Is AI Mis-Generation?
AI mis-generation refers to instances where AI produces outputs that are factually incorrect or don't align with the given input. This problem spans across multiple types of AI tools, from large language models (LLMs) to machine learning algorithms used in predictive analytics.
Some common examples of AI mis-generation include:
- Incorrect Information: AI systems may create fabricated statistics or content that appears credible but is entirely false. For instance, a content generation tool might produce a blog post containing inaccurate data, which could mislead readers and harm your brand’s credibility.
- Mistaken Identification (False Positives): AI might wrongly identify something that isn’t there. In digital marketing, this could mean a sentiment analysis tool misclassifying a positive customer comment as negative, leading to incorrect actions.
- Missed Opportunities (False Negatives): An AI system may fail to recognise important information. For example, it might overlook a high-performing piece of content, causing missed opportunities for content promotion or amplification.
Why Does AI Mis-Generation Happen?
There are several reasons why AI tools sometimes produce inaccurate results:
- Training Data Issues: AI models are trained on vast amounts of data from across the internet, which can include both accurate and biased information. This makes it challenging for AI to consistently produce correct and unbiased outputs.
- Prediction vs. Truth: AI excels at predicting the next word or data point based on patterns, but it doesn't have an inherent understanding of truth. This can lead to the creation of plausible but inaccurate content.
- Design Limitations: Even with clean data, the way AI models are built can sometimes result in outputs that are not grounded in reality, as the AI is merely pattern-matching rather than understanding.
The Implications of AI Mis-Generation
Mis-generation can have significant consequences for businesses, especially in digital marketing where trust and accuracy are paramount. Here are some of the key risks:
- Erosion of Trust: Incorrect outputs from AI tools can diminish confidence in AI solutions. For example, if your marketing team relies on faulty AI-generated reports or predictions, this can lead to misguided strategies and wasted resources.
- Misinformation: Inaccurate AI content can mislead customers or create confusion, damaging your brand’s reputation. For instance, incorrect personalisation in email campaigns could alienate your audience and reduce engagement.
- Ethical and Legal Risks: In certain industries, such as healthcare or finance, inaccurate AI outputs can have serious legal and ethical ramifications. Failing to properly vet AI-generated content can lead to data privacy breaches or the dissemination of false claims, both of which carry legal consequences.
How Can Marketers Address AI Mis-Generation?
While it’s unrealistic to expect AI-generated content to be flawless, there are steps that both AI developers and end users can take to mitigate the risks of mis-generation.
On the developer side, improving data quality, refining model training, and grounding AI systems in verifiable sources of information are critical. Grounding ensures that AI-generated content is based on factual data rather than simply predicting what "sounds right."
For marketers and other end users, there are practical approaches to use AI tools in a responsible and efficient way:
- Double-check AI outputs: Always verify the information generated by AI, especially when it comes to data or claims that aren’t common knowledge. Avoid relying on AI-generated content for research-heavy tasks, and substantiate any facts with reliable sources.
- Leverage AI for brainstorming: Use AI to generate ideas or explore different angles on a topic rather than treating its outputs as final content. This way, AI can support your creative process without the risk of factual errors.
- Optimise editing and outlining: AI can help you structure content or improve grammar, but always include a human touch for final edits. By focusing on editing or expanding AI-generated drafts, you can maintain accuracy while gaining efficiency.
- Monitor AI in reporting: Using AI to organise and analyse marketing data can save time, but it’s crucial to ensure the conclusions it presents are accurate before acting on them.
Conclusion
AI mis-generation is a reality for marketers who rely on AI tools, but it doesn’t have to be a dealbreaker. By understanding the limitations of AI and ensuring human oversight, marketers can continue to benefit from the speed and efficiency AI offers while minimising the risks associated with mis-generation.
The key takeaway? AI is a powerful tool, but it is no substitute for human expertise. By combining AI with thoughtful strategies and careful monitoring, marketers can harness AI’s potential without falling victim to its occasional inaccuracies.
Written by Glenn Miller
An exceptionally experienced digital marketer, proactive and future-forward thought leader, I deliver exceptional customer experiences, industry leading digital strategy and superior marketing results.
