Overcoming Obstacles to Gen AI for Content Services

While humans are critical to the success of AI initiatives, they may also present obstacles. In this blog post, we will walk through some of the challenges organizations face when implementing AI for content services.

Trust Issues and Data Quality Concerns

One of the primary obstacles is the basic trust issue surrounding data safety and reliability. Organizations may question whether their data is secure, whether sensitive information could be exposed, and how to verify the accuracy of outputs from large language models. To combat these concerns, it's essential to start with a curated set of high-quality data for the language model, leveraging workspace types or data sources known for their reliability.

Promoting a Mindset of Verification

To foster trust and success, employees must be trained to always verify the outputs and decisions derived from AI models, much like they would scrutinize information from any other source. While AI can provide more information to inform decisions, ultimately, it's crucial to understand the underlying data and maintain a healthy level of skepticism.

Prompt Engineering for Control and Quality

Prompt engineering can be a powerful tool for controlling the output and quality of AI models. By crafting prompts carefully, organizations can request responses based solely on their curated data, rather than external sources, reducing the risk of inaccuracies or unauthorized information leaks.

Mitigating Security Risks

Executive leadership often expresses concerns about the security risks associated with AI applications. To mitigate these risks, organizations should seek assurances that their data is not stored or transmitted insecurely. Additionally, proper information governance practices, such as implementing strict access controls and permissions, are essential to ensure that AI outputs are only accessible to authorized individuals.

Embracing a Balanced Approach

While the potential benefits of AI in content services are compelling, organizations must approach implementation with a balanced mindset. By addressing trust issues, training employees, leveraging prompt engineering, and implementing robust security measures, organizations can overcome obstacles and harness the power of AI responsibly and effectively.

This blog post is based on the transcript of an original AIIM OnAir podcast, recorded on April 22, 2024. Listen to the full episode. AIIM used the Pro version of Anthropic's Claude.ai to convert the transcript to a blog post and then the post was edited by AIIM staff and the author.

Public link

Please use the above public link if you want to share this noodl on another website.