AI peaked in 2022 with the release of OpenAI's ChatGPT and Dall-E 2. These tools and their offspring, like Google's Bard, enable the regular production of high-caliber AI-generated content. The foundation for each product is a language machine-learning model refined on enormous training data. To fine-tune and train the generative AI model for your specific use case, you must hire a generative AI consulting company. Now, users are using AI models to complete various content creation tasks involving text, image, video, audio, and synthetic data. However, the technology's potential benefits come at a cost. Let’s understand the challenges associated with Generative AI.

Challenges of Generative AI

Technical Debt
If enterprises cannot create meaningful change by deploying generative AI, it may become technical debt attached to old systems. When an organization uses AI models for customer service, it can claim optimization success because human agents will handle fewer cases. To justify the significant investment in AI, a corporation must be prepared to make substantial changes, such as reducing the number of agents in front-line support functions. Strategic resource management is required to avoid technical debt.

Compatibility with Legacy Applications
Generative AI integration with legacy setups creates new challenges for businesses. IT leaders must choose between replacing outdated systems and integrating newer ones. For instance, Pablo Alejo, a partner at consultancy West Monroe, stated that financial institutions thinking about using a language model to detect fraud will likely discover that the new technology conflicts with how its present systems function. Older systems "have a precise way of doing that, and now you've got generative AI leveraging different types of thinking. Organizations must find new ways to create integrations or adopt new capabilities with new technologies that enable them to reach the same outputs or outcomes faster and more effectively.

AI Hallucinations
AI models reduce the expense of creating content. This benefits companies, but it also benefits threat actors, who can more readily alter current content to produce deep fakes. Digitally modified media can hyperpersonalize and closely resemble the original. This includes everything from voice and video impersonation to fake art and targeted attacks. Threat actors can abuse generative AI systems, but the models themselves have the potential to mislead users: artificial intelligence hallucinations can fabricate "facts" and spread false information.

Algorithmic Bias
Emerging technologies can run into intellectual property issues, opening companies to lawsuits. Generative AI models have introduced the risk of seeking training data at a massive scale without considering the creator's approval, which could lead to copyright issues. Another significant legal risk is algorithmic bias. When generative AI models are trained on inaccurate, missing, or non-representative data, the entire system becomes biased. This unchecked bias can seep into the systems and influence decision-makers who rely on the findings, potentially leading to prejudiced outcomes.

Also read Generative AI in Customer Experience

Conclusion

Newer technologies always attract forward-thinking business owners. Generative AI is a promising technology and can not be overlooked. Businesses need the right strategy to integrate Gen AI effectively. An experienced Generative AI consulting company can also help establish a center of excellence for modern businesses. They can help by ensuring the successful spread, setup, and scalability of Gen AI solutions across a whole business and uplift your business to stay ahead in the competitive business environment.