By Apoorva Kasam - May 30, 2023 5 Mins Read
Generative AI offers real-world capabilities with promising advancements. However, generative AI models have a B-side- dangers of misuse and technical complexities.
Many businesses believe that generative AI is advantageous; however, with the evolving landscape, companies should also focus on the challenges and misconceptions of generative AI. Here are a few challenges and misconceptions of generative AI that businesses must consider.
Generative AI models consist of various parameters that simplify a complex task.
While these models are unsuitable for training for most organizations, the essential computing resources make this technology expensive and ecologically unfriendly.
It necessities businesses to adopt generative AI via cloud APIs with minimal tuning.
Integrating generative AI into legacy systems or conventional systems could raise issues. Hence IT leaders must carefully evaluate whether they want to merge or replace legacy systems. Legacy systems have a specific operating method compared to new generative AI models.
Therefore, businesses must find ways to initiate integration or adopt new capabilities with new technologies. They must ensure that the decisions must not affect the outcomes and the efficiency of the tasks.
Generative AI could combine with legacy systems as technical debt when businesses fail to achieve a significant change during its adoption. Companies deploying AI models for customer support may result in successful optimization because human agents will handle other pressing cases.
Hence, firms must significantly minimize the number of employees in the frontline support to account for AI investments. Moreover, if the businesses won’t diminish the traditional processes, it raises a question about the optimization efforts. It only adds debt to the operations.
Generative AI reshapes processes within the organization. While AI minimizes the number of agents in customer support, businesses would require employees to assess and enhance AI-assisted tasks.
AI models minimize the cost of content creation that helps businesses; however, it allows cyber-attackers to augment the existing content to establish deep fakes. Digitally altered media resembles the original content and can be hyper-personalized- video and voice impersonation.
While threat actors misuse generative AI systems, the models can mislead the users by offering misinformation and falsely created facts- AI hallucination. The hallucination rates depend on AI tools’ responses.
Generative AI technology can collide with intellectual property issues. These models have contributed to the risks of seeking training data at a large scale without considering the creator’s approval. It might lead to copyright issues.
Another legal risk is algorithmic bias. It happens when the generative models undergo faulty and incomplete data training. These results are then systemically prejudiced. Unassessed AI bias outspreads via the system and influences the decision-makers resulting in discrimination. These factors elevate downstream preference in the datasets leading to a single point of failure.
It is a misconception that a unified large language model (LLM) or other generative AI model types could define all use cases. Especially for businesses, the generative AI’s nature suggests that companies might require multiple models.
Some models are good at bulleted lists, some at reasoning, and some at summarization. Therefore, businesses have numerous editorial formats of messaging. Companies must consider all these pointers when choosing a suitable model.
Generative AI models incur vast amounts of computing resources. Moreover, the enormous costs required for businesses to build foundational models are just one testimonial of these costs. Therefore, high prices are why utilizing a suitable model is so important. More importantly, businesses must be selective on how much IQ their Model requires per their use cases.
The generative AI’s accuracy and reliability are one of the biggest concerns around the technology. The AI algorithm can provide an answer; however, generative AI models can offer solutions that are not accurate and reliable in some cases.
Enterprises heavily invest in creating reliable data; hence, customers must utilize models and a technology architecture that relies on the factuality of their data. Generative AI models propel this business data needs.
Customers have multiple information sources- finance, legal, and HR; however, not all companies allow open access to all this data. Business leaders dedicate their efforts to building all the information into a vast language model.
It requires the Model to answer all the questions at a global or organizational level. Once the company dedicates its efforts to keep its information factual and private, it must manage who can ask questions and at what level.
A generative AI model gives the impression that it thinks for itself by creating dynamic outputs, analyzing inputs, and making decisions. Businesses must understand that humans construct the framework for AI-based decision-making, set parameters, and prepare the data for information to create new data to enhance the system continuously.
AI enhances the process of data collection and computation; however, it lacks the context of human experience that drives natural intelligence.
Integrating AI’s capabilities for predicting and analyzing data with human skills and insights leads to cost-efficient and timely solutions. The more businesses overcome AI misinformation, the more they can leverage AI practically and profitably. Generative AI algorithms require accurate training of data to complete the tasks.
Businesses must understand that the generative AI models do not offer original content; instead, they combine what they already know. Regarding security, companies must enhance or improve data security training to avoid privacy challenges.
Apoorva Kasam is a Global News Correspondent with OnDot Media. She has done her master's in Bioinformatics and has 18 months of experience in clinical and preclinical data management. She is a content-writing enthusiast, and this is her first stint writing articles on business technology. She specializes in Blockchain, data governance, and supply chain management. Her ideal and digestible writing style displays the current trends, efficiencies, challenges, and relevant mitigation strategies businesses can look forward to. She is looking forward to exploring more technology insights in-depth.
A Peer Knowledge Resource – By the CXO, For the CXO.
Expert inputs on challenges, triumphs and innovative solutions from corporate Movers and Shakers in global Leadership space to add value to business decision making.Media@EnterpriseTalk.com