Generative AI has unquestionably emerged as a focal point in boardroom conversations. Ok, yeah, analytical AI solutions have been in development for many, many years. This is true. But the introduction of generative AI (epitomized by models like ChatGPT) has brought about a transformation in how businesses and industries approach problem-solving and innovation.
Generative AI models, with their ability to understand and generate human-like text, have gotten to the point where they're indispensable tools for a multitude of applications, from natural language understanding to deep content generation. Generative AI has disrupted traditional business processes, offering a competitive edge to those who adopt it, impacting fields such as healthcare, finance, marketing and beyond.
As businesses continue to integrate generative AI into their operations, they're not just enhancing efficiencies. They're also advancing the quality of interactions with customers, expanding the potential for personalization, and driving progress towards the long-sought goal of AI systems that can emulate human-level cognitive abilities.
However, concerns regarding trust, compliance, authorization and intellectual property do remain. According to research from Cloudera, more than eight in 10 decision-makers for data strategy and management teams are concerned about sharing data with third parties for the training or fine-tuning of generative AI models, alluding to the perception of a still untamed, Wild West-like environment when it comes to data privacy, security and compliance.
“Wild West” isn’t something I’ve heard used with generative AI, but it does make sense. "Wild West" implies a sense of lawlessness, a lack of well-defined rules and regulations, and a degree of unpredictability. In the realm of generative AI, this underscores the challenges associated with safeguarding sensitive information, especially when it involves sharing data with external entities. And so, decision-makers are acutely aware of the need to strike a balance between harnessing the capabilities of AI models (like ChatGPT) for raw innovation, and respecting the privacy and security concerns of their data stakeholders.
Therefore, organizations are forced to navigate a complex web of legal and ethical considerations when sharing data. One way that Cloudera points out is to maintain full control over data during AI model training, as it helps build trust in the outcomes generated by AI. This control ensures that the data used for training is not only accurate and representative, but also ethically sourced and compliant with regulatory standards. It allows them to protect sensitive information, guard against potential bias or privacy breaches, and oversee the entire data pipeline – from acquisition to transformation and validation.
By exercising this control, organizations can enhance transparency, accountability and the overall quality of AI results, fostering trust among stakeholders (including customers, partners and regulators) in the reliability and integrity of AI-driven decision-making and predictions.
“Organizations are apprehensive about the potential exposure of training models using publicly available data and/or receiving erroneous responses from AI models that have not been trained with relevant enterprise context,” said Abhas Ricky, Chief Strategy Officer at Cloudera. “Our survey results confirm our understanding that data moats are real and organizations who have been successful in creating trusted and secure data sources will have an advantage in producing higher fidelity outputs with generative AI applications."
Be part of the discussion about the latest trends and developments in the Generative AI space at Generative AI Expo, taking place February 13-15, 2024, in Fort Lauderdale, Florida. Generative AI Expo discusses the evolution of GenAI and feature conversations focused on the potential for GenAI across industries and how the technology is already being used to create new opportunities for businesses to improve operations, enhance customer experiences, and create new growth opportunities.
Edited by Alex Passett