Have you ever gazed upon a Monet painting and lost yourself for a time? I have. I love great works of art. The University of London\u2019s research says beautiful art catalyzes an instant release of dopamine into the brain. I feel that jolt of reward and motivation when I see a masterpiece.\n\nAs an artist and an engineer, I find myself curious about the techniques, stories of the paintings, reasons behind the color choices, the style of brushstrokes, and the preservation of each piece. I started studying art to give myself a break from the high-tech world. Now, ironically, the art world is being disrupted by emerging technology\u2013specifically generative AI tools such as OpenAI\u2019s ChatGPT, Google\u2019s Bard, and Meta\u2019s LLaMa. These tools enable me to discover the information I am seeking\u2014and do so in near real-time and in novel, profound ways.\n\nArt, business, and generative AI\n\nArt has existed since the dawn of humankind, giving us a window into history and stories waiting to be revealed. Each masterpiece is more than just the marks on canvas; it\u2019s the culmination of a culture, an artist, and influence. Art is an ideal example of how generative AI can create a massive amount of value by extending the way we look at artwork. \n\nGenerative AI is a way to create augmented information that expands our understanding of an image and the ephemera around it. For instance, if you start with a Monet painting and use an extended generative AI model trained on all Monet artwork and complementary artists, you can create an input dataset that includes images by the artist. The dataset is complemented by a text-based dataset sourced from the entire internet. You can ask generative AI to tell you about the Monet image, and it will generate an essay about Oscar-Claude Monet and that specific painting. Then, you can use the essay to create more information with direct and indirect correlations, such as to another oil painting, related modern impressionist art, or a different Monet image. Generative AI does this based on a knowledge graph, a network of related concepts. \n\nSimply put, generative AI is a powerful way to exponentially expand a small amount of information into a very large understanding, which in turn leads to smarter, more informed decisions.\n\nPowerful potential but less than perfect\n\nGenerative AI is rapidly moving into the business world, across industries and companies of all types and sizes. Companies such as Salesforce, Amazon, The Coca-Cola Company, and Snapchat are making bold moves to integrate generative AI into a host of capabilities. Generative AI has the potential to revolutionize many aspects of our lives, but ethical considerations must be addressed when developing and deploying generative AI models.\n\nEthical considerations must be a fundamental part of the development and deployment of generative AI models to ensure they are used in ways that are fair, safe, and beneficial for society as a whole. For that, generative AI needs explainability.\n\n4 ways to enable explainability in generative AI\n\nCreating explainability in a generative AI model can help build trust in the models and the confidence to develop enterprise-level use cases. Explainability requires careful consideration and planning throughout the entire development process. (You can even ask ChatGPT about this.)\n\nHere are some key guidelines:\n\nMoving ahead\n\nGenerative AI has the potential to transform many aspects of our lives, including the world of art. We already use models to create new works of art, preserve existing works, and expand our understanding of art history. For example, researchers have used AI to restore a painting by Rembrandt that was damaged by fire. The AI model analyzed other works by Rembrandt to generate a new image that closely matched the original painting.\n\nAs generative AI evolves and extends value into more enterprise use cases, IT leaders, technologists, and developers must adopt a holistic approach that considers the technical, ethical, and social aspects of AI explainability and involves all relevant stakeholders in the development and deployment of AI models. By doing so, we can ensure that we use generative AI in ways that benefit society as a whole.\n\nHow we collectively work toward responsible use of generative AI is the story we want future generations to discover when they see the masterpieces we build with this powerful capability.\n\nNicole Reineke is senior vice president of innovation at Iron Mountain. Prior to this role, she was a senior distinguished engineer in the office of the CTO at Dell Technologies. Over the last 20 years, she has founded and led high-tech companies in product executive leadership roles establishing expertise in areas such as sustainability-aware infrastructure, data trust, blockchain, hybrid cloud, artificial intelligence\/machine learning, artificial intelligence ethics, augmented and virtual reality, data center management, and intelligent data management. She has 14 patents, awaits grants on 75 additional filings, and is the co-author of \u201cCompassion-Driven Innovation: 12 Steps for Breakthrough Success.\u201d She is a passionate hobbyist\u2014a pianist, dancer, and artist\u2014who enjoys hiking with her dog and family.