Generative AI can explore many possible designs of an object to find the right or most suitable match. It not only augments and accelerates design in many fields, it also has the potential to invent novel designs or objects that humans may have missed otherwise.
Early foundation models, like ChatGPT, focus on the ability of generative AI to augment creative work. However, by 2025, Gartner expects more than 30 per cent of new drugs and materials to be systematically discovered using generative AI techniques — up from zero today. And that is just one of numerous industry use cases.
With that said, AI innovations are accelerating, creating numerous use cases for generative AI in various industries. Here are four examples of how generative AI will have an impact across industries and add value to enterprises.
No. 1: Generative AI in drug design
A 2010 study showed the average cost of taking a drug from discovery to market was about $1.8 billion, and the discovery process took a whopping three to six years. Generative AI has already been used to design drugs for various uses within months, offering pharma significant opportunities to reduce both the costs and timeline of drug discovery.
No. 2: Generative AI in chip design
Generative AI can use reinforcement learning (a machine learning technique) to optimize component placement in semiconductor chip design (floorplanning), reducing product-development life cycle time from weeks to hours with generative AI.
No. 3: Generative AI in synthetic data
Generative AI is one way of creating synthetic data, which is a class of data that is generated rather than obtained from direct observations of the real world. This ensures the privacy of the original sources of the data that was used to train the model. For example, healthcare data can be artificially generated for research and analysis without revealing the identity of patients whose medical records were used to ensure privacy.
No. 4: Generative design of parts
Generative AI enables industries, including manufacturing, automotive, aerospace and defense, to design parts that are optimized to meet specific goals and constraints, such as performance, materials and manufacturing methods. For example, automakers can use generative design to innovate lighter designs — contributing to their goals of making cars more fuel efficient.
Generative AI enables systems to create high-value artifacts, such as video, narrative, training data and even designs and schematics. There are a number of AI techniques employed for generative AI, but most recently, foundation models have taken the spotlight.
Before you forge full-speed ahead, remember that generative AI doesn’t just present opportunities for business; the threats are real, too — including the potential for deepfakes, copyright issues, and other malicious uses of generative AI technology to target your organization.
Work with security and risk management leaders to proactively mitigate the reputational, counterfeit, fraud and political risks that malicious uses of generative AI present to individuals, organizations and governments.
Finally, consider implementing guidance on the responsible use of generative AI through a curated list of approved vendors and services, prioritizing those that strive to provide transparency on training datasets and appropriate model usage, and/or offer their models in open source.
Brian Burke is Vice President at Gartner, Inc. where he provides thought leadership on emerging and strategic technology trends. Gartner analysts will provide additional analysis on the acceleration of data and analytics at Gartner Data & Analytics Summit, taking place March 20-22 in Orlando, FL.
