According to Gartner, the adoption of generative AI will slow down due to concerns about copyright infringement. As generative AI models are trained on large amounts of data, including content from the internet, there is a risk of using other people’s work without explicit permission. This has led to legal action and defensive spending by organizations to protect intellectual property. Gartner also predicts that building large language models from scratch will be costly and complex, leading many enterprises to forgo their efforts. To maximize results, chief data and analytics officers should balance AI ambition with risk tolerance and design open systems to switch models as needed.
By 2028, Gartner predicts that one-third of interactions with generative AI services will utilize action models and autonomous agents for task completion. Autonomous agents have the potential to handle complex tasks, learn from their environment, and improve over time. They can reduce the need for human intervention and impact sectors such as healthcare, education, gaming, and insurance. Clear objective functions are crucial for controlling the behaviors of autonomous agents and delivering value.
According to EY, generative artificial intelligence (GenAI) integration is the top opportunity for tech businesses in 2024. Most companies are still in the early stages of AI maturity, but strategies based on GenAI have the potential to fuel the industry’s resurgence. The report also highlights the utilization of GenAI in front-office and back-office trials and the importance of careful solution design and additional controls.
According to Gartner research, more than half of enterprises that built custom large language models will abandon initiatives due to costs, complexity, and technical debt by 2028. Moving fast in generative AI can lead to high technical debt, but early movers have the advantage of upskilling and acquiring talent. However, generative AI efforts may help in reducing tech debt in the future. Enterprises can use generative AI tools to replace legacy apps, shrinking modernization costs. Balancing AI goals with risk tolerance is crucial, as the competitive advantage of AI solutions is short-lived.
Why do we care?
I’m shocked to learn that reality will slap hype back! Who knew? But in all seriousness, this data is both obvious and insightful. Customers need help finding the right use for this new technology within legal bounds. It has significant potential, and the trick is to make smart first moves and not drag technical debt forward.
I love it all. There are lots of ways to solve this problem, and “move as fast as possible” is not the best one. I’ll also offer that “not moving” is a pretty poor one.