Council Post: Navigating The Generative AI Hype Cycle

to decreasing performance on some tasks. Users have complained about weakened logic and more erroneous responses, among other issues.

This trough is natural and occurs with every innovation. Consider the dot-com bust of the early 2000s. Despite that crash, online businesses are now a part of our daily lives. At this Hype Cycle stage, we must avoid adopting GenAI only because it is new and exciting. At the same time, we cannot ignore it because of its present shortcomings. We must thoughtfully evaluate the best way to leverage the technology for maximum value and minimal risk.

Large Language Models hold immense potential for many applications. However, the utility hinges upon the training data. LLMs trained on massive general databases lack the critical nuance required for specialized fields like legal. When generating and redlining contracts, generic LLMs can miss the bigger context and ultimately generate contracts counter to best practices, not applicable to the circumstances or legally unenforceable.

CEOs are excited about AI's potential to enhance efficiency, but we won’t achieve the desired results if the tech creates more work. Toggling between applications adds steps to workflows, and forcing users to transfer work back and forth increases the potential for error. AI solutions should integrate into other applications. Implementing AI where people do their work encourages wider adoption.

The current"generative AI for everyone" algorithms aren’t widely applicable to specialized industries like law. AI creators need input from legal experts to build algorithms for tailored use cases, such as contract drafting. Legal teams require tools trained on domain-specific data that cater to their unique tasks. Achieving this goal requires collaboration.GenAI is here to stay. While the tech still suffers from growing pains, it will eventually hit the Plateau of Productivity.

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