AI is driving a technological revolution more profound than the internet revolution. Yet, at a time of extreme transformation and extraordinary opportunity, many businesses are hesitating to act. As the gap between AI’s transformative potential and its real-world implementation widens, the stakes for businesses grow higher. This disconnect demands urgent attention and understanding.
This open letter delves into the issue. What is holding businesses back? Is this the result of informed decision-making? And what role can the legal sector play in helping businesses break through this inertia, make informed business decisions, and seize the opportunities of the AI age?
Understanding the Generative AI Revolution in Context
For those old enough to remember the Internet revolution, the release of ChatGPT in late 2022 was akin to when the open Internet became readily accessible through America Online’s home dial-up services in 1993. The internet revolutionized digital communication: it enabled information to be transmitted near-instantly at low cost between any machines connected to the network. The internet democratized access to information—transforming how business could be conducted, and life as we knew it.
In the mid-1990s, forward-thinking businesses recognized that creating a website wasn’t just about having a digital presence—it was about pioneering a new frontier of customer engagement, operational efficiency, and market competitiveness. Companies that learned how to harness the power of the internet to create new value and efficiencies, and reimagined their business models accordingly, became the leaders of their industries, and the new data-driven economy. Today, we stand at a similar inflection point with generative AI. But the technology is different.
Generative AI is revolutionizing information processing: using digital neural networks loosely modeled after the brain, it enables digital reason and understanding. The generative AI revolution is democratizing access to intelligence—transforming how work is performed, the roles of knowledge workers in business operations, and the very nature of business in ways we’re only beginning to imagine.
The effects will clearly be profound. Within the lingo of economists, AI is a “general-purpose technology,” akin to the steam engine, electricity, and the internet: it has wide-ranging potential use across industries, can be continuously improved over time, and enables further innovation. The flexibility, broad utility, and power of this technology have a twofold effect. First, it offers an enormous opportunity for competitive advantage to companies that learn to harness it effectively. Organizations that excel in implementing AI to streamline tasks, enhance services, and create new value, will enjoy cumulative advantages in efficiency, innovation, and strategic market positioning, eclipsing those who don’t.
However, the second effect is that implementing AI is not straightforward. Because it is so versatile and powerful, the optimal way to integrate AI into existing workflows is not obvious and requires considerable effort. Large language models from companies like OpenAI, Anthropic, and Meta are general-purpose tools in themselves, useful for innumerable tasks and easily integrated into “compound AI systems.” Yet, a business cannot determine how to effectively leverage these tools in the abstract. It takes work.
The AI Implementation Challenge
The analog to creating a website in 1993, in the context of the generative AI revolution, is not training a custom AI model, but rather, creating custom implementations: integrating AI into your own business process to accelerate, improve, or automate discrete tasks that previously required human intelligence or simply weren’t possible. AI implementation is an iterative process unique to each organization, demanding work and experimentation. Companies must invest time and resources into understanding how AI can integrate into their existing systems and processes to achieve objectives unique to each business. The path is uncharted; to pull ahead, companies must experiment.
Ultimately, the winners in the AI-driven economy will be those who not only (i) most effectively implement AI to automate tasks (reducing costs below what is possible with human labor), better serve their customers, and (ii) adapt their business models to harness these advantages, but perhaps most importantly, (iii) create a lasting competitive advantage by harnessing the value of proprietary data from across their organization, and use it to improve their AI systems and offerings. While the capabilities of AI models and the methods of leveraging them will rapidly evolve—making AI implementation both easier and more powerful than before—the ability to extract unique insights from proprietary data sets will remain a key differentiator.
For this reason, as more AI-mature companies already recognize, strategic data governance is a cornerstone of AI-driven success. Companies that can collect, manage, and leverage their data effectively will be best positioned to exploit the full potential of AI technologies, staying ahead in an increasingly AI-driven business landscape where the quality and uniqueness of data-driven insights drive innovation and market leadership.
In short, like the days of the early internet—if not more so—this is a time of tremendous business opportunity and formidable challenge. Yet, despite all that is at stake, many businesses find themselves paralyzed at this critical juncture, failing to recognize that inaction itself is a high-risk strategy in the face of such transformative change.
In Part II of this letter, we examine why many companies are hesitating to take action and explore how the legal sector can better help them navigate these challenges.
