Seeing Slow

The arguments in this HBR article are similar to what other like the author who work in large enterprises are saying about the impact of AI. Their perspective is informed by what they see around them and if they have been a product of that ecosystem long enough, it becomes easy to forget what the world outside looked like. More importantly, they are deeply disconnected from that world as it is today. While there is some truth to what he says but it is very far from the whole truth and this distinction is a cautionary one.

The argument that AI revolution will be slow and enterprise adoption will face significant friction goes contrary to consumer-driven adoption which is already happening at a rapid pace. Even my mother knows of ChatGPT and what it is good for. Tools like that and AI-powered copilots have quickly gained widespread use, influencing how people work and setting new expectations for productivity and capability across industries. Startups and smaller companies are leveraging AI to disrupt traditional markets and force larger enterprises to adapt more quickly than the article suggests, indicating that change may not be as sluggish as the author claims. He simply may not have observed it from his vantage point.

He highlights the commoditization and open-source models as reducing competitive advantage, but open-source AI can actually accelerate innovation by lowering barriers to entry and enabling a global ecosystem of developers to build and iterate on new applications. Rather than slowing the revolution, the availability of advanced AI tools at low or no cost may drive faster adoption and democratize access to cutting-edge technology. The cumulative effect of many incremental improvement, across processes, products, and customer experiences can be transformative, even if each individual change seems modest.

The focus on challenges of enterprise integration and downplays broader societal and ethical risks. Critics argue that the most pressing issues surrounding AI are not just about slow adoption or integration, but about the potential for job displacement, bias, misinformation, and loss of privacy. These concerns require urgent attention and could reshape the AI revolution in unexpected ways. The author underestimates both the risks and the potential for rapid, disruptive change, especially as AI continues to evolve outside traditional enterprise channels.