Stargate – A $500 billion AI joint venture between Oracle, OpenAI and Softbank. The goal is to build the data centers and infrastructure needed to power AI development. Unrelated to this announcement, there are many conversations and posts on AI generation, AI agents, and agent workflows. Many SaaS organizations are launching agent workflows within their product suite. There are many predictions about the role of agents, with some suggesting that agents will soon represent individuals in discussions, leading to scenarios such as – Have your agent talk to my agent and we can discuss after our agents join . AI assistants are in every app and every platform. There are numerous models for consumption in LLM, many open source and many new ones that we have to review every few weeks/days. There is so much excitement and AI in the news, on magazine covers and popular ads from major technology providers.
However, for most of the organizations I engage with, adoption is not widespread. One sees “micro-bursts” of productivity driven primarily by gains from specific, repeatable tasks. But 10 hours per employee per month is not a game changer for anyone. This is not to say that specific organizational roles are not significantly affected. Meta CEO Zuckerberg told podcaster Joe Rogan how the company was looking to replace “mid-level engineers” with AI. Organizations are also realizing that this is expensive. Are the returns commensurate with the investments along the commitments? Finally, hallucinations remain a real danger. Even Apple recently brought back its AI news aggregator.
The lack of widespread adoption at this stage begs the question: Is the adoption of generative AI inevitable in our daily professional and personal lives? Or is this another technology that some roles within an organization will adopt because it has a significant impact, but is unlikely to be widespread? The answer is that we expect large-scale effects from this technology. Throughout history, the combination of different technologies has been crucial to significant advances, and generative AI also follows this pattern.
Technological convergence refers to the integration of two or more unrelated technologies. This convergence can accelerate these unrelated technologies or sometimes merge to form new ones. Some notable examples of this phenomenon include cell phones and the Internet. Mobile phones were originally designed for voice communication, but with the introduction of smartphones, they have evolved into multi-functional devices. One can make calls, send messages, surf the Internet, take pictures, conduct business transactions and more. This convergence has fueled the adoption of mobile phones and accelerated the development and use of Internet services. Another example, on a smaller scale, is smart watches, such as the Apple Watch. You can use your smartwatch to keep track of time, track your activity, track key health parameters, communicate and engage with various apps on a wearable device.
What are the components that support convergence for generative AI?
NEW— The movement of data into the cloud has been continuous and evolving, fueled by technological advances and powerful computing capabilities. This has been happening for a while and it is fitting that we find ourselves in a situation where most organizations are in the mature stages of their journey.
The data—We are getting more data, which is also more accessible. We can consume data in many forms, structured and unstructured. Furthermore, the fact that generative AI can be used to engage with all this data in its current forms is a key motivator for using this technology.
Digital reading—Organizations have increasingly better digital literacy. Further, these foundational models have democratized AI, from data scientists sitting in an ivory tower designing algorithms that the rest of the organization doesn’t understand to putting the power of these models in everyone’s hands. Engaging with ChatGPT does not require any special knowledge of how AI works.
Multimodal—What began as large language models (LLMs) that understand, generate, and manipulate natural language has now evolved into image, audio, and video processing. So they are technically large multimodal models that are more natural to the way we make decisions.
Based on my conversations with leaders across organizations, here is my educated guess on the impact. Generative AI will fully automate 20% of daily tasks, allowing more time for creativity by eliminating the mundane. At the end of the day, we will adopt whatever works for us. It will further increase efficiency and productivity in another 60% of tasks. The impact here can be extensive – 25%-50%. Meanwhile, the remaining 20% of tasks will evolve and still need human supervision, mixing technology with the human touch. All this to say that the issue of widespread adoption may be premature. We need to understand that it is a journey and start with where adoption will bring the most value. Writing code, for example. And as we identify other sources of value, we’ll be sure to design effective and economically viable solutions that work.
While challenges remain, the outlook for the future of generative AI is promising. We are ready to change the way we create, communicate and engage. However, we must balance this innovation with a responsible approach that will allow us to harness the potential of generative AI, creating efficient and fair tools for all users.