Many companies feel overwhelmed by the rapid progression of Artificial Intelligence (AI), and it's no wonder. AI companies are introducing eye-popping advances daily, making identifying the START line difficult. Reflecting on my work history, it feels like we are returning to the command line, a full circle that has traveled through a lot of very expensive UI and UX work.
I think Slack saw this long ago and is positioning itself as the universal interface for work and data.
We are moving toward having conversations with our data, allowing humans to function more naturally and freeing them from the need to become "power users" of specific software. As a result, companies will have options to upgrade their technology or layer on AI interfaces because of less user pain.
Despite these advancements, businesses will still need to gather, clean, integrate and normalize their data shrapnels—at least today. Even so, AI tools have already significantly eased this process.
Large language models (LLMs) are next-gen utility companies that provide foundational services that businesses can build upon. However, they've made such explosive advancements in the last year that it's challenging to understand where they will draw their lines. They've already eaten many companies that existed purely to process data across all modalities, e.g., text, images, audio, video and sensors.We like to take the "let's get something working" approach. Start small, focus on the end-to-end and choose the right tools for the job. Particular to AI, the knowable things are a) you need ready access to clean data, b) the tools you choose today may be different in a year, and c) you won't know what exactly to build up-front, but it will clarify in-flight and d) you will never complete this mission.The characteristics of AI's adoption journey point to the marriage of AI and low-code platforms. This approach benefits both enterprises looking to extend their existing stacks and SaaS companies needing to enable clients to infuse their data and workflows with AI technologies.Low-code platforms provide a glide path to integrating advanced technologies and are well-suited to handling the certainty of change. Detailed requirements will not be worth the time and effort spent writing them. With a low-code platform, developers can make rapid adjustments based on what they encounter and the businesses' up-to-the-minute analysis of where their investment will drive value.By integrating low-code platforms into the tech stack, companies can get something working without betting on the company or making substantial financial investments that may or may not deliver a return.
