From tool to system – APIs, integration and manager selection

In the previous post in this AI series, I wrote about The five A's and why many organizations stop using AI earlier than they think. This post builds on the same framework, and takes a closer look at what distinguishes simple AI use from more mature and integrated use.

A term many have heard of, but few put into a strategic context, is API - Application Programming Interface. In the curriculum book Generative AI for Business, an API is described simply as a bridge that allows systems to talk to each other. It sounds technical, but the consequences are primarily organizational.

As long as AI is used as a stand-alone tool, value creation occurs mainly at the individual level. You use ChatGPT, Copilot or similar, experience quick benefits and get a feeling of being "up and running". However, the figures show a clear paradox: Millions use generative AI, while only a small proportion of businesses have integrated the technology into their core processes. At the same time, studies estimate that around 95% of AI pilots have not produced a measurable financial gain, and can be interpreted as AI being overhyped. I think that is a fallacy.

This pattern is well documented in analyzes from, among others, MIT, Gartner and Forrester: AI only creates value when it moves from experiments to operations. A lack of return is rarely about weak technology, but about the organization not being equipped to use it. Blue. Itera points out that many organizations stop at Access or Assistants level.

Only when AI is connected via APIs to the company's systems, data and processes does a real shift take place - from using AI to building with AI. From the Application level upwards this is absolutely crucial. Without integrations, there will be no real automation, and no scalable value either.

When I look back on my experiences with IoT, sensor technology, automation and robotics, this is very recognizable. The value was never in the technology in isolation, but in how data was connected to systems, decisions and work processes.

In an age where technology works, scales and becomes increasingly affordable, it is not the tools that separate businesses from each other, but the ability to set good priorities, take ownership of decisions and translate AI into lasting value creation. This is where the difference between AI AWARE and AI READY becomes clear.