The entire tech industry has embraced the “AI” label in the past few months, but how real are the offerings in the marketplace today, and who will reap the benefits of these AI functions and capabilities in many of the tech tools we all use?

AI, ML, LLM and related terms have been emerging in many different areas of tech for the past few years. At Oracle for Research, we funded a lot of AI projects – including use of AI to triage accident victims based on X ray images of long bone fractures, use of ML to interpret three dimensional posture analysis based on the inputs from a smart watch (trained on exercise videos on YouTube), AI assisted molecular modeling for drug screening; and a project for which I was proud to be a co-author on a conference presentation using AI to map agricultural land use in Nigeria from satellite photos. In fact, we sponsored so many AI and ML workloads that I had a weekly meeting with the GPU team to determine where in the world was best to run these workloads to minimize impacts on paying customers.

It’s clear that the impacts of AI and ML in many enterprise systems will be large and I see Microsoft, Apple, Oracle, Google, and others making enormous investments to add these capabilities to consumer and enterprise products. This afternoon I was able to take a photo of a plant in my garden, and the ML integration with the iPhone camera was able to tell me immediately what the pant was and gave me a set of informational links on how best to care for it.

I’ve been using ChatGPT for help on scripting and coding too – it’s great at suggesting R and Bash prompts based on what I have already done – and then I can test whether it’s correct in RStudio immediately. The success rate is not 100%, but it’s pretty good – and more efficient (although probably not as good for my learning) than the countless google searches for suggestions I would have otherwise used.

Realistically, though, how is AI going to impact most of the businesses and organizations that I have spent the past 20 years working with around the world? AI and ML might transform how things are done in Palo Alto, Seattle, Austin, and Cambridge but are they really going to make a big difference for that international steel distributor I worked with? The one that had 35 different ERP systems with no shared data model, data dictionary, or documented processes (and yet was still a billion dollar company). Or the truck parts manufacturer in Indiana with facilities in five countries who didn’t use cloud resources because they weren’t sure if it was a fad? How about the US Federal department that oversees a substantial part of the GDP of the nation – where their managers vaguely waved their arms about “AI” transforming their (non-documented) processes. How, I asked, were they going to train models when they didn’t actually collect data on processes and performance today?

I don’t mean to be a downer, and I think the capabilities of AI and ML can, and will, transform many aspects of our lives but I do worry that most of the people who are the technology’s biggest advocates have no idea how exactly the vast majority of their users (organizations and end-users) work day to day. Most companies and organizations in North America, Europe, and APAC haven’t even mastered and deployed search yet. Employees spend substantial parts of their work weeks looking for things that exist – and many of the largest tech firms are in this situation, not just mom and pop businesses.

The process of transforming most organizations and enterprises around the world to data driven practices – which will then provide data that can be used to train models – is still underway and has been for many years. The general purpose LLMs will be great for fettling language in press releases, and the pattern matching models will be great for sorting and tagging my photos, but true, transformative change to the way that organizations work based on AI insights tailored to their specific needs and trained on their data will be much further away.