Artificial Intelligence is a hot space right now, and I read lots of articles and marketing messaging about cool new AI products. The problem is that AI is a massive field that has been evolving for decades, so telling me your product uses AI doesn't actually tell me anything specific.
For consumer applications it may be enough to tell your customers they're buying an AI-powered product, and ride the associated hype curve. But if you're selling to enterprise it probably won't be enough. They'll want to really understand what they're getting: are they buying random forests, machine learning or neural networks?
When you read pitch decks, articles or marketing you'll see a lot of the acronyms and jargon in the space used inter-changeably, when they don't actually mean the same thing. A particular favourite seems to be confusing Machine Learning and Deep Learning. If you use the wrong terms or inconsistent terms in pitch decks or press releases you run the risk of both investors and customers assuming you don't really know what you're talking about. Never a good thing!
The issue for consumers is that they are being told that they should embrace artificial intelligence – and machine learning – as part of the solutions they buy, but vendors are too often communicating those two concepts as equivalent terms, and sometimes those terms are misrepresented.