AI is nothing new, yet we are only scratching the surface. I believe that every Technology company is incorporating broadly-defined “AI” into their products and services offerings at this juncture.
“AI” at its core is based upon statistical models that underpin various advanced automation processes such as machine learning, deep learning, neural networks, natural language processing etc. Google has been an “AI company” since inception. One could argue that SAS Institute with its advanced analytics has been an “AI company” since inception. Every Technology company I can think of incorporates AI into their products, services or internal operations at some level. I wish that media members and institutional investors would refrain from using the term “AI companies”. It is too broad a term to have any real meaning.
Phones: Your iPhone, Google Pixel, Samsung phone and other smart phones are packed with various AI elements from the hardware itself (Apple and Google are increasingly designing more of the AI chip hardware as the chip architecture is a source of competitive differentiation), to the apps that reside on the phone. 5-6 years ago the apps on your phone had to go to the edge of the network to execute various advanced automation functions. Now, those functions can take place on-device, even without an Internet connection. These advances are a result of the advances in chip technology. Throw NVDA into the group along with AAPL, GOOGL, MSFT and AMZN in terms of companies that are advancing their hardware as it relates to AI.
Enterprise Cloud Platforms: Google Cloud Platform (GCP), Oracle Cloud, Microsoft Azure and Amazon’s AWS platform all incorporate core AI services into their respective offerings. Thus, every Technology company built on top of GCP, Azure, AWS or Oracle Cloud has immediate access to core AI services. Add a natural language front end and things get really interesting. We are probably a decade away from having ambient AI (read our ambient AI article HERE).
AI Examples: That customer service chatbot that helps you while banking or processing an online transaction leverages Natural Language Processing – a form of conversational AI. (We used an open source Natural Language Processing platform – Apache OpenNLP – back in 2015 for our insurtech offering “Identity Hub”. This stuff has been in the market for years. It’s nothing new).
Netflix’s recommendation engine and recommendation engines in general have machine learning underpinnings that observe user behavior to predict what movie the user may like to watch next. Predictive Analytics is a form of “AI” and received a ton of hype 2013-2016. Deservedly so as machine learning models underpin every website and observe every bit of online behavior that an individual conducts in real-time.
Every Google app incorporates AI at some level. That’s not an exaggeration. That comment is probably also true of Microsoft. I can’t think of a MSFT application that does not leverage AI. Ditto for Apple.
There’s a good chance that the background landscapes in the video games you play were created with AI (read our related article HERE).
Productivity Gains: I mentioned ambient AI earlier in this article. Voice will be a big AI driver as people use AI platforms such as Google Assistant, Amazon Alexa and Apple’s Siri to help automate day-to-day tasks. Within the Enterprise voice combined with robotic process automation (RPA) can lead to significant productivity gains. We have written at length about RPA. Click the RPA tag below to read RPA-related articles. Ditto for AI, machine learning and more.
Generative AI is another AI animal, but like its cousins, Generative AI is only as good as the data used to train its underlying models (LLMs in the case of Generative AI). Thus, while OpenAI/Microsoft and Google generate lots of buzz in the Generative AI space, I believe that massive value will only be unlocked as these companies acquire or partner with other companies that are vertical-specific. Google is taking the partnership approach. (You may view Google’s AI partners by viewing Google I/O 2023. Read about it HERE). In addition, vertical, data-centric companies (Bloomberg, FactSet, CoStar, Solera etc.), would do well for themselves to incorporate NLP front-ends. I’m not convinced that it’s necessary to partner with OpenAI or Google’s LLMs. We wrote about this point-of-view HERE.