AIaaS: Moving from Nice-to-have to a Business Necessity

In the last decade, artificial intelligence moved from an obscure sci-fi theme to a crucial element of business life. With the deep learning processes gaining traction, the performance of AI algorithms significantly improved. Now, leaders of digital transformation argue that 2020 can be the year that AI-as-a-service models take off.
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AIaaS: Moving from Nice-to-have to a Business Necessity

In the last decade, artificial intelligence moved from an obscure sci-fi theme to a crucial element of business life. With the deep learning processes gaining traction, the performance of AI algorithms significantly improved. Now, leaders of digital transformation argue that 2020 can be the year that AI-as-a-service models take off.

AI as a service means receiving artificial intelligence algorithms and know-how from a third-party provider to improve efficiencies, gain better customer insights, and eliminate friction in the production process. Commercial companies who do not prefer to build well-rounded data scientist teams tend to contract vendors who have already invested in talents and technology to develop sufficient AI services.  The need to incorporate AI into business models is increasing due to three primary reasons: 1. Improving efficiencies is less costly and more sustainable with AI technology, 2. Early adopters of AIaaS will have a significant time and resource advantage in the medium run, 3. Data will be used to power the future of business optimization, and more data means better AI algorithms.

Seeing the gap in the market, AIaaS start-ups make new technologies accessible across the spectrum of businesses. The sectors that benefit from AI varies from textile to education. All business models have at least one efficiency problem which can be solved with the right AI algorithm.

As the importance of AIaaS increases, the theories around it also deepens. Unbelievable Machine Company, lead big-data solution provided, sites that there are two kinds of AI: low level and high level.

High-level AI services solve well-researched and apparent problems. They tend to have a more straightforward interface, which makes them easily understood by non-AI experts. An example of a high-level AI service is face recognition.  High-level AI services are easier to solve only by a third-party provider and will be widely accessible across sectors by 2023.

On the other hand, low-level AI services require a long processing pipeline and well-rounded AI knowledge. These algorithms can be applied to the multiplicity of different tasks. Logistic regression is an example of low-level AI. AIaaS providers need a technical team to be ready on the receiver side to manage the processes without friction.

With AIaas start-ups like our umbrella firm Orientis, commercial companies can start benefiting from the power of high-level AI without having to produce the expertise to manage it. 2020 is the year that “AI as a service” shift from “nice to have” to a necessity in the commercial ecosystem. The International Data Corporation predicts that 75% of commercial enterprises will use AI by 2021. As Algomedicus and Orientis, we are thrilled to stand prepared for the best to come.  As we always say, whether digital transformation positively affects societies or not solely depends on making the latest technologies’ reachable and affordable.  In the case of AI, reachability and affordability mean increasing the compatibility of technologies and standardization of data sets.