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All you need to know about Artificial Intelligence as a Service (Infographics)

January 29, 2021

11:17 am

Artificial Intelligence is making businesses improve their products and customer experience based on predictive analytics tools. However, it’s a long and complex process. Not all organizations can venture into building the AI process in-house, requiring a huge investment. Here comes Artificial Intelligence as a Service (AIaaS)! AIaaS let you use AI functionalities without developing in-house expertise. InApp’s concise and two-minute read infographic introduces AIaaS.

 

All you need to know about Artificial Intelligence as a Service (Infographics)

 

Artificial Intelligence as a Service | AIaaS

AIaaS or AI-as-a-Service is a third-party offering of AI outsourcing. From $2.3 billion in 2017, the market of AIaaS is expected to reach $77 billion in 2025, with a forecasted CARG of 56.7% in the period 2018-25.

Low-level AIaaS: It focuses on a narrow task and requires expert knowledge for the implementation with the advantage of the creation of innovative solutions for the problems that are unsolved and non-standardized. 

High-level AIaaS: In high-level AI, a machine, instead of focusing on a single problem, applies intelligence to any problem.  Akin to a conscious mind, strong AI can perform any cognitive functions that a human mind can. 

Advantages of AIaaS

Allows a user organization to focus on their core business, instead of investing in and building AI applications 

  1. Keeps costs transparent
  2. Lowers risks of investments
  3. Increases strategic flexibility

Application Areas of AIaaS

  1. Digital Assistance & Bots
  2. Cognitive Computing APIs
  3. Machine Learning Frameworks
  4. Fully-Managed ML Services

Challenges of AIaaS

  1. Overcoming the high expectations of enterprises from AI
  2. Concerns about reduced data security
  3. Lack of talent for implementation and constant maintenance of AIaaS in user organizations
  4. Apprehensions arising as a result of dependency of some operations on the service provider 
  5. Reluctance on the part of organizations to share their data with service providers
  6. Lack of quality data