All You Need to Know about Artificial Intelligence as a Service (AIaaS)

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In the past decade or so, Artificial Intelligence (AI) has improved exponentially, and it solved many issues along its way. This has encouraged multiple organizations, financiers, and governments to invest billions of dollars in Artificial Intelligence.

AI is undoubtedly the most emerging, disruptive technology that offers promising opportunities to people relying on technology, and it has become more advanced the innovations in various fields. It’s certainly become an integral part in our lives and continues to permeate.

According to a Gartner survey, 70 percent of respondents wanted AI to perform all the tasks like research work, calculations, simplifying procedures, mistakes change, and solving problems. Additionally, more than 57 percent of people desire AI to perform one or two tasks, while 18 percent of survey respondents want the AI to perform more than 5 tasks.

 


What Is Artificial Intelligence as a Service?

Artificial intelligence as a service (AIaaS) is defined as a service that outsources AI to enable individuals and companies to explore and scale AI techniques at a minimal cost. Artificial intelligence benefits businesses in numerous ways, right from improving customer experiences to automating redundant tasks. However, developing in-house AI-based solutions is a complex process that requires huge capital investment. That’s why businesses are openly embracing Artificial Intelligence as a Service, where third-party providers offer ready-to-use AI services.

Artificial intelligence as a service refers to out-of-box AI services rendered by companies to potential subscribers. AI refers to a paradigm where computer systems perform human-like tasks by reasoning, picking up cues from past experiences, learning, and solving problems. Broadly, disparate technologies such as machine learning (ML), natural language processing (NLP), computer vision, and robotics come under the AI roof.

 

Artificial Intelligence as a Service Types



There are multiple types of AI services currently available in the market, and enterprises have to pick the correct type of Artificial Intelligence as a Service tool. By doing so, you are aware of the multiple types of services in Artificial Technology. Broadly, there are three best types of Artificial Intelligence as a Service solutions, and they are

  •      Bots
  •     APIs (Application Programming Interface)
  •    Machine Learning

Bots

In the current era, regardless of whether you search the web for anything from educational websites to shopping stores, you come across bots known as chatbots. Chatbots are text-or voice-based interfaces that simulate a natural human conversation.

Chatbots are typically interpreted and process the user’s text and provides an instant pre-set answer. They combine both machine learning capabilities and natural language processing (NPL) algorithms to understand human being’s conversation and provides relevant information.

Text-based chatbots are deployed online on social media platforms and websites, while voice-based chatbots are used for sorting customer service and call deflection. They are being increasingly used nowadays and one can witness them in action when you contact a company’s customer care. The chatbot gleans off the required preliminary information and makes it easier for the customer and help desk personnel to understand an issue better.

APIs

An Application Programming Interface (API) is a software intermediary that allows two applications to communicate with each other. APIs permit developers to add a new service to their application without writing the code. We can use APIs for multiple tasks like extracting entries from text, Natural Language Processing, emotion detection, computer vision, Conversational AI, and other tasks.

Machine Learning

ML is a member of the Artificial Intelligence family, and it is evolving fast. Using various programming techniques, enterprises use it to analyze and find patterns in a bulk amount of data, streamline processes, and make predictions. AlaaS makes it simple to adopt machine learning technology for business.

 

Advantages of Artificial Intelligence as a Service

 


Flexibility 

 It’s flexible. Artificial Intelligence as a Service offers customization options, and users can implement AI services to their business needs.

Easy of set up 

 It’s simple to set up, and no complicated installation is required. Users are allowed to access the required AI features directly.

Fee transparency – It’s transparent. You are only liable to pay for the AI tools that you use.

Scalability 

It’s scalable. Companies can check whether Artificial Intelligence as a Service complete the task successfully with profits by starting small projects. If the companies are satisfied, they can scale up or down as per their requirement.

Cost 

Artificial Intelligence as a Service is a cost-effective solution. Cost-saving is the main point for the emergence of Artificial Intelligence as a Service in the IT industry. Organizations don’t have to invest a huge upfront cost, and they need to pay only for the usage

 

Common Challenges of Artificial Intelligence as a Service

 


1. Data Privacy and Security

With the work from anywhere model due to the COVID pandemic, businesses need to be cautious of data usage and security mechanisms.

 There are also critical facets such as data privacy legislation such as GDPR and CCPA and the expiration of the US/EU data privacy shield, which compel businesses to be careful with their data.

 In such scenarios, using privacy-enhancing mechanisms and technologies such as encryption and data masking can help keep enterprise data safe.

2. Vendor Lock-in

Imagine you are using a different API, which uses other response formats. You may think it is easy to switch; however, the various response formats and changing APIs require some effort.

 Moreover, end-to-end ML services or even ML components are harder to switch tools because the developer team needs to familiarize themselves with them. All these facets lead to vendor lock-in, where firms need to understand the pain points of switching between competing products.

 3. Data Governance

It's critical for companies in highly regulated industries to limit data storage in the cloud. Companies in banking and healthcare may face limitations in leveraging AIaaS.

 4. Long-run Costs

On the one hand, Artificial Intelligence as a Service solutions allow businesses to set up quickly at an affordable cost. However, long-run costs could be high, and companies need to weigh both short and long-term costs before making significant Artificial Intelligence as a Service investments.

 5. Efforts For Bug-free Implementation

Yet another concern is implementing the Artificial Intelligence as a Service software, which may not be bug-free. And the implementation requires a lot of effort for a seamless and successful transition. 

Takeaway



Artificial Intelligence as a Service allows companies like Saiwa to exploit state-of-the-art AI, ML, and cognitive solutions without heavy investments into infrastructure, skilled personnel, or maintenance overheads. Instead, it acts as a driving tool to boost add-on functionalities into existing products and services. Most service providers promise to lend high-quality services with minimal efforts from the subscriber’s end. Artificial Intelligence as a Service may completely not replace the existing task force, but it will enable organizations to zero in on business-centered functions.

 With Artificial Intelligence as a Service, small firms can collaborate with state-of-the-art AI platforms to deploy cognitive functionalities for wider customer reach. However, businesses adopting Artificial Intelligence as a Service also need to cross-check a few details before they dive in. Questions related to data residence, data protection regulations, and others need to be answered, as it can affect your business. All in all, organizations need to perform due diligence with utmost care to avoid adverse business impacts.

 

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