As-a-service offerings are consistently showing growth and adoption popularity as organizations embrace cloud solutions to ready their digital infrastructures for the future.
Artificial Intelligence-as-a-Service (AIaaS) is an off-the-shelf AI service offering that helps organizations implement AI tools and technologies to overcome the costs and complexities of developing AI solutions in-house while still reaping the full benefits of using AI.
AI has often been a challenging landscape for organizations to traverse. They have had to deal with the complications of setting up AI solutions themselves, which often causes a complete overhaul of infrastructures and the need to hire, train, or upskill talent.
However, AI cloud service offerings are very accessible for organizations. Businesses can rely on third-party companies to build, monitor, and assist with implementing AI solutions into their organizations so that a company can concentrate on core developments.
AI-as-a-Service Use Cases and Examples
As organizations are continuously amassing large amounts of data amidst ongoing digital transformation, AIaaS presents an opportunity for those businesses to implement and scale AI and take advantage of all that analytics offers. Moving into 2022, the state of AIaaS is only growing as more organizations explore new AI advancements.
APIs
AI APIs are software applications that businesses can use from third-party service providers. For example, conversational AI is a way for organizations to create conversational experiences that replicate human conversations using digital technologies. This helps to engage customers and assist sales and service representatives. Organizations can personalize customer interactions, bring consistency to consumer omnichannel experiences, enhance self-service capabilities, and improve predictive sales leads.
Machine Learning (ML) Services
Businesses can use pre-built data model templates that are customizable so that machine learning model development complexity is reduced. This helps data science professionals build AI models using intuitive interfaces and tools.
Data Classification
Organizations are collecting a lot of data, and they need ways to classify this data, which improves data organization, accessibility, searchability, and retrievability. For example, organizations might need their media metadata automatically tagged, using AI-as-a-service offerings to enhance data classification.
Benefits of AIaaS
AI-as-a-service brings a lot of flexibility and agility to organizations, advancing infrastructure development at a rapid pace. Companies are looking to move beyond traditional infrastructure so that they can have more control over what they adopt and how they pay for it. AIaaS helps organizations accelerate AI and analytics deployments without the risks of buying complex technologies and instead opt for leveraging the investments of other technology companies.
Moving into 2022, organizations are adopting AIaaS because of the following benefits:
- Reducing the costs and investments of building AI services in-house
- Lower dependency and costs for IT infrastructure overhauls
- Not needing as much technical support from employees and reducing the need to hire new talent
- Data science and business professionals can focus on industry-specific use case development
- Improves data management
- Organizations can implement advanced analytics
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AIaaS has a Bright Future
The state of AI-as-a-service in will prove worth watching as more organizations are going to transition to digital environments, embracing and implementing ongoing AI initiatives. AIaaS helps organizations prepare for an AI future, giving them access to the AI tools and capabilities in a flexible and scalable cloud environment.
AIaaS is going to help businesses harness technologies like natural language processing, machine learning, or even deep learning capabilities.
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