The AI-as-a-Service Revolution: 3 Ways It Will Help Your Business

3 minute read

In this post, we’ll examine the business benefits of an AI-as-a-Service (AIaaS) implementation model. We’ll break down how AIaaS works and why it provides ideal flexibility for organizations looking to rapidly scale their efforts.

The Software-as-a-Service (SaaS) model forever changed how companies and consumers access computer programs. Rather than install and own one version of a company’s product, users were able to download and license software, while relinquishing update and maintenance responsibilities to the software vendor. Another simple yet equally crucial aspect of the SaaS model is the immediacy it allows users to access and run new applications. Why send away for program CDs, or call a vendor to send an install expert, when software can be accessed online, so companies can get to work right away?

Businesses are beginning to ask themselves similar questions about accessing AI. As companies continue to have success with AI implementations, user adoption is increasing. The faster users can get up and running, the sooner they can start to achieve return-on-investment (ROI). As a result, pivoting from a vendor-led implementation model to an as-a-service model is increasingly on enterprises’ radar.

In this post, we’ll examine several business benefits of an AI-as-a-Service (AIaaS) implementation model. We’ll break down how AIaaS works and why it provides ideal flexibility for organizations looking to rapidly scale their efforts and achieve faster ROI.

(Almost) Instant Gratification

With AIaaS, implementing automation tools can be as easy as downloading an email marketing application. You will be able to log onto a marketplace to find the right AI system for your business needs. You’ll be able to “interview” the software before making an investment, and if the system meets your standards, you can deploy the solution in a relatively short period of time.

This process gives you the freedom and flexibility to adopt, experiment and adapt AI deployments as you see fit. Unlike traditional deployments, which usually require large teams, installation experts, tons of hardware and more, AIaaS is a more user-friendly, nimble approach to automation that allows companies to adopt AI in a digestible way. This is particularly helpful when a company wants to deploy AI to address one or a few specific use cases in the near term to measure performance and effectiveness, and to help build internal support for wider and more ambitious AI deployments in the long term.

An Alternative to Vendor-Led Deployments

One of the best parts about the flexible adoption afforded by AIaaS is the ability to break free of vendor-led or vendor-dependent installations. Companies can certainly opt for a Do-it-Yourself approach working hand-in-hand with an AI vendor, depending on the AI use case, industry, regulatory and security requirements, project scope and size, etc. Working with vendors also allows businesses exposure to AI-related talent and expertise that they may otherwise not be able to access. However, as an alternative, AIaaS in ideal circumstances can cut down (or virtually eliminate) the time a company needs to spend with a software vendor’s sales agents, legal team and development personnel to deploy AI within their IT ecosystem. The customer is in charge of when, where, and for what use cases that AI is deployed — usually at a per unit cost (following potentially a one-time installation fee) for cost effectiveness.

Deployment Simplicity

Perhaps the best part about AIaaS is the simplicity with which it can be delivered. AI solutions delivered in an as-a-service model will be increasingly capable of guiding users through their own installations, pointing them in the right direction in order to guarantee speed and reliability. More advanced solutions will be able to handle back-end technical specifics on their own, with APIs into cloud-based systems that will require little or limited integration through human intervention.

Overall, AIaaS has the potential to significantly lower the barriers to entry for AI deployments —especially for companies that previously believed that such technology was out of reach due to a lack of technical skills — so they can make business better, more efficient and profitable.

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