How Is AI-as-a-Service (AIaaS) Changing the Way We Look at Data? | Robotics

From self-driving cars to Apple’s Siri virtual assistant, artificial intelligence (AI) is transforming the world. Now, AI-as-a-service () is bringing a new wave of data-driven innovations to businesses and consumers. To better understand why this is the case, let’s take a look at , its applications, and how it is changing the way people look at data.

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What Is AIaaS?

AIaaS drives real-time decision-making. It empowers end users with fast, efficient data analysis and insights. As a result, AIaaS can help users quickly process massive amounts of structured and unstructured data and use it to make informed decisions.

Perhaps a good way to think of AIaaS is in the same terms as infrastructure-as-a-service (IaaS), platform-as-a-service (PaaS), and other “as-a-service” offerings. Like these offerings, AIaaS is a cloud-based service supported by a third-party vendor. It allows users to experiment with AI technologies and test machine learning algorithms on cloud platforms.

BMC notes AIaaS can be used in a variety of ways, including:

– Chat Bots: Use natural language processing (NLP) algorithms to learn from human beings, imitate speech patterns, and answer questions. AIaaS enables companies to deploy chat bots to assist customers, freeing up employees to focus on other revenue-generating activities.

– Cognitive Computing Application Programming Interfaces (APIs): APIs allow developers to add a technology or service to an application without building code from scratch. Meanwhile, AIaaS allows developers to launch APIs for NLP, knowledge mapping, computer speech and vision, and much more.

– Machine Learning Frameworks and Services: Developers use machine learning frameworks to build machine learning models. These frameworks may ultimately lead to AIaaS-based machine learning services that allow developers to quickly add machine learning templates, models, and tools to their apps.

Today, there are four primary AIaaS vendors, according to BMC. They are:

– Amazon Web Services (AWS)

– Microsoft Azure

– Google Cloud

– IBM Cloud

Oracle, BMC, and other globally recognized cloud services providers are prioritizing AIaaS, too. At the same time, the rising global demand for AI-based services and solutions could lead startups and large corporations to introduce unique AIaaS offerings in the near future, industry analyst ReportBuyer predicts.

What Are the Benefits and Drawbacks of AIaaS?

AIaaS provides many of the same benefits as other as-a-service offerings, BMC points out. These benefits include:

– Reduced Costs: Small and medium-sized businesses rarely have the funds available to invest in AI systems. Thanks to AIaaS, companies – regardless of size – can integrate AI into their operations without the implementation, maintenance, and training costs commonly associated with AI systems. Plus, AIaaS is generally billed on an as-needed basis; this means a company is charged only when it uses AIaaS.

– Seamless Implementation: Although many businesses want to integrate AI into their day-to-day operations, doing so may prove to be difficult. AI technologies often require substantial time and resources to build and implement from the ground-up. And once these technologies are deployed, they offer no guarantees, either. On the other hand, AIaaS provides AI out of the box. It enables a company to incorporate AI technologies into its everyday operations without a time-consuming and expensive implementation process.

– Unparalleled Scalability: In some instances, a company may deploy AI systems across its operations and later discover that these systems fail to meet its business needs. Comparatively, AIaaS provides unmatched scalability in contrast to traditional AI systems. AIaaS can be gradually incorporated into a company’s everyday operations. As a business becomes accustomed to AIaaS, the company can instantly scale its AIaaS usage up or down as needed.

Like any technology, AIaaS is not perfect. BMC indicates that some of the drawbacks of AIaaS include:

– Limited Security: To leverage AIaaS, a company must share its sensitive data with a third-party vendor. If an AIaaS provider fails to properly secure a company’s data, it could expose the business, its customers, and its employees to data leakage and breaches. And if a company’s data falls into the wrong hands, the business may suffer brand reputation damage, revenue losses, and other immediate and long-lasting problems.

– Downtime and Outage Concerns: A business that develops and launches in-house AI systems has full control over these systems – without exception. Conversely, an AIaaS vendor owns and operates the AI systems that its customers use. If an AIaaS vendor experiences system downtime or outages, its customers will suffer the consequences.

Compliance Issues: AIaaS relies on cloud computing, but cloud data security mandates may prohibit certain companies from using this technology. For instance, businesses in highly regulated industries like healthcare and financial services are required to comply with various cloud data security mandates. Additionally, some countries have established regulations that stipulate whether or how companies can store data in the cloud.

When it comes to AIaaS, a company should weigh its pros and cons closely. If a business performs a comprehensive analysis, it may be better equipped than ever before to determine if now is the right time to invest in AIaaS.

Tips for Using AIaaS

For businesses that want to streamline data management and analytics, gain a competitive advantage over rivals, and find new ways to drive revenue growth, AIaaS may be ideal. In fact, TechRepublic points out there are lots of things a business can do to optimize the value of AIaaS, including:

– Establish realistic expectations. AIaaS may help a small business morph into an industry leader, but it is important to remember that this transformation is unlikely to happen overnight. If a company takes a slow, steady approach to AIaaS implementation, the business can use AIaaS to drive gradual improvements.

– Measure your results. Merely deploying AIaaS is insufficient. By integrating AIaaS into a business strategy, a company can study the link between AIaaS and assorted key performance indicators (KPIs) and plan accordingly.

– Teach your employees about AIaaS. A company should educate its employees about AIaaS, how it works, and its benefits. By doing so, this company can show its workers how to use AIaaS to collect and analyze data and drive meaningful business improvements.

AIaaS is a business disruptor that promises to have far-flung effects on companies and consumers in the years to come. As more companies search for ways to capitalize on AI technologies for data-driven decision-making, AIaaS could become exceedingly valuable to global businesses across all industries.

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