With artificial intelligence booming, we see more and more AI-powered solutions for business and everyday activities. Unsurprisingly, tech companies see plenty of opportunities in this surging market and offer AI as a service. In this article, we will discuss how things are going in the AIaaS field and how can companies benefit from it. According to Global Market Insights, the artificial intelligence as a service market size was estimated at $9.7 billion as of 2023. It is projected to reach $142.2 billion by 2032. The primary factors driving growth include:
- rising demand for AI capabilities
- cost-effective solutions
- rapid advancements in technology
- expansion of Big Data
Historically, North America has maintained its position as the frontrunner in AI as a service, commanding a significant 32% market share. This dominance is underscored by substantial investments and cutting-edge developments in the Silicon Valley of the United States, coupled with the burgeoning AI scene in Canada. These factors collectively cement North America’s place at the forefront of the global competitive AI landscape. The integration of AIaaS solutions presents its share of challenges and time constraints, particularly in seamlessly incorporating them into existing extensive workflows while addressing security concerns. However, there are industry-leading teams like Uvik that specialize in deploying tailored solutions with advanced cognitive capabilities. From NLP systems and chatbots to facial recognition systems, predictive software solutions, and data visualization systems, Uvik possesses the expertise to enhance business performance significantly, unlocking its full growth potential.
What Is AI as a Service?
Before delving deeper, it’s important to understand the concept of AI as a service. Artificial intelligence as a service (AIaaS) refers to a solution provided by external vendors, enabling businesses to integrate AI-powered tools and functionalities into their operations. AIaaS stands out as a low-risk and cost-efficient model, as it allows businesses to leverage AI capabilities without the need to invest extensively in developing and deploying AI technology independently.
- According to a survey by Forbes, the majority of business proprietors hold a positive outlook regarding the potential benefits of artificial intelligence (AI) for their enterprises: 64% anticipate that AI will enhance customer relationships and boost productivity
- 60% foresee AI playing a pivotal role in driving sales growth
Furthermore, AI is viewed as a valuable asset for:
- enhancing decision-making capabilities (44%)
- reducing response times (53%)
- minimizing errors (48%)
Additionally, businesses expect AI to facilitate cost savings (59%) and streamline operational processes (42%). This means that vendors offering AIaaS have plenty of opportunities to deploy businesses with customized AI-powered solutions.
Types of AI as a Service
AI models can provide insights in real time and in this way assist businesses in decision-making and improve their operational efficiency. That is why they are more and more widely used in IoT devices, autonomous vehicles, and manufacturing. What are the types of AIaaS models deployed in various industries? 
1. Bots and Virtual Assistants This is probably the most frequently met model in our everyday life. The first chatbot was invented in 1966 by computer scientist Joseph Weizenbaum. The software analyzed user input and employed a predefined set of rules to produce a believable response. The developer called the program Eliza, inspired by Eliza Doolittle from the play Pygmalion. Then the works like Jabberwacky by Rollo Carpenter followed in the 1980s. This model was notable for the voice interaction. But AI was the key driver to ‘humanize’ chatbots that are often used as client assistants answering frequently asked questions. This is the way to optimize organizational structure and increase efficiency. Scaling requires more people for contact centers to meet the demand. But those employees should also be trained properly, which requires time and effort. That’s why machines are now quite common assistants to humans and a good opportunity for AI as a service companies. As per Gartner’s findings, chatbots successfully address 58% of return and cancellation inquiries. Yet they effectively handle only 17% of billing disputes. 
2. Application Programming Interface (APIs) Application programming interfaces (APIs) enable various software applications and systems to communicate, interact, and exchange data. Thanks to API e-commerce seamlessly integrates with express delivery services and payment systems. And many other different systems are connected and exchange data for business efficiency. According to the annual 2023 State of the API Report by Postman, 92% of survey participants anticipate that investments in APIs will either increase or remain consistent over the next 12 months, marking a slight uptick from the 89% reported last year. And what’s amazing: around 60% of API experts admit they use generative AI. Nearly 50% of them turn to AI to detect bugs in their code, while about 30% use it to create the code. When questioned about the projects that inspire developers in the upcoming year, the most popular response was the creation of AI-powered applications. The AI as a service companies will have a lot of work to do then. 
3. Machine Learning Frameworks According to Statista, the Machine Learning market is forecasted to reach a market size of US$204.30 billion by 2024. Furthermore, it is anticipated to exhibit an annual growth rate (CAGR 2024-2030) of 17.15%, leading to a market volume of US$528.10 billion by 2030. Due to high demand developers look for ways to simplify the learning process. And here is where Machine Learning (ML) frameworks created by AI as a service companies step in. ML frameworks can be described as libraries used for ‘educating’ new models. All of them should have solid knowledge in many fields to operate efficiently. That is why many tech companies see it as an opportunity to provide frameworks within the AIaaS segment. 
4. Data Labeling Data labeling encompasses an important part of AI model training, involving the assignment of informative labels to raw data such as images, text files, and videos. These labels provide context, enabling machine learning models to learn effectively. And this is what AI as a service providers offer to businesses in different industries. For instance, in computer vision, images may be labeled with bounding boxes or segmented at the pixel level to train models for image categorization and object detection. Similarly, in natural language processing, text sections are identified or tagged with specific labels to generate training datasets for tasks like sentiment analysis and entity recognition. In computer vision, data labeling involves tasks such as classifying images by content or quality type and segmenting images at the pixel level. Natural language processing requires the manual identification of important text sections or tagging of text with specific labels, enabling tasks such as sentiment analysis and entity recognition. In audio processing, sounds are transcribed into written text and then categorized to create structured datasets for tasks like speech recognition and sound classification. No wonder, the data labeling market valued at $2.22 billion in 2022 is projected to grow 28.9% annually from 2023 to 2030. Plenty of opportunities for providers including AI as a service startups. 
5. Data Classification The more AI models appear on the market, the more security concerns arise. Models are trained in ‘greenhouse conditions’ where everything is OK. But in a real commercial environment, they often tend to react poorly to any security threats. And that’s a problem as AI-driven apps are used in banking, financial services and insurance (BFSI), government, healthcare, and retail industries. And there tons of confidential data are stored and processed. So, not surprising that the demand for data classification is growing. It involves organizing data into categories for efficient storage, retrieval, and tracking. By tagging and labeling data, it becomes searchable and trackable, reducing duplication and storage costs while speeding up searches. Additionally, data classification enhances security by ensuring confidentiality and facilitating appropriate security measures based on the type of data being accessed or transferred. According to Imarc’s research, the market valued at $1.5 billion in 2023 will reach $9.1 billion in 2032.
AIaaS Benefits and Challenges
As AI paves its way into modern business and our everyday lives, it’s worth mentioning how all of us can benefit from it. Reducing human errors and automating some repetitive tasks is only a tiny part of what AI models can do. Let’s consider some AI as a service examples. In healthcare, for instance, AI can analyze patient records and make predictions. More complicated systems scan medical images and detect some dangerous changes like tumors. If we take banking AI may be used for various activities starting from customer communication and ending with cybersecurity measures. Chatbots discussed above are banks’ daily assistants in their dialogue with customers. AI-powered tools are also used to make loan-providing decisions. Traditionally banks consider such factors as credit history, scores, and references. But sometimes people do not have a ‘track record’ for financial institutions to provide. And here is where AI tools step in. An AI-powered loan and credit system can analyze customer behavior with limited credit history. Moreover, the system alerts banks to specific behaviors that could raise the risk of default. In essence, these technologies are pivotal in reshaping the future landscape of consumer lending. And AI as a service companies know that. Finally, AI tools help banks mitigate security risks. These technologies enable banks to swiftly identify fraudulent activities, proactively track and address vulnerabilities in their systems, and minimize potential risks associated with cyber threats.. If we take real estate, we can see some more interesting examples. AI-powered smart homes automatically manage lighting, heating, safety, and entertainment systems. This allows, for instance, to save energy and pay less for it. More advanced AI models help investors when choosing real estate for purchase. They make comprehensive data analysis. It may include not only the location and footprint but also the area’s economic perspectives, crime, and other things. They predict the building’s financial performance (and rent) in the future and help investors and real estate agents to make the right choice.
Pros
But all these solutions require significant investment in infrastructure and fighting for talent. That is why AI as a service faces growing demand as they already have niche specialists, expertise, hardware, and software for solving business problems. AIaaS can accelerate the development and deployment of AI-powered solutions. By outsourcing the development and management of AI models to specialized providers, using AI as a service business model companies can reduce time-to-market and focus their internal resources on core business activities.
Cons
Challenges facing the growth of artificial intelligence as a service industry include the significant initial investment required for AI adoption. Additionally, navigating the complex regulatory landscape adds further costs and complexities, posing hurdles for stakeholders seeking clarity and compliance in the market. Also, training an AI model on unreliable, biased, or unethical data may lead to erroneous outcomes and decision-making. AIaaS providers must furnish businesses with tools and services designed to verify datasets’ reliability, ethicality, and impartiality to mitigate such risks. There are also some general concerns businesses express about using AI. According to Forbes, 43% of their respondents are afraid of being highly dependent on AI. Another 35% worry about the technical abilities of using AI. 
Companies That Offer AI as a Service
Like software as a service provider (SaaS), the AIaaS market has its leaders who are pioneers in AI solutions and successfully collaborate with other known corporations. Of course, there are also numerous AI as a service startups. But let us have a quick glance at AI as a service providers who are driving the industry today.
1. Google Cloud AI
A renowned software giant has developed a versatile platform equipped with an AI tool. This tool enables the creation and testing of generative AI models, facilitating the development of Google-quality search applications using customer data. Additionally, the platform supports the creation of multimodal applications capable of responding with text, images, and other media formats. It also allows for the deployment of applications that orchestrate the documentation summarization process, among other functionalities. In summary, Google Cloud AI offers a flexible solution suitable for various industries such as retail, media and entertainment, financial services, and telecommunications. Leveraging this platform can lead to cost reduction and optimization of the value chain. No surprise Google is one of the leading AI as a service companies today.
2. Microsoft Azure AI
Another leading software development company has a contemporary platform. Azure AI services offer developers and organizations the capability to swiftly develop intelligent, state-of-the-art, market-ready, and ethical applications. This AI platform as a service provides out-of-the-box, prebuilt, and customizable APIs and models. They support various applications such as natural language processing for conversations, search, monitoring, translation, speech, vision, and decision-making.
3. IBM Watson
IBM Watson is a data analytics tool that utilizes natural language processing, a technology capable of analyzing human speech for both meaning and syntax. It conducts analytics on extensive datasets to provide answers to human questions in mere seconds. Another AIaaS solution that businesses often use.
4. AWS AI
One of the largest e-commerce tech platforms couldn’t do without AI. AWS pre-trained AI Services offer readily available intelligence for various applications and workflows. These services seamlessly integrate with customers’ applications to tackle typical use cases like personalized recommendations, enhancing client’s contact centers, boosting safety and security measures, and enhancing customer engagement. Leveraging the deep learning technology behind Amazon.com and our ML Services ensures high quality and accuracy through continuously learning APIs. The best part is that using AI Services on AWS doesn’t necessitate any prior machine learning experience.
5. OpenAI
And of course, the creators of the hyped ChatGPT are worth mentioning. Apart from its highly popular chatbot the company has also such products as DALL-E creating images from text descriptions, Sora (text-to-video), Whisper, with multilingual speech recognition and translation, Muse Net and Jukebox for music creation and some others.
Takeaway
In this article, we tried to answer the question: what is AI as a service? As we can see the demand for AI technologies will be growing while more and more companies will create customized models for their business needs. At the same time, AI integration into a company’s operational activity can be tricky because it requires expertise, infrastructure, and high costs. This is why AI as a service can be a good alternative option for those who are not ready to fight for talent and allocate budgets for buying hardware and training models. If you need experts who can be your guides in the dynamic world of AI, Uvik is the right choice. The company has experience and experts who can help you find the right solution that aligns perfectly with your business needs. 
