AI-as-a-Service: All That You Need To Know

Hussain Fakhruddin,
CEO, Teknowledge Mobile Studio.

In 2008, the worldwide software-as-a-service market was worth only $5.6 billion. Cut to 2020, and that figure is expected to soar to $133 billion – clearly indicating the rapid rise in demand for consumption-based software services (‘a la carte software’, so to speak). Between 2018 and 2020, the total number of SaaS subscriptions are set to jump by nearly 96%. This is, without a shadow of a doubt, one of the fastest growing technology sub-domains at present.

While services like Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) have been in discussion for some time now – the ‘as-a-service’ market is gradually being extended into newer, more cutting-edge, fields. The artificial intelligence-as-a-service (AIaaS) market is a classic example of that. According to estimates, the worldwide AIaaS market will be valued at just a shade under $11 billion by the end of 2023, with the 2017-2023 CAGR hovering around the 49% mark. The biggest of players, like Microsoft, Google, IBM and Amazon, are already heavily active in this field. In today’s discussion, we will take a look at some interesting facets of the growth of AIaaS:

What exactly is AIaaS?

As the name itself suggests, AIaaS refers to off-the-shelf artificial intelligence service offerings that can be bought and implemented immediately. In other words, it can be explained as ‘third party AI service offerings’ as well. Like all other _ -as-a-Service packages, AIaaS also makes use of cloud computing – and can add significant strategic flexibility to the operations of organisations, pulling up efficiency and productivity levels. Since AIaaS solutions are typically dynamic and highly adaptable, they also help in optimising the effectiveness of big data analytics. With these ‘readymade’ AI services, it becomes possible for companies to derive all the key advantages of artificial intelligence – without actually having to make huge investments (and bear the associated risks) for building their very own cloud platforms. The onus, however, lies with company CEOs and IT specialists to understand the precise type of AI service they require, and the potential benefits. AIaaS has multifarious benefits – but it should not be adopted without adequate initial research.

Note: While the popularity of AIaaS is a fairly recent trend, the concept of ‘artificial intelligence’ is far from being a new one. At present, we have vendors that offer multi-functional digital platforms powered by machine learning (apart from general cloud AI service providers).

Will AIaaS emerge as a worthy substitute of human intelligence?

The comparison is an erroneous one to begin with. Contrary to what many think (and indeed, what the concept of AI has meant for years), artificial intelligence is not ONLY about replicating the capabilities and (probably) the cognitive prowess of human beings. Instead, AI should be viewed as an end-to-end technology – which uses various techniques and modules to analyse data better, identify patterns and trends, and calculate the probabilities of different end results (say, for predictive purposes). Broadly speaking, two different types of algorithms – the deep learning (DL) algorithms and the machine learning (ML) algorithms – are used in full-fledged AIaaS services. The prime objective for implementing AI solutions is to enhance the capabilities of existing IT setups, and allow them to ‘learn’ new functionalities (without additional coding having to be done). The entire artificial intelligence vs human intelligence debate is over-hyped, and in most instances, misplaced. The two should ideally complement each other.

Note: The need to collect and securely store big data is going up rapidly for companies. AIaaS makes artificial intelligence tools more accessible – and hence, help a lot in data handling/management requirements.

What are the main types of AIaaS?

For AI to indeed deliver the desired results, enterprises have to select and correctly deploy the ‘right’ type of AIaaS first. Doing so, in turn, requires the IT managers to be aware of the different types of these ‘ready-to-use’ AI services. Broadly, there are 4 different forms of AIaaS: first, there are the customised machine learning (ML) platforms and frameworks, that can create data models and and can ‘read’ patterns from existing data pools. Next up, there are the AI-powered bots – powered by the ever-improving natural language processing, or NLP, capabilities (in fact, chatbots are the most popular use cases of AIaaS). Then, we have the entirely managed ML services – which make use of drag-and-drop tools, cognitive analytics and custom-created data models to generate more values (compared to the general machine learning frameworks). The fourth type of AIaaS includes the third-party APIs (application programming interfaces) – which are built to add extra functionalities to any new/existing application. All that organisations willing to join the digital transformation revolution have to do is identify the type(s) of AIaaS that are likely to boost ROI figures, purchase them from AI vendors, and start implementing them immediately. Small changes, if required, can also be made.

Note: Apart from Microsoft, Amazon and Google, several other companies – like SalesForce and Oracle – are also highly active in the AIaaS space.

How fast is the AIaaS market growing?

As competition rates are increasing and digital technology is getting more and more refined, the AI-as-a-Service sector is growing rapidly (~$11 billion in 2023). From a $4810 million valuation last year, the global market for artificial intelligence will jump to well over $88500 million by the end of 2025. The growing demand among organisations for using cutting-edge machine learning services on the cloud is also pulling up investment figures. A recent report estimated that overall expenses on AI will show a 4X increase between 2017 and 2021 – as different industries start to adopt AIaaS solutions. The biggest advantage of AIaaS is it allows enterprises and workers to focus on their core capabilities/lines of business – without having to worry about model building or cloud network development. Over the next half a decade or so, the growth of AIaaS will further gather momentum – and developers will be increasingly incorporating AI capabilities in both applications and big data systems.

Note: An enterprise-level study found that 8 out of every 10 companies prefer using multi-cloud models. Among them, specialised hybrid cloud services are the most in demand.

Does the AIaaS market have different segments?

The scope of artificial intelligence in general, and AIaaS in particular, is huge. As such, trying to understand everything about the service at one go can be complicated, and in fact, an exercise in futility. For purposes of research clarity – the AIaaS domain is divided in different segments, based on different parameters. According to functionality, there are the ‘managed services’ and the ‘professional services’, while from the technology perspective, we have the DL and ML services on one hand, and high-end NLP capabilities on the other. AIaaS can also be segmented in terms of the software tool(s) that lies at the heart of it – web/cloud APIs, processor tools, data archiving and storage, and others. In terms of usability, AIaaS is finding rapid adoption in different industry verticals – right from retail services, transportation, and banking & finance, to healthcare, manufacturing and telecom services (the impact of AI services on the public sector is also going up gradually). A wide range of customisations are also available, enhancing the usability factor of AIaaS.

Note: In the transportation sector, AI-as-a-Service can be used to make tasks like navigation, finding the fastest routes, and parking, simpler than ever before.

What advantages does AIaaS deliver?

The benefits of deploying AIaaS have a lot in common with the general advantages of any consumption-based (i.e., on-demand) software service. For starters, the seamless scalability is a big factor – since this allows enterprises to start off small, and then increase the scale of AI operations over time (according to project-specific requirements). In a scenario where the need for super-fast graphical user interfaces (GPUs) and parallel machines is going through the roof, AIaaS comes in handy – since it makes it possible for IT managers to implement and use the latest AI-powered infrastructure, without having to be concerned about the lofty expenses. Since AI-as-a-Service is, by definition, ready to use – the challenges posed by the relatively complicated nature of traditional AI solutions are bypassed. Yet another factor in favour of these off-the-shelf AI services is the complete transparency. Users have to pay only to to the extent of their use of the services – instead of arbitrary amounts and high overheads. Smarter AI-powered operations at easily manageable budgets – that’s the key for AIaaS for delivering value to enterprises.

Note: Machine learning plays a mighty important role in facilitating ‘intelligent optimisation’ for different industries.