Latest posts by Hussain Fakhruddin (see all)
- AI-as-a-Service: All That You Need To Know - October 30, 2018
- Top 12 Trends In Digital Transformation To Watch Out For In 2019 - October 23, 2018
- Machine Learning in 2019: Tracing The Artificial Intelligence Growth Path - October 9, 2018
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 multifunctional 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 overhyped, 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.
What factors are driving up the demand for AIaaS?
Ours is a data-driven environment, and in here, the value of real-time decision-making capabilities can hardly be overemphasised. This, in turn, serves as a key driver of AIaaS solutions. The volume of data obtained from specialised, smart sensors, UAVs and different types of IoT applications is expanding exponentially – and the need of the hour is for improved, intelligent data management, use, accessibility and security. AIaaS is ideal for smarter big data management, as well as for helping computing systems perform specific tasks (with the help of ML modules). Since these services are available as ready-to-use packages from vendors, the development/deployment time is minimised. The fact that AIaaS can be used by practically everyone (thanks to the user-friendly underlying algorithms) also boosts its demand. The growing need for faster GUIs, and customised APIs also acts as an important driver for this market. For cloud providers in particular, and for businesses in general, AIaaS can deliver significant competitive advantages.
Note: The Distributed Machine Learning Toolkit by Microsoft allows users to run multiple ML applications simultaneously. Predictive analytics, speech recognition and translation services are included in the Google Cloud Platform. IBM has its very own Watson Developer Cloud.
Will growth of AIaaS increase the demand for specialist data scientists?
Yes, and in a big way. What’s more – as AIaaS starts to become mainstream, more time and higher budgets will also need to be allocated. Given the heavy investments (maybe not at the start, but certainly in the long-run) involved and the potential benefits, it is only natural that companies will ramp up their search for IT professionals with high expertise and a lot of relevant experience. These data scientists will be responsible for working with different types of customised AI algorithms. Over the years, AI solutions have mostly been used by the largest players – simply because others did not have qualified, adequately trained manpower (and tech generalists were not enough). However, with the proliferation of AIaaS, a new generation of AI data scientists will appear – and companies of all sizes will be able to hire them and take advantage of artificial intelligence/machine learning. Make no mistake – AI is a complex technology, and proper qualified personnel are required to handle it.
Note: Amazon Web Services is still the market leader in the public cloud domain. However, Microsoft Azure is growing the fastest in this sector. Google Cloud and IBM Cloud occupy the third and fourth spots respectively.
Are there any challenges/barriers for AIaaS?
For all its advantages and relative ease of use, there are certain points of concern about AIaaS (like any other new tech service!). Since users have to depend on third-party AI services for the data/results/information required, unforeseen delays can crop up. The greater reliance on external service providers can also pose data security challenges – since quite a lot of business-critical data have to be shared with the third-party vendors. The key here is to ensure that the chosen AIaaS has robust security and data governance standards, to rule out unauthorised access. Once we go beyond the initial cost-advantages of AIaaS (over traditional AI), the chances of expenses going up in the long-run – as the technology gets more refined and more complex – also become apparent. Since the vendors provide AIaaS as a package offering, it is impossible to really understand the internal AI mechanisms – although the data inputs and the expected results are known. As a result, the overall transparency of the AI services gets reduced. Over the next few quarters, the technology will get more advanced, and we can reasonably expect that most of these challenges will be satisfactorily resolved.
Note: Serverless technology is leading the way in cloud service adoption. Container-as-a-service (CaaS) is also fairly popular.
10. How important is it to select the right AIaaS for business?
Let’s just put it this way: if a AI service is implemented without adequate background research, the entire thing can turn counterproductive. At the very outset, a company has to take a stand on whether it at all needs AIaaS solution(s). A thorough comparison between AIaaS platforms and self-coded implementations also needs to be done – to get a fair idea on which option will be more suitable. Users also need to continuously test the AI services, to make sure that they are performing at optimal levels. In any AIaaS, the process of implementing the algorithms is not explained – and that makes thorough AI testing all the more important. In ‘low-level APIs’, there can be glitches in the process pipeline – which need to be identified and removed quickly. As already highlighted above, awareness of the different types of AIaaS, and their respective functions and utilities, is also an absolute must. AIaaS is a vital cog in the digital transformation journey of enterprises – but only if it is chosen and implemented correctly.
Note: According to a research report, nearly 36% of all the expenses on cloud services are wasted. Going forward, the focus has to be on reducing this figure.
11. How about the importance of AIaaS in the public cloud?
A 2018 RightScale report found that, 67% users are set to increase their spendings on cloud services by at least 20% (18% companies have plans to double their cloud expenses). The adoption of AI-as-a-Service is rising across the board in the public cloud – with both AI data practices as well as AI computing capabilities developing continuously. The recent advancements in neural networks and deep learning mechanisms are also instrumental in pulling up the adoption of AIaaS in the public cloud space. Cloud vendor companies are offering ready-to-use APIs which do not require elaborate machine learning models – enhancing the convenience factor. In the public cloud, AI services can broadly be classified under three heads: cognitive computing, conversational artificial intelligence, and custom cognitive computing. The AI data infrastructure, on the other hand, includes RDBMS, Data Lake and NoSQL.
Note: Cutting down on total expenses is the biggest point of concern for cloud users as present. Generating better financial reports and porting more workloads on cloud are also things that are being focused on.
12. AIaaS: The future
In terms of adoption and market share, North America (with a 46% share) is the clear leader in the global AIaaS sector. Europe, with ~28% share, occupies the second position, followed by the Asia-Pacific. There is also a definite ‘gap’ in how the services are being used – since only around 33% of the ‘AI companies’ actually leverage artificial intelligence in any meaningful way. In the next couple of years, more users will ‘understand’ the potentials of AIaaS and the far-reaching scopes of the technology – and the deployments will be more effective. The market for web APIs and cloud APIs is set to witness healthy growth, while the NLP market is also on an upward spiral ($21+ billion by 2025). The markets will continue to grow, and as the technology becomes more nuanced – we are sure to see more interesting use cases for specialised AI services.
More than 60% professional marketing experts feel that artificial intelligence is the most important element in their overall digital strategies. AIaaS makes the technology easily accessible – with users being able to enjoy the benefits at a much lower cost. Of course, to truly generate value and improve ROI figures, AIaaS has to be used smartly (with in-depth research). According to reasonable estimates, AI services can push up productivity by up to 40%.
The AIaaS market will continue to grow stronger in the foreseeable future. It remains to be seen how companies manage to use it as a key differentiator, and stay ahead of the competition.