Cloud business: The easiest and cheapest way for companies to leverage the best of artificial intelligence

Since the end of 2022, when generative artificial intelligence emerged, increasing attention has been paid to other forms of artificial intelligence, including artificial intelligence as a service, or AI-as-a-Service (AIaaS) models. In discussions about AIaaS, everyone immediately thinks of the animated series ‘The Jetsons’. Although it premiered almost 62 years ago, it has been watched by nearly all generations, from boomers to Generation Z, and perhaps even some members of Generation Alpha. The series showcases the advantages and challenges of using advanced technologies, as well as examples of how people can apply artificial intelligence in their private and business lives. From AI assistants to supercomputers, the Jetson family was definitely ahead of its time. Due to current achievements in artificial intelligence, most companies can access advanced technology and be as efficient as Spacely Space Sprockets, the factory from the animated series that uses AIaaS tools in its operations.

Popular bots

AIaaS allows individuals and companies to experiment with artificial intelligence for various purposes without significant initial investments and with minimal risk, either through one-time payments per use or subscriptions. Artificial intelligence is more accessible than ever, and it is helping many companies with customer service, data analysis, and production automation. It is also important to emphasize that some AIaaS solutions do not require any coding skills, but it all depends on the level of complexity of the application, which varies greatly. Additionally, in most cases, solutions come fully ready for use without formal training.

Companies can utilize various types of AI services depending on operational needs, including software as a service (SaaS) or AI tools for customer support (AI for customer service tools). The most common AIaaS tools currently are bots and virtual assistants that use deep and machine learning as well as natural language processing (NLP) to learn from human interactions. They improve with each interaction, providing a more natural, personalized experience over time. Bots are often used to resolve common customer issues or to find answers to frequently asked questions.

Machine learning and the Internet of Things

Machine learning frameworks, or cloud-based software libraries and tools that allow developers to create custom AI models, are also common. AIaaS providers offer pre-built machine learning frameworks that enable companies to easily train and apply these AI models without spending significant resources on research and development. Examples of such frameworks include Google Cloud AI and Microsoft Azure Machine Learning. The third most common form of AIaaS is the Internet of Things (IoT). This refers to a network of connected devices that share data with each other, and a significant advancement has been brought by artificial intelligence of things (AIoT), which integrates artificial intelligence technology and machine learning capabilities into IoT, analyzing data to recognize patterns, gather operational insights, and detect and resolve issues.

AIoT devices can, with the user’s permission, send relevant information to the cloud to assist in product performance. AIaaS providers can offer predictive services that allow IoT devices to predict when machines and equipment may need maintenance, helping companies avoid costly downtimes. Such services are provided by Google Cloud IoT Core and Microsoft Azure IoT.

Tried in business

How this looks in practice was revealed by the company Martian & Machine.

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Marko Bosiljkov, Martian&Machine

—– The integration of artificial intelligence within Martian & Machine has never been an optional step for us but a necessary and purposeful one. As a venture studio, we are engaged in building startups from scratch, which often puts us in a position where we test many ideas to identify those with the highest potential. In doing so, AI tools fit excellently as they allow us to achieve results faster at various levels, starting from conceptualization and drafting ideas to product realization at the level of research, design, and finally development. Teams decide for themselves which tools they want to include in their processes and how, with speed, efficiency, and simplifying the path to results being the common denominator. As a starting point, all team members have access to a paid Chat GPT Pro Account and, depending on which team they belong to, various other AI tools – explained Marko Bosiljkov, co-founder of Martian & Machine.

Examples of using artificial intelligence within the team are various, he added.

– For instance, at the design level, Midjourney and various other AI tools prove to be excellent in creating visual content for specific requirements. They often shorten our path to visual solutions (images) because we can generate them according to our wishes. This has proven to be a good solution for high-level testing. At the development level, we use it when learning new programming languages, where it helps with ideas, implementation, and optimization. It has also proven to be very good at writing SQL queries or unit tests. Besides the learning level, we use it in debugging as it can very well decipher errors in code and alert us to them, which is a task that often fell to mentors or during some code reviews before AI. With this approach, we encourage the team to make more independent decisions and solve problems in a shorter time while learning – Bosiljkov listed.

How to introduce AI

While listening to others’ experiences in integrating artificial intelligence into their own business, many wonder how to introduce what is offered in the cloud and the best of artificial intelligence while staying alive.

Information security expert at King ICT, Filip Kiseljak, explains that introducing cloud and AI solutions into business requires a thoughtful and gradual approach.

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Filip Kiseljak, KING ICT

—– The first phase is to clearly identify the specific challenges that need to be addressed and set goals to maximize the value derived from these technologies. In this context, careful preparation and planning play a key role. A detailed analysis of the market offerings and evaluation of AI and cloud solutions are the foundation for successful selection. It is important to understand the specifics of different platforms, their advantages and disadvantages, and how they fit into business needs. Given the constantly changing trends in cloud and AI technologies, it is essential to always stay updated with the latest developments before making a final decision – Kiseljak said, adding that setting realistic, short-term goals is crucial in the initial phase, focusing on optimizing key business processes.

This process, Kiseljak claims, provides adaptability to the business environment, allowing for active responses to changes and gradual expansion of the application of cloud and AI technologies as the business develops. Of course, using cloud and AI technologies in business opens up the risk of unauthorized access to data, emphasizes the expert from KING ICT.

– By carefully selecting cloud service providers and applying strong encryption and access control, this risk is reduced, but a simple user error or incorrect system configuration can expose data to third parties. Measures that need to be implemented to further reduce the possibility of unintentional data exposure include regular training on security practices, automated checks of system configuration correctness, monitoring system activities, and periodic audits of security settings – Kiseljak listed.

In the event of system unavailability caused by failures, technical problems, or external malicious attacks, Kiseljak added, there is a possibility of business interruption. Here, it is crucial to emphasize the importance of implementing disaster recovery plans and procedures.

– With precisely defined procedures and processes for rapid system recovery, regular testing to identify potential weaknesses, and actively maintaining adequate protection of key services, the risk of data loss or business interruption is reduced, ensuring continuity and reliability in operations – Kiseljak concluded.