By Swapnil Mishra - September 06, 2022 4 Mins Read
When it comes to Artificial Intelligence (AI) adoption, there is a growing gap between the haves and the have-nots.
Larger organizations are currently twice as likely to have actively implemented AI into their business processes as smaller companies, who are more likely to be investigating or not pursuing AI at all due to development expense and scalability difficulties.
Due to significant financial repercussions, SMEs [small- to medium-sized enterprises] frequently struggle with the issue of growing their operations. There is a widening difference between those who have adopted artificial intelligence (AI) and those who do not.
In 2022, the adoption rate of AI increased by 4 percentage points globally, reaching about 35%, according to IBM Global AI Adoption Index 2022 report. The study did discover, however, that there has been a considerable increase in the past year in the gap in AI use between larger and smaller businesses.
AI initiatives typically have a long gestation time and high costs as they evolve and mature over months. AI as a service (AIaaS) can help with it. It was developed in response to the rising need for AI, cognitive computing, and widespread use of cloud-based solutions.
The motivation behind the solution is to democratize AI for all.
The COVID-19 epidemic gave rise to AIaaS, which began to gain popularity in 2020. It operates similarly to other “as-a-service” business models. It consists of various delivery models and product offerings, including but not limited to commercially available tools for building, deploying, and scaling AI/ML models, as well as vertical service provisioning options like inference-as-a-service, annotation-as-a-service, and machine learning-as-a-service (MLaaS). It can also be fully outsourced or managed.
Even though the term is a bit hazy, AIaaS does give customers access to the potential of AI/ML without them having to write a line of code or possess specialized technical knowledge. For businesses that have not yet realized the full potential of AI integration into their operations, leveraging AI as a service-based model may be the best course of action. Through plug-and-play procedures, AIaaS enables SMEs to quickly use pre-trained models at a low cost. Bots and digital assistants, cognitive computing APIs, machine learning frameworks, and data labeling are a few areas where AIaaS is assisting businesses.
SMEs benefit greatly from AIaaS, however, the service is not without flaws. Companies need to be aware of potential problems brought on by biased algorithms, inaccurate data insights, and the “black box” nature of AI.
An AI-as-a-service paradigm could expand quickly if it is not controlled. In the end, businesses can discover that they need more intricate adjustments and solutions, which can be more expensive and call for hiring and training specialized staff. AI systems can only learn from the data that has been provided to them when it comes to mistakes in data insights.
Finally, AIaaS frequently lacks any level of explicability. Even while a solution may frequently produce accurate results, it is unable to explain how it came to that particular conclusion.
The “strange and tedious” operational tasks that can be automated using AIaaS and do not involve private and secret data, such as data labeling and classification, or bots and digital help, are the finest SME use cases for the technology. Companies should do in-depth background checks on the AIaaS supplier to ensure that company data is secure.
Additionally, businesses need to develop usage, access, and security protocols. Organizations should evaluate the type of data that should be stored or shared by the system, as well as the extent to which it can be used or not used. They must also decide on the policies and guidelines for staff entering and leaving the platform. This information must be precisely documented and distributed to all organizational stakeholders.
Swapnil Mishra is a Business News Reporter with OnDot Media. She is a journalism graduate with 5+ years of experience in journalism and mass communication. Previously Swapnil has worked with media outlets like NewsX, MSN, and News24.
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