SaaS, PaaS, and now AIaaS: Forward-thinking entrepreneurs will offer customers all over the world with AI-powered plug-and play solutions to their business problems.
All industries are adopting off-the-shelf AI technologies. Industry experts predict that global AI software revenues, most of which is online artificial intelligence-as-a-service software (AIaaS), will grow at an astonishing annual rate of 34.9%. The market will reach over $100 billion in 2025. Although it sounds great, there are some limitations.
Companies that want to use AI to create business advantages, rather than just do it because everyone else is doing it, need to plan and strategize. This almost always requires a custom solution.
Sepp Hochreiter, the inventor of LSTM (an AI algorithm that is widely recognized and highly successful around the globe), said that slow building a team and using external experts are the best ways to ensure the lowest risk and time to market for your AI projects. You cannot hire the best talent fast. Worse, you will not be able to judge their quality during hiring, but only years later.
This is a far cry of what many online off-the shelf AI services offer today. AIaaS offers two types of artificial intelligence technology. The most basic is the one that promises to offer a single-size-fits all solution for all businesses. The modules offered by AI service providers can be used as-is to organize a stockroom, optimize a customer database, and prevent anomalies in the production of many products.
Many companies claim to offer AIaaS for automated production. These providers rely on case studies to show success. The data is limited in data sets and has limited generic goals. Generic AI solutions will produce generic results, however.
The process of training algorithms to detect wear would differ for different products. After all, a shoe and a smartphone are not the same thing as a bike. For real AI work, where intelligent modules managed and modified production in response to environmental factors and other factors, companies created customized solutions for clients.
Customers who have had bad experiences with AIaaS are likely to be less inclined to use it again. They feel it is a waste and will hesitate to do it again. Even cases that required heavier AI processing didn't deliver the promised results. Some cloud companies have been accused of misleading customers by giving the impression that off the shelf AI is a viable solution. However, they know full well that it is not. If a technology fails to work enough times, those who could benefit from real AI solutions may give up.
Standardizing a solution that works quickly and doesn't require extensive knowledge is the goal. AIaaS has proven to be a great tool for researchers who can run complex experiments without the need to hire an entire IT team to manage the infrastructure.
AIaaS is expected to allow individuals, even those who aren't AI experts, to use the system to achieve the desired results. If done correctly, even the current level of automated AI can still greatly benefit industrial production.
AI can bring great benefits to the industry if it is done correctly. Companies should not abandon AI. Instead, they should take a deeper look at the AI services that they are considering using. Is the solution customizable? What type of support is the service able to provide? How does the algorithm learn to work with your data? These are the key questions companies should ask when looking for AI services. Companies should only work with providers that are able to provide solid answers and support their claims with data about success rates.
AI applications, like all innovations that improve business activity require high levels of expertise. Engineers working for large cloud companies have this expertise, which could allow them to provide more value to customers by helping them create customized solutions. It is important to examine whether this can be done as an optional service, but the current system does not provide the solution.
