AI as a service to solve your business problems? Guess Again – TechCrunch
SaaS, PaaS – and now AIaaS: Entrepreneurial and forward-thinking companies will attempt to provide customers of all types with plug-and-play solutions based on artificial intelligence for a myriad of business problems.
Industries of all types are adopting out-of-the-box AI solutions. Global AI software revenue – most of which is Artificial Intelligence Software as a Software as a Service (AIaaS) – global revenue, industry experts say, is expected to grow at an astonishing 34.9 annual rate. %, with the market reaching over $ 100 billion by 2025. Sounds like a great idea, but there is one caveat – the ‘one size fits all’ syndrome.
Companies looking to use AI as a differentiation technology to gain business benefits – and not just because it’s what everyone else does – require planning and strategy, which almost always means a solution. personalized.
In the words of Sepp Hochreiter (inventor of LSTM, one of the world’s most famous and successful AI algorithms), “the ideal combination for the fastest time to market and the lowest risk to your business. AI projects is to slowly build a team and use experts too. No one can hire top talent quickly, and worse yet, you can’t even judge the quality when hiring, but you won’t find out until years later.
That’s a far cry from what most out-of-the-box AI services online today offer. The artificial intelligence technology offered by AIaaS comes in two flavors – and the main one is a very basic AI system that claims to provide a ‘one size fits all’ solution for all businesses. The modules offered by AI service providers are intended to be applied, as is, to everything from organizing a reserve to optimizing a customer database to preventing anomalies in production. of a multitude of products.
Several companies claim to provide AIaaS for automated industrial production. Much of the evidence presented by these providers is based on individual case studies, with issues involving limited data sets and limited generic goals. But generic AI solutions will produce generic results.
For example, the process of training algorithms to detect wear would be different for factories that manufacture different products; after all, a shoe is not a smartphone is not a bicycle. So for the “real” AI work – where smart modules managed and changed production in response to environmental and other factors – companies developed customized solutions for their customers.
Many customers who have been “burnt out” by a bad experience with AIaaS will be more reluctant to try it again, finding it a waste of time. And use cases that required heavier AI processing didn’t deliver the results they expected – or promised. Some have even accused cloud companies of deliberately misleading customers into believing that standard AI is a viable solution, when they know full well that it is not. And if a technology doesn’t work often enough, chances are those who could potentially benefit from real AI solutions will give up before they even start.
The goal is to standardize a powerful solution almost immediately and not requiring extensive know-how. The success of AIaaS so far has been to allow researchers to run complex experiments without needing the services of an entire IT team to figure out how to manage the necessary infrastructure.
In the future, AIaaS will hopefully allow people who are not AI experts to use the system to achieve desired results. That said, automated online AI services, even at their current levels, can greatly benefit industrial production, if done right.
Well-designed AI could bring great benefits to the industry. Instead of ditching AI, companies should dig deeper into the AI services they plan to use. Does the solution provide for personalization? What type of support does the service offer? How is the algorithm trained to handle data specific to your use case? These are the questions companies should ask themselves when looking for AI services. Suppliers who can provide substantial answers – and back up their claims with real data on success rates – are the ones companies should be working with.
Like all new developments that improve business activity, AI applications require a high level of expertise. Engineers who work for large cloud companies do indeed have this expertise, which means they could bring much more value to customers by helping them develop custom solutions. Whether this can be done ‘as a service’ needs to be considered, but the current system is not the answer.