The AI Strategy For Small, Medium, Large Companies
Learning machines constantly improve by observing their environment. This makes them ideal tools for structured corporate tasks. If companies want to gain initial experience with intelligent machines, they should first set themselves a specific goal. They should then achieve this step by step with a suitable AI strategy.
For beginners, it is suitable, for example, to improve and possibly automate individual processes or work steps. They might also consider minimizing defects in products and services . Or using AI, such as digital assistants or bots, at the interface to customers in service. It is important that they identify early on where the actual benefit of AI lies in their company and what role it should play. In addition, companies must take their employees with them along this process and convince them of the opportunities that AI offers.
But what if the use of artificial intelligence threatens to fail due to a lack of resources? Building, training and deploying AI models takes time and money. But there should not be a lack of necessary data expertise and enough (cleaned) data with which to train the AI. This is where external service providers come into play, who either provide professional help or provide pre-trained learning algorithms as a cloud or platform solution.
AIaaS Or PaaS?
If small, medium-sized or large companies want to integrate artificial intelligence into their business for the first time, they should initially rely on cloud or platform providers who already provide pre-programmed and trained AI. These can then be further trained and customized using your own data.
AI As A Service
AIaaS providers develop complex algorithms and AI models that are intended to make it easier for companies to work and enter the world of artificial intelligence. These AI models run directly on the provider’s platform, which takes care of installation, configuration and interface operation. AIaaS as an automated and semi-automated cloud solution covers most infrastructure issues such as data processing, model training and evaluation, and forecasts. The results of these forecasts can even be linked to the IT infrastructure of your own company via interfaces.
And corporate customers also benefit from the diverse application possibilities of AI, because frequently used external AI services are chatbots – virtual contact persons who can answer many (customer) questions automatically thanks to the structured environment in which they act.
Platform As A Service
If an external AI service is not sufficient, software platforms provide the required scripts and AI algorithms. Such platforms make it possible to develop and train machine learning and deep learning models in a time- and cost-saving manner because they offer pre-developed models that can be optimized for intended tasks. As an example, AI applications via PaaS are used in industry for the preventive maintenance of wearing parts or machines that are sensitive to failure.
Also Read: The Use Of Machine Learning In Production
