After the cloud and big data, artificial intelligence appears to be the next concept in vogue. Some figures clearly illustrate the magnitude of the phenomenon. According to a study by Vanson Bourne in July 2017, 80% of companies are already using some form of artificial intelligence (machine learning or deep learning, see below), while 30% of companies plan to increase their investment in AI in the next 3 years. In addition, 62% of companies plan to appoint an AI Director in the future.
Overall, according to the survey, companies are thinking about the deployment of AI in data transmission, intelligent workflows and decision support processes, as well as in large-scale analytics. The only barriers to a wider implementation of AI in the business, are a lack of IT infrastructure and a shortage of skilled IT staff.
First of all, we must agree on the definitions we use. According to Cigref (the French network of large companies), AI is “the capacity of a functional unit to perform functions generally associated with human intelligence, such as reasoning and learning.” In practice, AI covers two main areas: machine learning and deep learning. According to Cigref, machine learning “combines algorithms that learn from examples and from data.” In other words, machine learning makes predictions based on data sets. Deep learning is all about a technology’s ability to learn from raw data.
AI also includes other technologies, such as symbolic AI, logic programming, and rules engines. Since all of this has to do with learning processes, it is necessary to feed the systems first, before training them in the context of multiple iterations, which requires the use of data scientists.
As a first step, AI targets business areas such as marketing, maintenance, logistics, control, human resources, and customer relations. In other words: all sectors where data volumes are (very) important and where models, trends, and repetitive operations can be identified through analytics and information processing. The evolution consists of moving from (human or automated) programming to learning, with systems that gradually become self-learning. Please note that a human no longer needs to understand a phenomenon or process to teach it to a machine, as it swill seek and find the right solutions.
At the moment, it’s still a dream to think that machines will become really intelligent – according to some sources even more intelligent than man. However, computing power – especially neural computing – can significantly improve the speed and volume of data processing, provided you have quality data, accurate algorithms, and specialized human knowledge. Because in the end, the quality and nature of the data largely determine the technological choices.
Overall, the company will have to choose between either contacting an external partner who will develop a custom algorithm, or investing in existing AI solutions that the company will buy or rent as SaaS (Software as a Service). Similarly, APIs (Application Programming Interfaces) can be developed to access and use data in AI applications automatically and transparently.
AI for all
That said, you don’t need to be called Amazon, Uber, Netflix or Airbnb to embark on an AI track. Data processing and data analysis are not the exclusive domains of data scientists from large organizations. Data storage infrastructure and analysis tools are available for everybody in the cloud. At the same time, harnessing the potential of AI requires not just a strategy, but also budget and time. Thanks to AI, small and medium-sized companies will be able to perform complex tasks more efficiently and at a lower cost. It will allow them to develop new business models, but also to consider new activities – completely new or derived from existing products.
At the end of the day, AI leaves no room for improvisation. AI requires not only technological knowledge, but most and foremost a perfect understanding of the customer’s business. Who else but your trusted IT partner understands your strategy, needs, and goals? Aprico Consultants assists, guides, facilitates, and coordinates your projects to enable faster implementation, generate efficiency and reduce costs.
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