The most important phase, and the first step, is to show the c-levels how using artificial intelligence can help each company’s business unit.
Artificial intelligence has revolutionized the world since its arrival, establishing itself as a new horizon on companies to focus their efforts. Much progress has been made in this area from a research point of view, but few companies have incorporated AI throughout their value chain.
For this reason, the multinational Stratasys, a technological hub between Europe and America, make a recommendation of the five key points to incorporate AI into the value chain of a company effectively:
Incorporate Artificial Intelligence into the DNA of the company: The most important phase, and the first step, is to show c-levels how AI can help each business unit. It must be understood that AI is “cross” to all areas that make up an organization and can support expert decision-making, offering up to 10 times more efficiency while raising productivity by up to 40%. Today, the trend of leading companies is to create an AI department to significantly increase productivity and, consequently, their income and margins.
Quality data: Large volumes of data can be found within a company using the best technologies to manage them. However, creating effective AI models will not be feasible if they lack quality. The data quality must be managed jointly between Business, Architecture and Data Scientists. The Business teams must take the leading voice as the most knowledgeable of the data and the problems to be solved. Models learn from what they are taught; if the data is low quality, the AI models will be too.
Development methodology: One of the most important phases of creating an AI department is to have a solid AI model development methodology. This phase should include a multidisciplinary team of business people, data engineers, technology architects and data scientists; while coordinating through the “business translator”. The latter is the cornerstone for understanding the problem from a business point of view and translating it into the language of AI technical teams. In addition, the “business translator” must understand the results of the models created and translate them into the company’s KPIs.
Artificial Intelligence Platforms: Today, AI platforms are needed to help us streamline, manage and automate models effectively and efficiently. The selected platform must be able to democratize AI to those areas of the company where there are no data scientists. The platform must provide low-code functionalities so that business experts can create their own AI models with only the supervision of data scientists. This can lead to making two to three times as many AI models across the enterprise. The idea behind this democratization is that a new culture permeates organizations by applying these new techniques, generating a deep differentiation from the competition.
Explanatory Artificial Intelligence: A fundamental part of creating AI models is understanding what the model has done and how it makes decisions. Furthermore, understanding what decisions the model provides in outliers helps business experts and data scientists understand the integrity of the model when making predictions. Currently, regulations are being issued in various sectors, for example, the financial industry, to use only those models that can explain the predictions. Therefore, creating explanatory models should be on the table from the conception of the use case.
