Artificial Intelligence (AI) can help your organization to work more efficiently at lower costs, as well as enable you to provide better services. Self-learning systems take over human tasks, rapidly analyze big amounts of data, discover trends, and make predictions that give businesses a head start. But what is possible and not possible to do with AI for your organization? Based on three important topics, we listed six facts and myths about AI.
Competitive advantage with AI
Fact: Companies gain competitive edge with AI
If you apply AI in the correct way, you can get a head start of your competitors. There are benefits to achieve in three key areas: risk reduction, speed, and sales. With risk reduction, you could think of accurate detection of deviations in the production line. Speed is about operational efficiency or spotting trends in the market, but also about a short response time to problems or quickly answering customer questions. With sales you could think of following up on leads more quickly or improving the user experience by means of AI.
Myth: AI is the only way
Although AI offers many opportunities to get a head start on the competition, there is also value to gain from Big Data without AI. For example, you could connect data from different sources and analyze it. By examining client data in combination with purchases, payments, complaints, and online activity, you attain useful insights. You could also enrich your own data with external data, like the weather, traffic, and seasonal patterns. Then you can find correlations between them. With these two activities you learn which aspects play a role in your client’s demand and how to make certain processes more efficiently. You can get ahead of your competition by applying this, even without using AI.
AI and Ethics
Fact: Results from AI-models are often not traceable
AI machine learning models are usually trained with data from the past. The model finds patterns and thus comes up with solutions for the future. Despite the patterns being based on existing data, it is not always traceable how the model comes up with the solutions. Although this is true for most models, there are forms of AI where the result is traceable. For example, the machine learning model Decision Tree makes a smart tree of decisions, which can be printed and easily understood. The model Linear Regression creates a formula, which can be verified by humans. And lastly there is the ‘old-fashioned’ rule-based programming, where the programmer tells the AI model what to do step by step. In these three cases the results of AI models are in fact traceable.
Myth: AI-models do not generate objective outcomes
There are disturbing cases of AI models drawing negative or unwanted conclusions. An example is the application of AI in recruitment: female candidates were viewed as less competent because they were underrepresented in historical data. Those kinds of problems can be prevented by analyzing the data before building an AI model. Data scientists can find out if there is an unwanted bias in the data. Data sets are often prejudiced because societies and working environments are more homogenous than we would like them to be. By acknowledging this beforehand, choices can be made. In the recruitment process, for example, by not letting the AI model scan pictures, gender or other female characteristics.
Besides that, there are various possibilities to create algorithms for subjects in which prejudice cannot get into the dataset. That is because personal characteristics do not play a role in the predictions. An example is a project from BPSOLUTIONS in which waiting times in the hospital emergency department are being predicted. The only patient’s characteristic that is involved in the prediction is their priority of treatment. Besides that, the algorithm looks at the patient’s stage in the process and the room within the emergency department in which the patient stays.
Fact: a lot of data of high quality is needed to apply machine learning
An AI machine learning model gets better when it is trained with more data. Also the quality of the data is important. The better the quality, the better the model performs. After investigating the quality of the data, sometimes the data scientist sees possibilities to improve the quality. For such an improvement step, the data quality needs to reach a minimal level. Rows of insufficient quality are therefore removed from the data set.
Myth: you need to clean all your data before you can use it in a smart way
If you would like to start with AI, it is best to start with a small, delimited test case. Such a test gives an impression of the possibilities of AI for the organization. The test case has impact on the business, but the case itself is delimited and therefore manageable. Verified is which data is needed, how it can be disclosed, and how clean the data is. Only the data necessary for the test case is cleaned. Thereby, the value of AI can be shown and employed, without big data cleaning projects beforehand.
BPSOLUTIONS can help you with the disclosure of data from different sources and with the development of reliable AI models. BPSOLUTIONS also assits with implementing the infrastructure needed for that. Thereby, opportunities emerge to work more efficiently, to improve customer satisfaction, and to stay ahead of the competition.
About Leonie Syrier – Lead Data Scientist
As a strategic advisor, consultant and data scientist I am offering clients perspective on the value of their data. Part of my work is conducting analysis, visualizing and building artificial machine learning models, gaining value out of data.
BPSOLUTIONS helps organizations to organize their Mission Critical IT in a way that makes the organization smarter and can make progress. We do this by, on the one hand, ensuring that IT is and remains up and running and, on the other hand, we ensure with Data Analytics and AI that companies are ready for the future, with which data will really work for your company. In everything we do, we make the world a little smarter.