AI and Machine Learning

Artificial Intelligence (AI) has entered a new phase over the past few years. Computer power has increased considerably. The advent of the cloud means that data storage and computing power are available without restriction.

Because of this, AI algorithms can now be used in a practical way. AI is now an integral part of our lives, from voice-controlled assistants to facial recognition. Companies are also keen to implement AI. But studies have shown that the use of AI tends to stagnate in many companies. Let’s have a look at the main reasons for this.

Three AI challenges organizations face

Many publications have been written about the challenges posed by AI. Three of these challenges seem to feature in almost every publication:

• The quality of the data is insufficient. To properly use AI, you need data. But for AI applications to work, both the quality and the quantity of data needs to be sufficient. Quality means the data has to be complete, up-to-date, consistent and well-structured. Many AI projects also fail simply because there is a lack of data to make a proper test set. Another problem that occurs is that data is not classified well. Millions of well-classified data points are needed to attain a high degree of accuracy.

• Data is not available to those who need it. Many data scientists spend the majority of their time just unlocking data sources, so they can be transported and formatted. Only on completion of this process they can start working on AI algorithms. This is a time-consuming process that increases the chance of mistakes occurring. It also means the talents of the data scientist are not used to the full.
• Pilots run too long and yield too little. Many AI initiatives start with pilots or small experiments. This pilot is the start of the search for AI applications that can help a company get ahead. Many companies start by formulating hypotheses and spend months collecting the right data to prove or disprove a hypothesis. This can be a quick process, but it usually isn’t and it often doesn’t yield many results. Companies tend to reinvent the wheel. It’s possible that a similar company has already established the necessary AI factors to get the desired result.

Is there another way?

Companies that successfully implement AI have solved the first two issues by using a data strategy. This data strategy is often combined with a ‘cloud-first’ strategy; all relevant data is stored in the cloud. A first step can involve moving existing applications and databases to the cloud. Organisations who are serious about AI tend to store their data in a dedicated data platform that runs in the cloud. This enables them to perform smart analyses and develop AI applications.

More and more companies have an active policy regarding data quality. Laws such as GDPR means companies are obliged to keep a tight grip on the data they use, especially when it comes to personal information. Data can change through time. By applying active data management, you’ll increase the quality of your data. Data-management solutions are available to support you with this.

To prevent your company reinventing the wheel, we advise to explore a solution that has already been proven to work in your industry. Whether you’re in manufacturing, logistics or retail, using a solution and vendor that has already yielded results in your industry, you’ll save time and money.

Our experience around AI

It’s in Luminis’ DNA to embrace up-and-coming technologies. Because of that, we have comprehensive experience with establishing and implementing AI and similar techniques, such as machine learning and deep learning. We help customers in several fields:

• Our data platform InformationGrid enables our customers to set up a central data hub in the cloud. All data can be stored here; active data management will increase its quality
• We’ve developed AI applications for specific industries. For example, a recommendation engine for e-commerce companies
• Do you lack the right expertise or could you use a hand? Luminis has a team of experienced data scientists.