Artificial intelligence is finding applications in more and more business use cases across business verticals. The main reason for this popularity is due to the positive return on investment for early adopters of AI.
AI has been helping organizations make better decisions and reducing the manual workload of employees. Even though machine learning and AI bring a lot of advantages to the table, there are lot of issues organizations face while trying to adopt AI. Having an idea of the issues faced could help in anticipating and solving hurdles in the AI adoption better.
Cold start issue
Data is the fuel for machine learning algorithms. The more data there is, the better your AI algorithms performs. Your machine learning algorithms might not perform great with lesser volumes of data. Since most of the data generated today is unstructured, the format of data you have in hand could dictate the machine learning approach you are going to take, the hardware you are going to rent and also the AI talent you are going to hire. You can choose to to enrich your data from external sources through processes such as crawling or publicly available data or through manual human effort.
Quality of the data plays an important role in building good machine learning models. Many machine learning experts spend significant portions of their time in cleansing the data. A regular influx of new data can help in constantly evolving over time and beat other models. In a recent survey it was found that 51% machine learning experts said that improving the quality of data was the biggest bottleneck in running successful AI/machine learning projects.
Implementing end to end AI projects requires deep technical expertise. Finding and retaining talent who have expertise in the area of machine learning related to your business use case might be difficult.
AI and Machine learning experts who can get things done are hard to find and expensive. Building a machine learning project from scratch would require software engineers, product managers, ML / AI experts, statisticians and machines which could consume a sizable chunk of your budget. Here is an estimate of the average salary of a machine learning engineer in the United states according to Glassdoor.com.
Lack of labeled data
A majority of the data generated today is unstructured. In a recent survey, it was found that data scientists spend 51% of their time on collecting, labeling and organizing data. For data labeling, you can use your internal resources, certain publicly available labeled data sets and external data labeling services such as Skyl.ai.
Ability to Interpret predictions
Many machine learning models come with up predictions but fail to explain why a certain prediction was made. This could make adoption difficult in certain business verticals such as banks which could lead such practices to be classified as discriminatory lending. Also, in the medical field it is very important to understand why a particular prediction was made since human lives are at stake.
Most AI solutions are built using a combination of open source tools such as TensorFlow, Spark ML and PyTorch. Such tools are constantly evolving and could bring in new risks and vulnerabilities to the AI implementation process.
Any new AI implementation project would require robust infrastructure that scales as per the needs of the projects, is secure and is cost effective. With a weak infrastructure setup, even the best machine learning models can be a big failure.
Many machine learning projects may not show positive ROI within the first few months of going live. In the long run, most AI approaches have exceeded business expectations.
Integrating any new process into a business process would bring in new issues. For example, using machine learning approaches for lead scoring would require integration to your CRM systems such as Hubspot which might not be readily available.
Visit Skyl.ai to learn more about different machine learning and artificial intelligence projects.