The rising barriers to AI adoption across the Enterprise

Current state of Artificial Intelligence is considered to be the beginning of a new era of computer-based decisions. While the ethical issue is still under discussion, there is no doubt that AI and machine learning are already adopting across the enterprise and attracting enormous interest and investment. But do AI principles of operation are clear to everyone? What are the main development priorities in terms of using AI technology and how to benefit from it? These questions are needed to be answered and the responsibility lies with executive board members, particularly CIOs and IT leaders.

While AI adoption is on the rise, some issues and challenges are still remined. According to Gartner, three main barriers in artificial intelligence implementation exist: qualified workforce, fear of the unknown benefits of AI projects and sufficient and quality data collection to enable the mechanism functioning correctly.

The lack of human resources arises not only among specialists engaged in the development of AI technology, but also among internal employees who perform operational functions and envision how AI should be introduced and adapted to the company’s processes. A way out of the problem serve to upskilling its own employees. However, true upskilling needs not only training courses but also ability to provide instant occasions and incentives to practice newly acquired AI skills. A possibility to train and implement this knowledge in day-to-day activities could help not only to create a digital, AI-ready mindset but also to improve company’s performance. Another crucial activity could be cross-skilling. In order to stimulate employees, look at the problems from a different angle and give tech specialists an opportunity to come up with business solutions they should understand non-tech employee’s “language”.

Even today, AI technologies gain incremental in-house productivity – companies can see significant savings from data collection that was changed from long and monotonous hours of human work to AI extract information. Whether this is the limit of the technology or AI can do much more? The answer is yes, and IT leaders can be aware of thousands of opportunities this technology provide. According to a survey organized by PWC among C-level executives managing risk, fraud and cybersecurity, supporting decision-making and gathering forward-looking intelligence are the good examples of how AI can enhance internal activities. In the nearest future, these opportunities will only grow and not be limited to recommendation engines and advanced modelling methods for business processes, but AI can auto-classify devices on a network to uncover unauthorized entry, perform pattern recognition to identify malicious behaviour in software.

In order to enable the AI mechanism to work effectively, it must be provided with quality data from different sources. Important to make sure that this data come from different departments such as marketing or finance to achieve full integration with broader automation initiatives, data analytics or both.

All in all, AI algorithm enterprise implementation is still at the early stage. A lot of questions and concerns emerge in different levels of society due to unclear understanding what exactly is hidden by AI tools and techniques, therefore it is a right time for IT leaders and CIOs to coordinate each other and discuss strategies for integrating them into companies and society.