|Firms nowadays are transforming into intelligent enterprises / Photo Credit: Billion Photos (via Shutterstock)|
Companies all over the world are transforming into “intelligent enterprises,” firms that take advantage of disruptive technologies to enhance productivity, revolutionize the customer experience, and digitize their businesses, according to Samsung SDS BrandVoice via business news sites Forbes. These organizations combine disruptive technologies such as 5G, AI, and IoT to bring insights, automation, and innovative process improvements in new and exciting ways.
AI, specifically, is responsible for driving innovation to enable intelligent enterprises. Since network connectivity, deep learning algorithms, and processing power are improving, companies attempt to leverage AI to tap into their ever-increasing amounts of data and formulate innovative solutions. At Samsung, AI is used to predict sales demands accurately and optimize inventory needs.
However, many firms struggle to employ AI in their day-to-day operations. Why? It’s because they spend too much of their resources on the AI-development process, preventing them from collecting, analyzing, and even utilizing advanced analytical models. Another reason is the shortage of AI and data scientists.
AI development is time-consuming, which can take six to nine months. And that time is used to process the data needed to train the model. Companies can reduce this timeline by relying on AI-based analytics platforms to accomplish the most time-consuming stages of the development process, namely data pre-processing, model selection, and model training. On the other hand, the lack of AI talent is a roadblock for companies seeking to be intelligent enterprises. Even if the data scientists in the firm have the right data and are equipped with the domain expertise to provide context to that data, they still lack the expertise to fully extract insights.
Firms can bridge this talent gap by developing citizen data scientists rather than looking for more AI professionals. A citizen data scientist can generate models that utilize advanced diagnostic analytics or predictive and prescriptive capabilities, but their main focus is outside the field of analytics. With the right platform, they can leverage AI-enabled analytical tools without the help of “expensive and time-constrained AI experts.”