The next wave of technological disruption is here, and it is called Artificial Intelligence (AI). Whether or not AI has yet gone mainstream remains a matter of debate. But the potentially transformative technology is increasingly being discussed in boardroom meetings worldwide. And, it is fairly evident that the vision of an AI-driven enterprise is far too compelling for chief executives to let go and simply adopt a wait-and-watch attitude.
From bots that are proving to be capable of servicing customer queries effectively to automated back-office processes that completely eliminate the need for manual intervention, AI is slowly transforming enterprises inside and out. Projected to deliver transformative business value, AI could very well become the most disruptive of all promising technologies in recent times. It could also usher in unprecedented growth opportunities for early adopters who are willing to venture into the hitherto unknown. However, in spite of all the excitement surrounding its many emerging and envisioned applications, most organizations remain clueless about how to deploy AI and become an AI-driven enterprise.
While consumer applications such as self-driving cars and digital personal assistants attract all the press, enterprise AI continues to be pushed under the carpet. One possible reason for this could be the complexity associated with data. Most organizations are still in the process of discovering the value of their data as digitalization efforts gather steam. With data being the core of AI, organizations will definitely need to switch gears to better manage data before they embrace AI to its full potential. Once that is accomplished, using machine learning, data can be converted into insights that the AI engines will use to drive intelligent automation comparable to human actions. This attribute, along with various other elements of AI including cognitive computing, natural language processing and advanced analytics, can then become the principal lever for an organization looking to use AI to take a quantum leap in their operations.
Consequently, the first step enterprises must take to become AI-driven, is to uncover and unlock the value of their data. This exercise should include both capturing the right data as well as transforming it. From a state where it is primarily designed for human use – perhaps with gaps or inaccuracies that humans can easily deal with – data should be transformed to a state where it can be interpreted by machines effectively. Hence, there is a pressing need for establishing robust data management practices, a capable data management infrastructure, and most importantly, a high level of data governance. Ensuring this will pave the way for organizations to cash in on the AI momentum by either deploying specific products or using “Intelligence as a service". They will then be able to replace time-consuming and error-prone human work with AI-powered superior intelligent automation. And that will be an unlimited canvas on which numerous other use cases of AI-driven enterprise services can be further drawn.
The enterprise AI adoption journey is marked by many milestones. The first critical milestone is to capture, process and turn voluminous amounts of data into business insights with ease. Next up will be the automation of most routine processes, backed by intelligent systems that can handle exceptions as they arise. The third significant milestone will be automated processes continuously focusing on self-learning to adapt to changing business environments. Finally, AI-driven business efficiency should become the foundation of all enterprise decisions and actions.
Contrary to popular belief though, the humans in an AI-driven enterprise become much more valuable as they start focusing on creativity and strategy to run the business, rather than worry about performing mundane tasks. But organizations would be wise to remember that deploying AI itself is not the end goal, it is the benefits that it can deliver to the business that hold the key.
Contact Us Today
Finding Hidden Patterns and Correlations Innovative technologies such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are transforming the way we approach data analytics. AI, ML and NLP are categorized under the umbrella term of “cognitive analytics,” which is an approach that leverages human-like computer intelligence to identify hidden patterns and correlations in data.Explore
The Rise in Big Data Analytics According to Internet World Stats, global internet usage increased by 1,339.6% between 2000-2021. With nearly thirteen times as many people using the internet, this has resulted in a massive increase in the amount of data being processed daily. Our increased sharing and consumption of digital media also compounds this increased usage to create an enormous pool of data for big data analytics firms to process.Explore
What Is an SQL Query Engine? SQL query engine architecture was designed to allow users to query a variety of data sources within a single query. While early SQL-based query engines such as Apache Hive allowed analysts to cut through the clutter of analytical data, they found running SQL analytics on multi-petabyte data warehouses to be a time-intensive process that was difficult to visualize and hard to scale.Explore
The Cloud is the Key to Transformation Success… Transitioning your applications to the cloud is undeniably a critical factor to a successful digital transformation endeavor. It’s more than just a lift-and-shift, however. Let’s explore several things that you need to consider before migrating your applications to the cloud, including: Readiness of your application portfolio Where to begin – the right business case and migration strategy Technology requirements and considerationsExplore
Application Modernization at Speed and Scale Enterprises are pursuing greater application scalability, cost efficiency, and standardization with containerization and virtualization platforms. So, what’s the difference? Containers are a type of virtualization technology that allows users to run multiple operating systems inside a single instance of an OS. They are lightweight and portable, making them ideal for running applications across different platforms.Explore