Digital transformation is a very powerful concept in the current business scenario. Amidst all the recent discussions on bringing digital transformation to the center of every business foundation, we are witnessing a new wave of technologies disrupting the world. A majority of the business leaders acknowledge that AI and Machine learning types have a powerful important role as ‘digital twin’ technologies, making it possible for everyone to reach and interact with people and tools of your choice from anywhere, any time.
However, merely thinking of AI and Machine learning as a driving force to bring about digital transformation isn’t going to solve existing problems, if not create new ones. We need to build a sustainable digital transformation strategy with machine learning types. In this article, we have dealt with the various scenarios in the digital transformation journeys that are built and expanded with various types of AI and machine learning capabilities.
Let’s understand these.
Where do AI and machine learning come into the picture of digitization?
Digital transformation is defined as a systematic approach to integrating one or many different types of digital technology platforms into multiple business domains, envisioning a fundamentally transformed business process related to engineering, manufacturing, sales, services, and human resources management. One of the brightest examples of digital transformation in the modern era is the adoption of remote workplace culture, and “Zoom meeting” collaborations, both of which have allowed millions of people to stay connected with their employers, colleagues, customers, and partners through the toughest pandemic months. Businesses that adopt digital transformation strategies are considered more agile and flexible in meeting the modern demands of their customers, in addition to racing ahead of the competition with a superior focus on innovation, engineering, and adaptive synergies across all departments within the organization. In making this leap from traditional business models to agile IT-driven companies, businesses associate themselves with Artificial Intelligence (AI), Machine learning, Automation, Data science, and business intelligence and analytics – all of these converging under the massive infrastructure brought upon by “Digital Transformation”.
Automation Powers Sustainable Digital Transformation
From printing documents to converting IT operations into a virtualized environment, automation has come up as the massive enforcement of transformative journeys.
Machine learning types matter in digital transformation and automation exemplifies it.
The answer is fairly simple – the choice you make with machine learning types to digitally transform the business’s operations allows the stakeholders involved in making long-term strategies and communicating the goals clearly to decision makers, investors, and users. Larger companies that have a focused machine learning roadmap are found to be 50x more agile and adaptive in dealing with challenges that crop up during the digitization journeys. A stronger AI foundation would always deliver better and faster results, but that’s not the only reason why AI leaders back machine learning and data science for building digital transformation infrastructure. It has more to do with automation of back-office tasks, cyber security goal sheets, and above all, employee experience management.
Digital transformation with machine learning: 5 Things you should focus on
A majority of the business leaders agree that their stakeholders are mostly ignorant of the machine learning usefulness in a real sense, though they know some of the other benefits of machine learning types but haven’t seen it work in practical applications.
A classic example is that of virtual chatbots or virtual assistants used in advanced contact centers for customer service departments. Businesses that are using machine learning for their customer service interactions are seen as leaders in the world of sales and marketing relationships. Yet, only 3% of the consumers ever realized that their interaction with a chatbot was actually powered by their own actions and experiences shared with the company! The entire journey is pivoted on the ability of a company to develop and deliver a highly responsive customer service based on Big data analytics, CRM, experience management, and of course, call analytics, which drills down on advanced NLP, Text analytics, speech to text translation, and other techniques. Bot building is the advanced phase of bringing together under one roof, and then pushing it to the user’s interface to sustain digital transformation goals across all contact center points.