How Machine Learning is Changing the Digital Transformation Landscape
For decades, businesses and economies have strived to unlock the full potential of digital technologies. And now we’re grappling with a new age of transformational technologies - robotics, analytics, artificial intelligence (AI), and machine learning are here to bring progress at an exponential rate.
- The second wave of digital transformation involves empowering machines to perform tasks and make decisions through the use of technologies such as robotics, analytics, artificial intelligence, and machine learning.
- Machine learning can automate tasks, improve decision-making, and optimize processes in industries such as manufacturing, healthcare, and retail.
- To successfully implement machine learning in digital transformation efforts, companies should have a clear strategy, prioritize data governance and security, and invest in upskilling their workforce.
Machines are quickly surpassing human performance in a diverse array of tasks, from manual labor to cognitive activities and even those involving tacit decisions or sensing emotion.
This groundbreaking step change offers unprecedented opportunities for business growth, economic development as well as mass outcomes for society as a whole.
The Second Wave of Digital Transformation
Companies jumped on the first wave of digital transformation when they invested heavily in IT. It revolutionized our lives and businesses by letting us search, shop, and transact through browsers or mobile devices, providing collaboration tools for remote work, and automating tasks for enhanced productivity.
Ultimately this initial wave focused on making it easier for humans to locate what they need and connect with each other while increasing efficiency levels at the same time.
But we're now entering the second wave of digital transformation. This wave is all about empowering machines to perform tasks and make decisions, rather than simply enabling humans to use technology more efficiently.
Companies in a range of industries are already investing in cloud-native and AI-driven applications to drive innovation and improve efficiency in the operational technology (OT) world.
The focus of digital transformation has now shifted toward machines. McKinsey’s Digital Manufacturing Global Expert survey reveals that most manufacturing companies (68%) consider connectivity, intelligence, and flexible automation to be their top priority. The global industrial automation market is expected to reach US$326.14 billion by 2027 after a decade of CAGR at 8.9%, according to Fortune Business Insights.
How ML is Changing the DX Landscape?
Standalone devices providing mediocre functionality are no longer considered state-of-the-art solutions. Rather, a truly innovative device should collect, communicate, analyze and take actions based on data.
Here is how machine learning can supercharge your digital transformation efforts:
Machine learning algorithms can automate a wide range of tasks, including sorting and routing customer service inquiries, extracting and classifying data from various sources, and more.
This automation can free employees to focus on more important, value-added work, improving efficiency and driving digital transformation efforts.
Machine learning can also reduce the risk of errors from manual processes, further enhancing the benefits of automation.
A good case in point is Vodafone. The telecommunication giant has decided to migrate its SAP (Systems, Applications, and Products in Data Processing) systems to the Google Cloud platform to take advantage of the scalability, security, and reliability offered by the cloud.
The migration is now in its pilot phase and will take two to three years to complete. It involves moving and automating 100 separate SAP apps and processes to the cloud to streamline operations and reduce the workload for employees.
"Now with the transition to the cloud and with Google Cloud tools, it can expand how it uses its data for analytics and process mining. This includes operations and monitoring opportunities to map data with other external sources, e.g., combining HR data from SAP with other non-SAP data, resulting in data enrichment and additional business value.”
Machine learning systems can analyze large amounts of data to provide insights and recommendations that would be impossible for a human to discern on their own.
This can drive improved decision-making in many areas, such as inventory management, customer service, and supply chain operations.
ICICI, one of India’s leading private banks, has deployed ML in over 200 business process functions across the organization including retail banking, agri-business, trade, and foreign exchange, treasury, and human resources management.
The software robots, which include chatbots, software bots that help customers with loan choices and carry out remittances, and email bots that sort emails based on transaction status, have helped the bank improve productivity and efficiency by performing over a million banking transactions per day, reducing customer response time by up to 60% and increasing accuracy to 100%.
As a result, the employees can focus more on value-added and customer-related tasks.
Machine learning has empowered businesses to make more informed, data-driven decisions that can drive efficiency.
Whether identifying patterns in sales data or predicting which products will be most popular in the future, machine learning can provide valuable insights that can inform decision-making and drive business success.
Machine learning can help businesses deliver a more personalized experience to their customers, whether through personalized product recommendations or targeted marketing campaigns.
By analyzing customer data and identifying patterns and relationships, machine learning algorithms can provide insights that can inform personalized recommendations and marketing efforts.
This can drive improved customer satisfaction and loyalty. According to research, 34% of retail store customers are fine with chatbots interacting with them as they shop.
By leveraging the power of machine learning, businesses can deliver a more tailored and relevant experience to their customers, driving success in the digital age.
The transition to an intelligent machine economy holds the potential to not only generate economic value but also improve and enhance daily life and safety.
To thrive in this environment, it is essential for embedded systems companies to embrace the second phase of digital transformation and adopt contemporary, digital edge-compatible platforms, tools, and processes.