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4 ways AI and Digital Transformation Enable Deeper Automation

The term “Digital transformation” has been a buzz for quite some time across industry verticals. At the same time, the advent of AI and ML has created a huge enthusiasm in organizations in driving towards automation. While most of us think AI to be a future of technology, but AI is already evolved in most of the business across various verticals. For instance, Spotify (Music App) uses machine learning for music recommendations, also AI is now in our homes too in devices like Google Home and Alexa. 

Regardless of the hype around Artificial Intelligence, not all organizations realize how AI can be unified into their operational environments to make a significant impact. But in reality, AI capabilities such as machine learning is more readily accessible and easier to apply to daily operations than organizations may think. However, most of the businesses often don’t know that both Digital transformation and AI can lead the way to complete automation when they are combined.   

A recent survey report “Human Amplification in Enterprise” by Infosys, says the organizations who used AI-supported digital transformation has remarkably shown 15 percent raise in their revenue bottom line. The report also stated that Machine learning is the key to making more accurate decisions with zero errors. It also helped organizations to speed up their day-to-day activity by upto three times. 

Let’s understand the 4 ways AI and Digital Transformation enable deeper automation for organizations:

Augmented analytics

Data Analytics is something which no organization can ignore, as it provides a deep insight into a system or people behaviour (customers). This helps organizations to better understand their customers and recommend products or solutions that best fit their requirements. Analytics also helps organizations in Better Customer Service, More Efficient Operations, More Effective Marketing and Improved Decision Making.

AI/ML make modern data analytics extremely powerful! Machine learning is important for data analytics as it includes algorithms that can learn on their own. ML-enabled data analytics is highly capable in predicting the outcome, as the system continues to learn from the past data. This type of ML-based analytics is now referred to as “Augmented Analytics” and is highly used as well as recommended for enterprises of all sizes

Automation

Automated systems are now everywhere, Chatbots and Robotic Process Automation (RPA) tools are great for repetitive and predictable tasks, i.e, the bot will provide answers for queries that are deterministically programmed. These systems are not “Intelligent”, Automation is now past, Intelligent Automation is what matters. 

Intelligent Automation (IA) is a blend of artificial intelligence (AI) and Robotic Process Automation (RPA) technologies which collectively empower faster end-to-end business process automation and fast-track digital transformation.

AI expands the scope of automated systems and moves organizations to the second wave of digital transformation.

Enhanced Consumer engagement and insight

Analysing website traffic logs, is the most common thing companies are doing to measure their consumer engagement. AI based consumer engagement analytics helps organizations in optimizing customer engagement by dynamically aligning the website content as per the customer's preferences. Following are some of the benefits of AI in customer engagement:

  • Unification of data
  • Real-time insights
  • Business context
  • Gathers valuable data 
  • Appreciates customer preferences
  • Anticipate customer needs

AI-digitized supply chains

Artificial Intelligence in Supply chain does something beyond what an RFID (radio frequency identification) does. An RFID tag can track the entire information of the shipment right from the manufacturer to the customer's end, but it cannot identify the damages of the product while transiting. Here is where AI plays a crucial role. Integration of AI in the regular supply chain will help organizations to easily identify the damaged products that may cause during the transit. This kind of intelligence helps organizations to halt the delivery of the damaged product to the customer. 

 

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Mounika Raghavarapu 0

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