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Paving the way for new age business transformation


With the advent of Artificial Intelligence, human-machine interaction has not been interwoven into many of our everyday jobs. Over the past few years, technology has made considerable advances in approximating human interaction, especially regarding speech recognition, detecting emotions, visual cues, and voice intonation. From Apple’s Siri to Google Home, these technologies are fast becoming omnipresent and are likely to significantly impact how we perform our jobs in the future. We are rapidly moving towards a workplace where people interact with machines on a routine basis.

What is AI?

Before examining how AI technologies impact business transformation, defining the term is essential. “Artificial intelligence” is a broad term that refers to any computer software that engages in humanlike activities – including learning, planning, and problem-solving. 

Calling specific applications “artificial intelligence” is like calling a car a “vehicle” – technically correct, but it doesn’t cover any specifics. 

To understand what type of AI is predominant in business, we need to understand the contexts in which AI is used for business transformation. The two flavors predominantly used as synonymous with AI are Machine Learning and Deep Learning.

Machine learning

Machine learning is one of the most common types of AI in development for business purposes today. Machine learning helps put vast troves of data – increasingly captured by connected devices and the Internet of Things – into a representable context for humans. Machine learning can rapidly analyze the data as it comes in, identifying patterns and anomalies. This will facilitate decision-making based on the inference made by the Machine Learning Model.

Deep learning

Deep learning is an even more specific version of machine learning that relies on neural networks to engage in what is known as nonlinear reasoning. Deep learning is critical to performing more advanced functions like fraud detection. It can do this by analyzing a wide range of factors at once.

Deep learning has a great deal of promise in the business and is likely to be used more often. Older machine-learning algorithms tend to plateau in their capability once a certain amount of data has been captured. Still, deep learning models continue to improve performance as more data is received. This makes deep learning models far more scalable and detailed.

Relevance of AI and Business Today

Rather than serving as a replacement for human intelligence and ingenuity, artificial intelligence is generally seen as a supporting tool. Although AI currently has a difficult time contemplating tasks that involve common sense/intellectual response  in the real world. However, AI is adept at processing and analyzing troves of data much faster than a human brain. Artificial intelligence software can then return with synthesized courses of action and present them to the human user. In this way, we can use AI to help the game out the possible consequences of each action and streamline the decision-making process.

AI has already paved the way for today’s business and some of its industrial applications.

Human-to-Machine Interaction

Human-to-machine interaction is becoming highly advanced, and today the goal is to make machines interact like humans in as many ways as possible. Machines are just starting to detect emotion in our voices and written words, and they can learn to change their responses accordingly. 

Chatbots, for example, demonstrate how technology can increasingly capture the human essence by evoking and responding to human emotions and actions. For example, the next generation of Siri has a much more human-like voice and better intonation. That all comes from machine learning and AI. Many enterprise-grade chatbot platforms also support cognitive emotion detection.

Hence with this advancement, people need not master complex menus or learn how to program or operate things. Interacting with machines via the spoken word, they can simply ask a question or state a command, and a machine will autonomously provide answers and assist in completing tasks (Digital Personal Assistant). 

Cybersecurity

Artificial intelligence holds great promise with its ability to learn patterns of networks, devices, and systems and decode deviations that could reveal in-progress attacks. AI systems can recognize cyberattacks and other cyber threats by monitoring patterns from data input. Once it detects a threat, it can backtrack through your data to find the source and help to prevent a future threat. That extra set of eyes – one that is as diligent and continuous as AI – will significantly benefit preserving your infrastructure.

The use of data science to make Web 3.0 secure is another exciting example. A recent AI challenge that we held on our platform for a German startup helped develop an AI that can identify original NFTs and prevent wash-trading. Similar use cases lie in the existing financial system, where banks increasingly use machine learning techniques to prevent money laundering and fraudulent transactions. 

Customer relationship management

Artificial intelligence is also changing customer relationship management (CRM) systems which require heavy human intervention to remain current and accurate. Applying AI to these platforms transforms a standard CRM system into a self-updating, auto-correcting system that stays on top of your relationship management.

For example, customer service and CRM tools use artificial intelligence to transcribe customer calls instantly and suggest how they can better meet the needs of their customers or handle objections they encounter. It also assists the leadership in an organization to leverage insights and develop better experiences that delight their customers.

Internet and data research

Artificial intelligence uses vast data to identify patterns in people’s search behaviors and provide them with more relevant information regarding their circumstances. As people use these applications more and more, AI technology becomes even more advanced, users will have a more customizable experience. This will expose the diaspora of options because AI will have an easier time targeting a very specific audience. AI has helped to shift the paradigm of how the correct information finds the right user at the right time.

Consolidate Business Operations

One of the major problems faced by large-scale businesses is the fragmentation of various business processes and create bad synergy and silos. AI technology integrated into enterprise resource planning solutions (ERP) will be able to take the fragmented working parts and combine them into a functioning whole by testing and analyzing each piece of information. This will consolidate employees, operations, and systems and, consequently, more efficiency, productivity, and revenue. The onset of advanced analytics using AI has facilitated finding the data patterns and then the usage of machine learning to discover insights. It helps companies better plan business operations and better understand their customers. This technology enables business leaders to gain insight into their organizations as they function, increasing revenue, reducing costs, and improving overall customer satisfaction. Companies must act quickly and often in real-time in today’s digital age.

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Disclaimer

Views expressed above are the author’s own.



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