By: Rakesh Sancheti, Area Vice President at LTI, and Business Head for Analytics & Information Management Practice for Europe and Nordic

The speed of technological change is accelerating the wider acceptance of cognitive and AI realm. Recent advances in cognitive technologies, such as Cognitive Automation and Artificial Intelligence, are pushing the frontier of what humans and machines are capable of doing in all facets of the IT Service delivery.

To name a few; machine learning, natural language processing, conversational interfaces, robotic process automation, cognitive computing, cognitive cyber security and cognitive business signals, present multiple opportunities to bring enormous efficiency for the IT service provider, and enable cost savings benefits for the client. In the last few years, customers are also becoming more aware about the potential of AI & RPA technologies, and pushing hard to their IT suppliers to embed significant productivity gains in their current and new outsourcing contracts. The two most important reasons for including requests for AI &RPA capabilities, are cost reduction and visibility to long-term impact on their IT/Business operations.

As cognitive solutions start to become frontiers of CIO IT agenda, the consequences of not factoring productivity gains driven by AI & Automation in outsourcing contracts, will now become increasingly significant. AI and automation will result in reduction of the overall number of IT services sector employees between 7-10% by 2022 in India, and the US, according to the HfS research. AI and automation will drive rationalization of IT workforce and reducing roles at repetitive works, such as software maintenance, testing. But at the same time, it will open up new opportunities for more medium-skilled and highly-skilled jobs across service industry to do complex works, using the domain expertise.

Below framework represents realm of AI in Service economy and highlights 6 key building blocks, which will drive IT Service delivery organization of tomorrow. Let us try and understand each one of the blocks to gain clarity on their role and impact.

Cognitive Computing: Cognitive systems rely on deep learning algorithms and neural networks to process information by comparing it to a teaching set of data. The more data the system is exposed to, the more it learns, and the more accurate it becomes over time; and the neural network is a complex “tree” of decisions the computer can make to arrive at an answer. The goal of cognitive computing is to simulate human thought processes in a computerized model. Using self-learning algorithms that use data mining, pattern recognition and natural language processing, the computer can mimic the way the human brain works.

Data Fabric: In order to maximize the outcome of cognitive systems, they need to be fed with lots of data. Hence, there is a need to have a unified new data fabric to process overwhelming volume, variety and veracity of data coming from numerous process and systems, which can further be mined by cognitive brains to enhance the knowledge repository. Data fabric are backbone to the AI and Automation applications and provide necessary speed, scale, and reliability to support enterprise-grade cognitive applications. Companies and organizations are looking to wring the value out of all that data through AI.

Cognitive Automation: In simple words, Cognitive Automation is performed with a bunch of softwares, which have the ability to automate complex human tasks and also automate processes, which involve unstructured data (like images, documents, or PDFs). Cognitive Automation is performed leveraging various technologies, such as straight through processing, robotic process automation, OCR, digitization, etc. and powered by AI & Machine Learning. Cognitive Automation will ask for human assistance when it encounters something it cannot understand, and will learn from those escalations to continuously improve its ability to automate. Few examples of cognitive automation could be like identifying specific products or objects within an image, or extracting and matching relevant data from unstructured documents, or synthesizing large volumes of information into concise descriptions.

Artificial Intelligence: Artificial intelligence has enabled us with multiple capabilities like natural language processing, natural language generation, machine learning, deep learning, computer vision, speech recognition, etc., which can be leveraged across business and IT processes to build intelligent solutions. NLP refers to the ability of computers to work with text the way humans do. For instance, extracting meaning from text, or even generating text that is readable, stylistically natural, and grammatically correct. Machine learning is the process of automatically discovering patterns in data. Computer vision refers to the ability of computers to identify objects, scenes, and activities in images. Speech recognition focuses on automatically and accurately transcribing human speech.

One of the common applications of AI in recent time have been Intelligent Chatbots. Today, chatbots are able to understand what we mean, not just what we say. Earlier there was a challenge with respect to translate someone’s words into feelings, and it was not possible because there were too many variables and possible outcomes. But with the emergence of pattern recognition technology, it has opened doors for AI to be able to “learn” and interpret an individual’s feelings, thus allowing for such a complex translation. Pattern recognition will be a key component for AI technology to succeed.

Knowledge Fabric: Knowledge of how to perform a certain task, or make a specific decision walks out the door with employees migrating to another job or retiring. Organizations are building knowledge graph, which in essence codifies the human knowledge and captures complex relations between actions, processes, and assets, coupled with advanced AI algorithms, semantic search, and deep learning. Knowledge fabric makes the mined knowledge accessible to the people right at the time when they need it. This helps employees make faster and more relevant data-driven decisions and solve issues leading to time and cost savings. Knowledge Assistants and Knowledge Applications are built on the top of knowledge fabric, which really bring out the value of user-guided and machine-assisted approach.

Mr. Algorithm: Algorithms are nothing new to us and we all are aware of the some of the fascinating work done by algorithms in the past few years, and how they have become ubiquitous part of our life. Service jobs across the country are increasingly being managed with the help of mathematical models of customer demand, revolutionizing everything from taxi driving to food delivery, home cleaning, and laundromats. Algorithms are used for calculation, data processing, and automated reasoning. Algorithms make systems smart and make AI and automation solution scalable & repeatable, however, without adding a little common sense into the equation, they can still produce some pretty bizarre results.
It may seem weird that human could be replaced by a computer, but with the given trend and advancement of technology, it is not unreal to say that algorithms could be new IT software engineering staff being billed to customers.

In summary, increasing sophistication of automation and augmentation technology, powered by artificial intelligence, is changing the rules of what’s possible. Cognitive automation is bringing increased reliability and speed to large segments of the IT delivery model (for example, the ‘run’ or ‘operations’ teams), and this improvement is being further enhanced through AI leveraging past learning data to further enhance performance. In order to drive, to bring cognitive automation & AI together, the Big data-powered backbone ‘data fabric’ needs to be in place, which enables cognitive systems to leverage deep learning algorithms and neural networks to process information and create knowledge graph.

The above allows the development of a true ‘Knowledge Fabric’, which improves ongoing process based on history, effectively codifying your knowledge direct into the delivery model. The outcome is algorithms fully tailored to your business. To conclude, AI will transform IT outsourcing by promoting efficiency through automation, reducing costs, providing control, increasing security and improving negotiation terms for contracts, and in my view, it presents a winning opportunity for both the customer and supplier.