The Infosys Labs research blog tracks trends in technology with a focus on applied research in Information and Communication Technology (ICT)

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March 29, 2018

Cognitive System-Mimicking Human Understanding

With advancements in artificial intelligence algorithms, it's possible for machines to mimic human understanding. They are able to analyze and interpret information, make deductions and identify patterns from the information sets analogous to human brain. These new generation of machines are categorized as cognitive systems. These systems aggregate machine intelligence, predictive analytics, machines learning, natural language engines and image/video/text analytics to enhance human-machine interaction.

Evolution of Cognitive System

The evolution of cognitive systems can be classified into three types:

  • Cognitive Systems for Process Automation
  • Cognitive System for deriving Insights
  • Cognitive System for Engagement

Cognitive Systems for Process Automation

The first phase focusses on the various machine learning and robotic process automation applications to develop substantial domain insights of particular processes and aim to automate them. This phase is aimed at automating repetitive, mundane and low intelligent jobs that employs highly trained human manpower. An example of it is the character recognition and handwriting detection tools deployed by various banks and financial institutions in middle and back office operations to reduce risk and cost. Another example is implementation of chatbots in customer service fields where the bot is able to answer general customer enquires with regards to account balance, credit card offers, utility bill payment questions etc. Cognitive automation helps in improving efficiency as greater volume of data can be processed at a faster rate while improving the compliance capabilities and reducing errors.

Cognitive Systems for deriving Insights

This phase of cognitive evolution encompasses extraction of meaningful insights and relationships from a myriad of data streams comprising of both structured and unstructured data. This phase is evolutionary in nature as the accuracy of the insights and observations improves as the system processes increased amount of data. Cognitive insights have capabilities of providing actionable understandings into possible future events by sensing and analyzing past and present events. This would enable leaders to plan and prioritize future strategies and roadmaps, and augment enterprise capabilities with changing market dynamics.  An example of cognitive insights could be implementing deep learning networks to understand credit card usage patterns among working class customers aged between 25 years and 40 years. Such tailored and actionable insight would help the bank to create hyper-personalized offerings which would be beneficial for the customers and in turn would improve customer loyalty.

Cognitive Systems for Engagement

The final phase of cognitive evolution is intelligent agents that interact and engage with customers using cognitive capabilities. An example of cognitive engagement could be the deployment of voice assisted virtual agents (like Alexa, Siri etc.) to interact with human for performing certain specific tasks. Customers can book an appointment with a fund manager through an Alexa enabled interface or employees' can clarify doubts with regards to the HR policies in an organization through a voice enabled assistant. Cognitive systems have the capabilities to unlock the power of unstructured information flowing through various digital engagement channels and other sources, leveraging image/ video or text analytics to generate actionable insights and helping the bank to develop personalized relationship with the customer.

Characteristics of Cognitive System

Cognitive systems are characterized by their capabilities to understand and extract context from data and learn from patterns to generate future predictions. Some of the characteristics of cognitive systems are:

  • Recognize and Understand: Cognitive Systems have the capabilities to extract information from handwritten text, image, voice and video by utilizing natural language processing, machine learning and predictive algorithms
  • Identifying Contexts: Cognitive Systems are capable of contextualizing information extracted from various data sources and deduce understanding based on context. As such, these systems are able to process information that are situation aware and build suitable data relationship models.
  • Decision Making: Because of its capabilities to identify and establish context, cognitive systems are able to reason and enable decision making based on real-time environment variables.
  • Learn and Improvise: cognitive systems are designed to continuously learn from data inputs and improvise its decision making capabilities based on previous results and feedback received.

Conclusion

Cognitive systems have the potential to help enterprises to optimize various business processes, infer insights from seemingly inconsequential unstructured data sets and create personalized engagement models. Even though the field of cognitive system is ever-evolving, enterprises should undertake a strategic view point over the long term benefits which could help the organizations to maintain their competitive advantage.

March 12, 2018

Trends and Innovation in HR

"Human resources isn't just a thing we do, it's THE thing that runs our business"
- Steve Wynn, Entrepreneur

The importance of the HR department has, till recent times, been overlooked. The HR department was initially handling record keeping, compliance to laws and regulations and compensation & benefits for employees. Over the past decade, with an onset and adoption of technologies and automation, the HR department has evolved remarkably. In addition to payroll process automation and streamlined on-boarding, new platforms and technology has enhanced the talent management systems, allowing more focus on personalized employee engagement.

Despite the technological and procedural advancements around the HR practice over the past few years, there are still many challenges to overcome. Some of these are in effective and efficient training, managing the workforce, recruitment, employee retention, addressing concerns and in meeting dynamic demand requirements.

In order to overcome these challenges, there are numerous technological and business trends that are being considered to enhance the HR landscape. The business trends include consumerization of HR (providing a consumer-style experience for employees), workforce on-demand (based on the concept of on-demand economy, where recruitment is temporary, on contract basis, and as per need), collaborative HR (a model where HR extensively works and grows with marketing, finance, IT and other functions in the organization) and agile HR (which involves policy changes to build organizational and employee agility, so as to develop and retain top talent in all geographies). All of these trends involve modifying HR processes and operations to adapt to the ever changing employee requirements, and to enhance employee engagement and experience.

Human resources coupled with an emphasis on technology & professionalism is the quality structure of organization" - Unknown


The technological trends include new technologies such as Artificial Intelligence (AI) and Machine Learning (ML), Conversational User Interfaces (CUI), Blockchain, Augmented Reality (AR), Virtual Reality (VR) and Big Data Analytics. These technologies, based on their applications, are categorized into themes, namely:

  • Cognitive HR, which is essentially the use of cognitive technologies such as AI, ML and CUI to improve various enterprise HR processes and operations. Cognitive HR can be applied in the form of chatbots which enables easy accessibility, instantaneous response & round the clock availability to address employee concerns, and sentiment analysis, which helps to identify teams and units under stress by capturing the sentiments of mails and chat conversations. These help to improve various processes such as talent management - where AI can be used in talent acquisition, onboarding procedures and training, and performance management - for continuous assessment and unprejudiced appraisals using AI. Companies such as Mya and BetterWorks provide AI powered HR solutions, such as automating 'resume to hire' processes, and continuous performance assessment.

  • Blockchain is a decentralized and secure ledger which gives participating parties a way of validating the information, thereby helping to streamline HR processes in organizations. Blockchain helps to improve processes such as candidate verification by ensuring validity and authenticity of data, cross border payments by reducing transaction time and costs by eliminating intermediaries, data protection by using blockchain's consensus mechanism and immutable properties, and smart contracts to automate various administrative tasks such as labour agreements and payments. Companies such as Bitwage (facilitates cross-border payments on blockchain) and Chronobank (uses blockchain-type technology to allow employers to pay contract workers without going through banks) are using blockchain to improve HR processes.

  • Immersive HR leverages AR, VR and IoT to enhance employee engagement and experiences. The various applications include employee training - for on the job training and guidance using AR and VR, recruitment - to provide a glimpse into the company work environment and opportunities using AR, and employee management - to analyze and get insights about employees by using wearables and IoT. Companies providing immersive solutions in HR include Galivac, which provides VR solutions for an immersive employee assessment program, and STRIVR, which offers VR solutions for employee training.

  • Data and Analytics helps to gain insights about individual employees and functions within the organization. The HR processes that can be improved using data analytics include forecasting - to accurately predict hiring numbers, dynamic rostering - for hiring, managing and assigning workforce dynamically, talent management - for data driven improvements to the hiring strategy, performance management - for unbiased, data driven employee appraisal and review, and proactive grievance redressal - by analyzing information from different communication channels to predict and address impending employee concerns. Companies that offers data driven solutions for HR include Impraise, whose solution utilizes analytics for improving employee performance appraisals, and Pivotal Talent, which has a solution named TAMI that uses data and predictive algorithms to determine an optimum shortlist from thousands of candidates.

Investing in, and keeping up to date on new and improved technology and business trends in HR is essential for an organization to avoid any inefficiencies related to outdated processes and policies. The new trends in HR not only keeps the employees engaged, but also provides a level of personalized care and support that greatly enhances employee productivity, welfare and trust in the enterprise, leading to successful organizational development.