Artificial Intelligence

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Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
Artificial Intelligence
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Artificial Intelligence

AI Technology Landscape

AI defies future with ubiquitous and trustworthy applications to perform specific tasks of visual perception, speech recognition, and decision-making.  Through data interpretation and specialized knowledge in areas of natural language processing, learning, planning, and executing as the forefront of technology in AI research and development.

Increased incidence of AI applications as Business Process Automation (BPA), Internet of Things (IoT), Robotics, Autonomous Vehicles, Cybersecurity, Intelligent Virtual Assistants (IVA), Cognitive Computing and Big Data having a meaningful impact with multiple use cases in the technology stack, including speech recognition, computer vision, machine learning, natural language processing (NLP), text analytics, and social media monitoring.

Key Areas

Intelligent Automation

Intelligent automation optimizing IT processes to combat the growing skills gap with deeper focus on higher value work.

Rise of Cyber Assistants

Skilled AI systems identifying abnormal behaviors and better understand cybersecurity threats by consuming billions of data artifacts.

Synthetic Data

Synthetic data to augment real data to speed up the training of AI models, protect sensitive data, improve accuracy.

Energizing a Carbon-Neutral Future

Deploy strategies by applying AI technology to establish more transparent, traceable, and decarbonized supply chains.

Foundation Models

Foundation models introducing systems that can be used and re-used for different tasks, with minimal fine-tuning.

AI implemented at Enterprise Scale

Unboxing data to do meaningful EDA (Exploratory data analysis) and leveraging AI with a focus on governance of AI systems.

Personalization in Multiple Dimensions

Conversational AI with expressions and emotions along with the voice of choice will bring in the personalization needed for making digital transformation possible.

Explanatory Data Analysis

IBM’s Global AI Adoption Index 2022 reports 57% of IT professionals in India report active deployment of AI and over a quarter (27%) plan exploring the use of AI.

7 key areas of AI adoption 2023

AI Powered Intelligent Automation

With the right AI Powered Automation processes and team in place, intelligent automation will optimize business and IT processes across all industries and combat the growing skills gap to create a deeper focus on higher value work.


Rise of Cyber Assistants

Skilled AI systems or Cyber Assistants are adept at identifying abnormal behaviors, assessing vulnerabilities dynamically and flagging anomalous activity that can indicate new threats. AI will also improve its knowledge over time to better understand cybersecurity threats and cyber risk by consuming billions of data artifacts.



Creating Reusable AI through Foundation Models

Foundation models will replace the task-specific models that have dominated the AI landscape to date by introducing systems that are trained on a broad set of unlabeled data and can be used and re-used for different tasks, with minimal fine-tuning.

Trustworthy AI implemented at Enterprise Scale

As organizations continue to leverage AI to improve processes, there will be an increased focus on trust, transparency, and governance of AI systems to realize it’s true potential. Unboxing data to do meaningful EDA (Exploratory data analysis) coupled with platforms to identify bias, data quality to inform customers and regulatory bodies about how specific decisions were made.

Synthetic Data

Data about financial information, healthcare records and consumer analysis come with significant hurdles such as privacy, ethics, and copyright laws. Synthetic data will offer a work-around because they are computer-generated examples that can augment or replace real data to speed up the training of AI models, protect sensitive data, improve accuracy, or find and mitigate bias and security weaknesses.

Energizing a Carbon-Neutral Future

Environmental risks are business risks, but technology can help companies mitigate them by deploying strategies that decarbonize and digitize businesses across industries. By applying AI technology, companies will establish more transparent, traceable, and decarbonized supply chains. AI and automation will help organizations to collect data, identify risk, validate documentation, and provide audit trails, even in high inflationary periods, while also managing their carbon, waste, energy, water consumption and material utility.

Personalization in Multiple Dimensions

In 2023, AI will continue its impact on the consumer care journey with more personalized and fully realized interactions. Conversational AI with expressions and emotions along with the voice of choice will bring in the personalization needed for making digital transformation possible.