Empowering Enterprises on Their AI Adoption Journey

The growth of AI/ML represents an exciting journey towards unparalleled efficiency & creativity. In this WSTA exclusive, Mphasis CEO shares his insights about how CSPs help financial firms cautiously navigate this transformative landscape.

Written by Nitin Rakesh, Chief Executive Officer and Managing Director, Mphasis.


As we find ourselves in the midst of October 2023, the digital transformation landscape continues to surge forward, driving enterprises worldwide to optimize their operations and bolster efficiency. With global IT spending projected to reach an impressive USD 4.6 trillion[1] this year, and with an overwhelming 77 percent[2] of enterprises having allocated budgets for technology advancements throughout 2023 and beyond, it is evident that businesses are wholeheartedly investing in the future of tech and innovation. Servicing these enterprises that are on a digital transformation journey requires that service providers have an innate understanding of enterprise needs.


Driving transformation with AI

Many enterprises are experimenting with AI/ML (artificial intelligence/machine learning), evaluating the potential of the tech to automate tasks and create new products and services. Emerging as a recent technological development with significant industrial applications, Generative AI has the capability to produce synthetic, high-quality content tailored to users’ natural language inputs. These tools have immense potential to improve customer experiences in different industries and they are being rapidly deployed. McKinsey[3] has pinpointed 63 potential applications for Generative AI and predicts that these use cases could contribute anywhere from USD 2.6 trillion to USD 4.4 trillion to the market annually.

This growth highlights the profound influence these technologies will exert on the global economy, reshaping business landscapes and revolutionizing customer experiences. This transformation represents an exciting journey towards unparalleled efficiency, productivity, and creativity, shaping a future full of possibilities for businesses and consumers alike. However, we also need to be cautious as we navigate this transformative landscape.


Meeting enterprise needs with cloud

Enterprises today must adapt quickly to market changes and customer demands. Cloud computing emerges as a valuable solution, offering computing power, storage, and networking resources to keep up with this evolving landscape. Cloud services are agile and offer enterprises much-needed scalability and flexibility. They also offer a cost-effective infrastructure for the easy integration and deployment of generative AI-powered solutions. As they scale to unprecedented levels of computing demand, they can manage the net carbon footprints better than most enterprises can.

This means working with the latest tools and services that make it easier for enterprises to deploy and manage new products and services. Cloud service providers (CSPs) are well-positioned to play this key role in the development and adoption of next-gen technology and tools such as generative AI and Robotic Process Automation (RPA) solutions.

CSPs must possess both the necessary expertise to seamlessly integrate AI capabilities into existing technology solutions and offer stand-alone AI-powered services. They must demonstrate their capacity to deploy cutting-edge conversational AI platforms, empowered by generative AI technology and large language models (LLMs). This enables enterprises to facilitate sophisticated interactions and communication with users. CSPs should have the ability to provide data analytics and ML solutions tailored to address crucial aspects of businesses, such as fraud detection, customer churn prediction, and more.

Maintaining a competitive edge
CSPs need to remain up-to-date with industry trends and how these trends impact enterprise requirements. The adoption of hybrid and multi-cloud environments, the increasing significance of data analytics, and the growing demands for AI and ML are some of these trends. By staying informed, service providers can adapt their services to meet evolving customer demands.

It also means investing heavily in research and development of AI/ML models. CSPs that can keep pace with the rapid evolution of generative AI technology and develop new tools and services that make it easier for businesses to adopt AI are well-positioned to generate significant revenue from this emerging AI market.

Enterprises must invest in technology to navigate the changing tech landscape. From digital transformation to analytics, cloud computing, AI, and application services, businesses are leveraging these trends to drive growth, improve customer experiences, and reinvent financial services. Cloud service providers must be partners in their digital journey.


About Mphasis

Mphasis’ purpose is to be the “Driver in a Driverless Car” for global enterprises by applying next-generation design, architecture, and engineering services, to deliver scalable and sustainable software and technology solutions. Customer-centricity is foundational to Mphasis, and it is reflected in Mphasis’ Front2Back™ Transformation approach. Front2Back™ uses the exponential power of cloud and cognitive computing to provide a hyper-personalized (C=X2C2TM=1) digital experience to clients and their end customers. Mphasis’ Service Transformation approach helps ‘shrink the core’ through the application of digital technologies across legacy environments within an enterprise, enabling businesses to stay ahead in a changing world. Mphasis’ core reference architectures and tools, speed and innovation with domain expertise and specialization, combined with an integrated sustainability and purpose-led approach across its operations and solutions are key to building strong relationships with marquee clients. Click here to know more. (BSE: 526299; NSE: MPHASIS)


For further info, please contact: Deepa.nagaraj@mphasis.com

[1] Gartner Forecasts Worldwide IT Spending to Grow 5.5% in 2023

[2] Technology Report 2022 by Bain and Co

[3] The economic potential of generative AI: The next productivity frontier