Rajan Nagina, Head of AI Practice, Newgen Software

7 Mar 2022 , 09:41 AM

In an interaction with Mamta Maity, indiainfoline.com, Rajan Nagina, Head of AI Practice, Newgen Software said “Using AI, enterprises can scale automatically and provide a differentiated customer experience.”

What does AI mean for customer experience? How important is AI for increasing customer acquisition and improving customer retention?

Enterprises are adopting artificial intelligence (AI) at a rapid pace. And one of the key reasons for this accelerated adoption is that it helps deliver a superior customer experience.

Using AI, enterprises can scale automatically and provide a differentiated customer experience. For instance, with an AI system, banks can process loans instantly by analyzing the risk in real-time. Such instant gratification results in customer delight. Also, insurers can deliver a similar experience in case of claims.

Furthermore, AI helps enterprises analyze and understand each customer uniquely and enables them to make real-time decisions to deliver personalized experiences. Enterprises can also harness AI capabilities to enhance brand recognition and provide customer-centric offerings to improve customer acquisition and retention rates.

Is AI already an integral part of business strategies today? Can you provide any insights into the acceptance of AI by businesses?

Business leaders are increasingly appreciating AI as it can give them competitive differentiation and can act as a key growth driver. Various organizations are using AI. For instance, Pandora uses AI to serve the right music at the right time, Netflix to recommend new content and Uber for a lot of things. 

Many organizations have been working on a proof-of-concept to integrate AI with their business and have seen relative success in 2021 compared to previous years. More and more companies will soon board this ship, as was found in a study by Accenture where 75% of the global executives believe that will go out of business in the next 5 years if they do not scale AI .

According to you, which industries will remain at the forefront to adopt AI-enabled solutions? What are some of the most common AI use cases across industries?

The widespread adoption of AI is a testimony that AI can support use cases across industries. We have witnessed substantial traction for AI-enabled solutions and capabilities in sectors such as BFSI, healthcare, industrial automation, and others. The BFSI sector, in particular, has been one of the pioneers in adopting AI. AI plays an instrumental role in various financial processes such as credit scoring, fraud detection, signaling, etc. Using AI, financial institutions can optimize collections, validate credit card transactions, manage health insurance coverage checks, analyze vehicle photographs for fraud claims, and more.

As most common use cases are concerned, enterprises can deploy AI-based bots and virtual assistants to automatically respond to customer queries. Also, AI can help improve decision-making with detailed insights and enhance capacity utilization by predicting prices. For instance, insurance companies can utilize AI for classifying a claim as fraudulent in real-time and reduce revenue leakages. Additionally, enterprises can leverage AI to gain customer-level insights to drive campaign effectiveness, compute customer lifetime value, reduce churn, and determine the next-best-action for deeper engagements.

Development and implementation of AI solutions must require domain expertise. What is your take on that?

Onboarding data scientists and other data experts can be expensive. Though these experts are invaluable, upskilling the existing workforce inclined towards AI and related technologies seems an ideal approach. Analysts should be quickly upskilled to accept the role of citizen data scientists, while business users can be empowered with data and analytics with proper tools and training.

Also, to leverage AI successfully, it requires seamless collaboration among various groups such as domain experts, business analysts, citizen and expert data scientists, machine learning operations teams, and business users. This is feasible with proper tools, training, and governance.

What are the key areas of focus for CIOs looking to scale their AI investments and realize the true potential of AI?

A few areas where CIOs should focus more are:

• Take AI beyond expert data scientists. It can help you foster a rewarding collaboration among business users, domain experts, citizen data scientists, and other stakeholders to quickly build data and AI assets
• Invest in machine learning operations to successfully embed AI in your business processes from the proof-of-concept to the delivery stage
• Opt for cloud-based AI platforms as this will give you the flexibility to start small, experiment, and scale full-fledged at the right time