“If AI is deployed too aggressively without a clear plan, it may do more harm to customer relationships than good,” says Deepak Visweswaraiah when asked about AI strategies to improve Customer experience. And he is not alone.
Vice President, Platform Engineering and Site Managing Director,
Pegasystems India
According to a survey by Deloitte Digital, CX leaders recognize the risk of relying heavily on AI integration to impact customer trust. Preserving trust and empathy stands out as a key focus when considering the expanded use of AI.
26% of experienced leaders consider the integration of AI (including generative AI, machine learning, responsible AI, and others) to be a low priority in the field of customer and employee experience in the coming years, as per Deloitte.
In a conversation with CIO&Leader, Deepak Visweswaraiah, Vice President, Platform Engineering and Site Managing Director, Pegasystems India shares his opinion on how businesses can leverage AI without compromising their customers’ trust and what a Robust Toolkit looks like for enterprises.
CIO&Leader: Why is integrating real-time decision-making into CX strategies essential for companies? How does it enhance customer experience?
Deepak Visweswaraiah: Integrating real-time decision-making into CX strategies is essential for companies aiming to mitigate revenue risks and avoid potential pitfalls. It enables them to optimize every customer interaction regardless of the channel, leveraging the latest available data to evaluate the best message to send to each customer. This capability goes beyond speed—it’s about using data to personalize interactions, quick problem-solving, and exceed customer expectations. By leveraging real-time insights, businesses can boost efficiency, build trust, and foster loyalty, ultimately gaining a strategic advantage in competitive markets.
To support real-time decision-making, a robust toolkit is crucial, including:
* Advanced CRM systems for a comprehensive view of customer history
* Knowledge management systems for quick access to information
* AI-powered chatbots for handling routine queries and improving response times
* Omnichannel communication tools for seamless interaction across channels
Pega leads with an AI-powered customer service platform that automates tasks, recommends next best actions, and delivers exceptional experiences at scale. The Pega Customer Decision Hub integrates analytics, AI, business rules, and real-time customer data to optimize decisions based on business priorities. This approach shifts companies from traditional campaigns to one-to-one, real-time customer interactions, positioning the Customer Decision Hub as the core of customer engagement.
CIO&Leader: What quantitative impacts has Pega observed in business outcomes through the implementation of real-time decision-making? Could you share specific use cases that demonstrate these benefits?
Deepak Visweswaraiah: Pega’s technology enhances customer experiences through predictive analytics, adaptive learning, and real-time decision-making. The Pega Customer Decision Hub uses AI to analyse data instantly and recommend the best actions. Predictive analytics forecast customer behaviour, while adaptive models refine predictions based on new data, ensuring relevance over time. This approach delivers tailored, data-driven engagement strategies. The Hub improves efficiency by automating decisions, reducing manual intervention, and saving costs. Its predictive and adaptive capabilities streamline targeting, cut marketing waste, and simplify system management, leading to quicker, and more effective responses to market and customer changes.
One specific use case is the retention use case, where the application provides the agent with the tools to understand the value of the customer and the best options to retain customers who are looking to churn.
In another instance, a retail bank could use the Hub to analyse a customer’s transaction history and provide personalized credit card offers, while a service provider might use it to predict and proactively address service issues.
CIO&Leader: What is Pega’s GenAI Blueprint, and how does its integration with the Customer Decision Hub enhance customer satisfaction and drive business success? What unique advantages does it offer compared to other solutions in the market?
Deepak Visweswaraiah: Pega GenAI™ Blueprint is a revolutionary tool for application design. It merges generative AI with Pega’s best practices to drastically speed up the design process and transform digital transformations. Gone are the days of lengthy design marathons. With Blueprint, teams can align on a vision in hours instead of days. Its combination of generative AI and industry expertise makes it the fastest way to translate clients’ goals and processes into effective application designs.
Pega’s GenAI Blueprint and Customer Decision Hub create a powerful synergy, revolutionizing application development and customer engagement. The GenAI Blueprint rapidly transforms ideas into functional applications, offering a low-code platform that streamlines workflows and decision-making. When integrated with the Customer Decision Hub, this combination unifies data and channels to predict customer needs in real time, enabling hyper-personalized actions.
This approach allows businesses to define and visualize their 1:1 engagement strategy by understanding their industry, key products, business-driving outcomes, and prioritized engagement channels. It also helps optimize customer journeys through AI-generated content and data insights, enhancing both operational efficiency and customer satisfaction.
CIO&Leader: What are the best AI strategies and practices for meeting the increasingly high customer expectations? How can these strategies help companies differentiate themselves in competitive markets?
Deepak Visweswaraiah: Today, we are on the verge of the most significant inflection point we have ever seen in customer experience (CX), driven by the latest breakthroughs in AI. Indian businesses are recognizing the immense potential of generative AI, with CX leaders feeling the obligation to harness it. As emerging technologies heighten customer expectations, companies must start delivering instant, personalized experiences in real time, anticipating needs before they arise.
However, if AI is deployed too aggressively without a clear plan, it may do more harm to customer relationships than good. A strategy for AI and CX starts with first understanding business challenges and using it strategically to overcome the drawbacks. The best AI strategies and practices focus on a holistic, integrated approach to customer service. Rather than implementing isolated AI solutions for individual touchpoints, companies should invest in comprehensive AI-powered tools that create a unified view of the customer.
To unleash the true potential of AI, companies need to focus on creating connected journeys that span all aspects of customer service. This includes contact center operations, workforce management, performance frameworks, and direct customer interactions. By identifying all AI-impacted processes within the customer service ecosystem and understanding the connections between them, companies can build AI-infused workflows that deliver seamless, intelligent experiences across all touchpoints.
Another crucial strategy is the use of AI in augmenting human capabilities. By providing real-time assistance to customer service representatives, AI can help employees deliver more accurate and efficient responses. This hybrid approach combines the empathy and problem-solving skills of human agents with the speed and data-processing capabilities of AI, resulting in superior customer experiences that can set a company apart from its competitors.
To differentiate themselves further, companies should focus on transparency and explainable AI. As AI becomes more prevalent in CX, it’s essential to build trust with customers by clearly communicating how their data is being used and how AI-driven decisions are made. This approach not only addresses growing privacy concerns but also demonstrates a commitment to ethical AI practices, which can be a significant differentiator in today’s market.
CIO&Leader: What challenges do organizations face when using predictive and generative AI (GenAI) to enhance customer loyalty? How can these challenges be overcome?
Deepak Visweswaraiah: One of the primary challenges organizations face is ensuring the quality and consistency of data used to train AI and ML models. For AI and ML algorithms to deliver excellent results, the data fed into them must be consistent and have minimal errors. The data fed into the system needs to be relevant to produce well-balanced outcomes. Furthermore, Gen AI systems also face a significant challenge when they fail to comprehend questions correctly, which leads to misinterpretations.
Additionally, organizations with legacy systems struggle with data silos where valuable information is isolated in different departments or systems. This fragmentation makes it difficult to access and integrate data for comprehensive AI analysis. It is also crucial for them to navigate complex privacy regulations and ethical considerations while using predictive and Generative AI. To overcome these challenges, organizations should implement robust data governance policies, including data cleansing and validation processes. They should also invest in data quality tools and regular audits to maintain high standards of data integrity.
To confidently deploy their AI models of choice in a responsible and governed way while minimizing risk, companies should incorporate auditing, rules-based governance, and workflow-managed human approval to advance safety, security, and reliability. This involves not only technological solutions but also organizational changes, a commitment to data quality and ethics, and an ongoing investment in skills and knowledge. As AI continues to evolve, organizations that can successfully navigate these challenges will be well-positioned to create deeper, more meaningful relationships with their customers.
CIO&Leader: Despite the hype around AI, some reports highlight issues such as inaccurate data and negative ROI, where technology costs outweigh the savings, alongside a lack of consumer trust. How can organizations navigate and overcome these challenges to ensure successful AI implementation?
Deepak Visweswaraiah: The foundation of successful AI implementation lies in high-quality, accurate data. Organizations must prioritize data governance and management strategies to ensure the integrity of their data. It is vital to focus on creating a data-driven culture where employees understand the importance of data accuracy and are trained in proper data handling practices.
To address the concerns about negative ROI, there is a need for organisations to take a measured, targeted approach to manage costs effectively and demonstrate tangible benefits, leading to a positive ROI. It starts with clearly defining the business objectives and identifying specific use cases where AI can add the most value.
Successful AI implementation is an ongoing process. Organizations should establish mechanisms for continuous monitoring of AI performance, regularly assessing its impact on business objectives and customer satisfaction. They should focus on creating a cohesive AI strategy that aligns with overall business goals and demonstrate tangible benefits to customers, such as improved service quality or more personalized experiences.