In today’s fast-evolving digital landscape, staying ahead requires an acute understanding of emerging technologies and their transformative potential. Kanchan Ray, Chief Technology Officer at Nagarro, shares his perspectives on the key trends shaping the future of customer engagement, the evolving role of AI and machine vision across industries, and Nagarro’s pioneering concepts like Boundaryless Architecture. He also delves into the critical challenges businesses face in implementing advanced cybersecurity frameworks and the transformative impact of AI avatars and deepfakes on customer interactions.
Some edited excerpts:
From your vantage point, what are the key emerging trends that will reshape customer interactions and redefine business strategies in the next five years?
The next few years are poised to witness significant shifts in customer experience, driven by advancements in technology and changing consumer expectations. Key trends include:
Hyper-personalized experiences using data and Generative AI: Leveraging AI and data analytics, businesses will be able to offer highly personalized experiences, delivering tailored content and recommendations that cater to individual preferences. This approach focuses on a “persona of one” rather than broad customer segments, enabling businesses to meet unique customer needs more effectively.
Invisible UX: The rise of voice and conversational interfaces, powered by AI, is transforming customer interactions. Voice assistants and AI-driven chatbots are providing more natural, efficient, and human-like interactions, automating numerous use cases and enhancing overall customer service.
Omnichannel customer experience: Companies are increasingly adopting omnichannel strategies to ensure engagement across both online and offline platforms. From brick-and-mortar stores to social media and streaming services, businesses aim to provide consistent and cohesive experiences wherever their customers are.
Personalized loyalty: Brands are using gamification as a way to engage with customers and create personalized loyalty programs. Moreover, rewards are evolving beyond traditional points or cashback, extending to innovative options like NFTs and social badges.
All companies must ensure data privacy for customers while creating a personalized experience. Customers expect transparency in how their data is collected, stored, and used, and they need assurance that their information is secure. It is important for companies to share usage and privacy intentions with crystal clear messaging so customers can better understand the why & how – why the data is being used and how their data is benefiting their experience.
The metaverse has sparked both excitement and skepticism. In your view, is it evolving into a mainstream platform, or are we witnessing a decline in its relevance?
The metaverse has witnessed fluctuating adoption rates over the past few years. As of 2024, it has over 600 million active users worldwide, with higher adoption rates in regions like Asia, MENA, and Europe compared to the United States.
We see the metaverse evolving in two primary directions – Consumer Metaverse and Enterprise Metaverse. On the consumer front, we are witnessing a surge in interesting use cases across gaming, immersive shopping, collaborative mall experiences, as well as educational and experiential learning applications. On the enterprise front, there is growing interest in leveraging hyper-immersive technologies to create virtual product experience centers, host virtual events, and enhance customer services. Organizations are investing in interactive learning solutions powered by simulations and immersive copilots within the metaverse, driving engagement and operational efficiency. There is also growing interest in immersive human-machine interactions through Digital Twins, maintenance, and inspection solutions—areas where Nagarro offers cutting-edge accelerators.
What advancements in computer vision and AI do you think will redefine how businesses analyze and utilize video data?
Vision and data derived from videos have become integral to numerous industries, with machine vision playing a crucial role in automating business processes. For instance, automatic inventory management, often supported by robots, is transitioning from experimental to mainstream. Machine vision also enhances security and safety by replacing human monitoring with machines that operate around the clock, offering greater accuracy at a lower cost.
On the consumer front, virtual try-ons and AI-assisted mirrors have become standard features in reputable retail outlets, both in physical stores and online platforms. In the healthcare sector, machine vision is transforming patient care through advanced medical image analysis, such as interpreting ultrasound and MRI scans, leading to faster and more accurate diagnoses and treatments tailored to individual needs. Manufacturing industries have widely adopted computer vision across their factories to streamline operations and enhance productivity. Similarly, banks and financial institutions leverage machine vision for remote KYC processes, improving accuracy, eliminating human biases, and drastically reducing costs.
At Nagarro, we worked with a major automobile client in India on a successful machine vision technology use case aimed at standardizing car pricing. In the used car market, dealers often offer different prices. Machine vision helped us establish a consistent value. By simply capturing images of the car, the system could detect dents and assess their severity—whether it was a surface crack, a minor dent, or a need for panel replacement. It could also analyze engine sounds to evaluate the car’s health. Based on the car’s type and model, the system then provided an accurate pricing estimate.
Nagarro’s concept of ‘Boundaryless Architecture’ is intriguing. How does this approach expand cybersecurity measures to safeguard not only internal systems but also customers and suppliers?
Our concept of Boundaryless Architecture is driven by the rise of generative AI, the growing adoption of AI models, the increasing prevalence of automated AI assistants and bots, and the growing interconnectedness of the digital business ecosystem—including suppliers, partners, and value-added service providers. These developments have made responsible and secure AI more critical than ever.
Traditional boundaries of security, which once focused on standard data security, governance, and IT protocols, are now fluid and dynamic. The integration of AI, data analytics, and machine learning has created diverse contexts for output consumption, resulting in new business operations around model simulations and decision-making related to model pipelines. These operations include processes like model publishing, hyperparameter observability, and auditing model reasoning, all of which push the boundaries of AI responsibility.
If you look holistically, AI governance encompasses multiple facets, including explainability, bias detection, and security. Explainable AI involves the ability to backtrack and justify AI decisions, linking them to micro-level data, hyperparameters, data lineage, and model version attributes. Capturing and mitigating AI bias requires technical capabilities to test for bias, monitor its impact, and enable AI systems to unlearn biases, potentially in real-time.
Securing AI entails implementing robust security controls to ensure models access only authorized data as dictated by role-based access controls (RBACs) and data ACLs. This task becomes increasingly complex as LLMs and metadata often span multiple user roles. With AI-related data now getting vectorized, ensuring security across data layers, business ontologies, and MLOps frameworks is a critical expectation.
AI governance requires 360-degree digital observability engineering, supported by a boundaryless architecture that fosters seamless integration across systems and processes. This includes monitoring and managing components such as data lakes, data pipelines, MLOps, APIs, microservices, model explainability, LLM-related vector databases, and prompt-engineering workflows. This is in tune with what we are doing at Nagarro, where, by breaking down silos and enabling interconnected operations, boundaryless architecture ensures transparency, reliability, and security in an evolving AI-driven landscape.
What are the potential risks and challenges in implementing such a wide-ranging cybersecurity framework, and how are they mitigated?
While businesses implement cybersecurity frameworks, the usual risks such as human error, resource constraints, and insider threats always remain. These challenges require the establishment of robust processes, tools, and safeguards to minimize vulnerabilities. Poor implementation of these measures can lead to significant security risks. Beyond these, there are several critical aspects businesses must focus on to ensure the effectiveness of their cybersecurity frameworks.
One crucial consideration is the complexity of the organization. For large, global, and diverse enterprises, implementing a security framework without disrupting business operations is a formidable challenge. Modular frameworks that allow phased implementation can help address this complexity. The same applies for scaling the security solutions. Another key aspect is the integration of security solutions with existing systems. Ensuring compatibility with legacy infrastructure is critical, as deviation can create vulnerabilities and disrupt operations.
Additionally, businesses must stay vigilant against the constantly evolving threat landscape. Cyber threats change rapidly, and static security frameworks can quickly become obsolete. Establishing agile processes that facilitate both scheduled and ad-hoc updates is crucial to maintaining up-to-date defenses. Incorporating AI into cybersecurity frameworks is becoming increasingly important as well. AI-driven tools can enhance threat detection capabilities by leveraging predictive analytics and adaptive defense mechanisms. These technologies enable organizations to identify and respond to threats more proactively and effectively, strengthening their overall security posture.
The boundaries of responsibility and governance are becoming increasingly blurred in this new era of digital transformation. Governance now extends to include external stakeholders such as suppliers, partners, and third-party applications. Consequently, a well-designed governance layer cannot be an afterthought; it must be an integral component of digital architecture.
How do you see AI avatars, chatbots, and deepfakes influencing customer engagement, and what safeguards should organizations implement against misuse?
AI is taking avatars to the next level, delivering personalized interactions and significantly augmenting the overall user experience. These AI-powered avatars adapt their tone, language, and messaging in real time based on user preferences and behavior patterns. Businesses are leveraging these avatars in various innovative ways:
Humanizing customer support: One of the key advantages of AI avatars is their ability to humanize customer support experiences. Unlike traditional NLP-based chatbots or automated responses, these avatars engage in human-like, contextual conversations, providing a more personalized and engaging support environment. This leads to improved customer satisfaction and higher Net Promoter Scores (NPS). Additionally, AI avatars offer 24/7 availability, making them ideal for businesses with a global customer base. Unlike human support representatives who may experience fatigue or inconsistent performance, AI avatars maintain consistent, high-quality service at all times.
Immersive experience: AI-based avatars also enable highly interactive and immersive experiences, supporting multiple languages and operating seamlessly in virtual environments such as stores, gaming platforms, and metaverses. In online or offline retail settings, they can enhance customer interactions by offering features like virtual try-ons, significantly augmenting the shopping experience.
Employee training: Businesses are using AI avatars to train customer-facing employees through simulation environments. These avatars help create realistic scenarios for product demonstrations or onboarding processes, enabling employees to practice and adapt to diverse situations effectively.
Emotional engagement: By analyzing user sentiment in real time through voice tone and facial expressions, AI-based avatars can tailor their responses to reflect emotional understanding. This ability to demonstrate emotional intelligence helps develop deeper connections with users, ultimately boosting customer satisfaction. Although deepfake technology often receives negative attention, businesses are finding creative ways to use it constructively. Hyper-realistic deepfake avatars can be used to create engaging marketing campaigns, onboard employees in lifelike virtual environments, and even develop innovative content for entertainment.
The most common safeguards businesses are implementing include training employees and customers on fraud prevention, as deepfakes can impersonate individuals and cause significant damage. Data privacy awareness is crucial, particularly since chatbots and avatars collect extensive text and behavioral data from customers. To ensure compliance with data protection laws such as GDPR, businesses should focus on encrypting data and limiting its collection to what is absolutely necessary.
Another important safeguard is addressing bias and discrimination, as AI avatars and chatbots can unintentionally reflect biases present in their training data. Establishing a robust AI governance framework and regularly auditing AI systems for bias are essential steps in mitigating this risk. Lastly, given that AI-driven systems can be vulnerable to cyberattacks, businesses must implement strong security measures. Regularly updating and monitoring AI systems is key to preventing potential exploitation and safeguarding