Building an AI ecosystem is key to creating value: Raja Mansukhani, Comviva

Artificial intelligence and cloud computing dominate today's enterprise tech landscape, prompting companies to develop platforms with increased scalability, security, and AI integration. Comviva, a global software provider, is actively shaping this transformation with its “Comviva 2.0” strategy. 

In a conversation with TechCircle, Raja Mansukhani, Chief Strategy, Technology & Transformation Officer at Comviva, explained the company’s strategic focus on AI as a core driver of transformation. Mansukhani emphasizes Comviva’s AI-first approach, which spans product innovation, engineering automation, AIOps, and cybersecurity, and more! 

Edited Excerpts: 

What does Conviva’s 2.0 vision mean for the company, and why introduce it at this stage?

Our transformation began with a clear purpose, to use AI as a differentiator, not a trend to follow. Earlier, we evolved with market demand and predictable technology cycles. AI changed that by creating parity between new and established players, giving everyone an equal chance to innovate.

Conviva 2.0 was built around this opportunity. As a software product company with a large base of technologists, our AI-first strategy focuses on value creation for clients and value capture for the organization. Customer-centricity and innovation drive this approach, with innovation treated as a core operational pillar.

We built our framework around four focus areas. The first is product differentiation, becoming an AI-native product company that leverages AI to leap ahead in the market. The second is engineering excellence, where AI strengthens our software development lifecycle and enhances customer experience. The third is AI-driven operations, or AIOps, which helps us automate traditional support functions, predict issues, and make systems self-healing. The fourth is cybersecurity, where we apply AI for early threat detection and proactive defense.

Across these blocks runs a unified goal: AI for all and AI first. This extends beyond technology teams to HR, finance, and other functions. We launched the ATC framework, awareness, training, and competency, to upskill employees across levels. About 50–70% of our workforce is targeted for AI awareness and training, ensuring organizational readiness.

AI adoption has also driven cultural transformation. We treat AI as a partner in change, focusing on mindset and collaboration instead of resistance. This program helps embed AI into every process, product, and decision-making layer.

Today, AI has become a default expectation across our FinTech, MarTech, RefTech, and DigiTech product verticals. Our early investment is delivering results, particularly in developed markets, while we continue strong growth across Africa, the Middle East, and Asia.

Our modular, non-monolithic product architecture and technology depth give us an edge against larger competitors. AI remains at the core of our transformation journey, a foundation for innovation, differentiation, and growth.

Many enterprises claim to be AI-first, but few are scaling it effectively. From your vantage point, what’s driving the biggest disruption in how industries like telecom and fintech create value today?

Agentic AI is becoming the key disruptor in this space, surpassing traditional production and service models. The technology is still maturing but shows significant potential to enable autonomous decision-making across industries. Building an effective AI ecosystem, however, takes time and discipline. Quick experiments with tools like ChatGPT can deliver instant outputs, but developing enterprise-grade capabilities demands focus on data quality, infrastructure, and governance from the start. These early steps define the maturity and outcomes of agentic AI applications. Many market leaders are investing heavily in this area, recognizing its impact, yet the journey remains iterative, requiring both patience and persistence.

How do you decide where generative AI adds real value across your platforms—FinTech, DigiTech, RevTech, and MarTech—and where it risks becoming just hype?

We apply GenAI and agentic AI across our ecosystem to address specific business and technology needs identified through customer feedback, market demand, and anticipated future requirements.

As an AI-native company, our work spans four areas — product innovation, engineering, AIOps, and cybersecurity. For example, in our RevTech communication platform, we use GenAI to detect and reduce fraud and termination losses by automating risk detection through in-house AI models.

Our AI Centre of Excellence enables organization-wide adoption by coordinating efforts across product teams and integrating external learnings. Another product, MRTM Mobilitex for customer value management, uses GenAI to generate campaign insights and anticipate demand at a country or operator level, combining AI and ML for personalized outcomes.

In engineering, we improve quality and efficiency using AI-enabled tools such as GHCP, Claude, and Cursor.ai. These tools automate tasks like code generation and review, allowing engineers to focus on architectural and complex problem-solving.

For AIOps, our Mobilitex deployment for a large African telecom uses GenAI to predict system failures and provide prevention recommendations before issues occur.

In cybersecurity, we integrate AI during code creation to identify vulnerabilities early, using GitHub Enterprise, GitLab Duo, and Amazon Q. This “shift-left” approach enhances enterprise-level security by embedding checks at the start of the SDLC.

Beyond technology, AI also supports recruitment automation, financial and procurement analysis, and R&D for market insights, significantly reducing time and cost in these processes.

How do AIOps, intelligent orchestration, and observability help achieve zero-touch automation, and how far are you in that journey?

We have gamified our internal AI performance tracking with daily leaderboards to measure progress. Our current accuracy benchmark stands at 70%, with a target of 90% focused on customer experience.

This system works through a continuous AIOps module that observes, analyzes, anticipates, and recommends actions. For example, in the BSS stack, it predicts potential order fallouts—such as when a customer purchases a bundled iPhone plan—and suggests preventive measures. If a failure occurs, the system self-corrects through automated resolution.

In support operations, we are automating L1 and L2 functions using AI to address customer queries directly within their ecosystem. Traditional ticketing processes with long turnaround times are being replaced by real-time AI-driven responses, improving efficiency and reducing costs for both clients and internal teams.

We are progressing toward 90% accuracy, building adaptive models that evolve with new scenarios and product versions. The AI layer is designed to operate across all business lines, ensuring consistent intelligence throughout our product suite. The system continues to evolve to remain effective and sustainable.

How do you balance real-time performance with cost efficiency across large-scale environments like telecom, digital payments, and marketing?

For any tech organization, the key priorities at the start are data quality and infrastructure planning. These must align with a cultural shift and leadership commitment to drive change.

Anticipating data volumes is critical, which is why partnerships with hyperscalers and the adoption of SaaS and cloud-native technologies are essential. Cloud infrastructure enables modular, scalable, and secure systems. While some customers and countries require on-premise or in-country private cloud setups due to data regulations, most infrastructure planning now centers on cloud-based models.

The company works closely with AWS and Microsoft to ensure scalability and optimized cloud architecture across its operations. In new markets, SaaS deployment has become a default due to its cost efficiency, scalability, and security, marking a shift from traditional to cloud infrastructure.

As AI and SaaS become more embedded in critical industries, how do you build security and compliance into product engineering from the start?

We are focusing on integrating AI into the early stages of our software development process to enhance security. The goal is to address vulnerabilities, threats, threat modeling, and compliance requirements from the start of coding rather than at the end, where traditional security testing like penetration testing usually happens.

Through our partnership with Microsoft and the use of GitHub Enterprise, we are embedding AI tools directly into the development environment. This setup helps identify potential threats and bugs in real time as code is written. We are investing in this approach to ensure only issues that AI cannot capture move further down the development pipeline. A proof of concept using this AI-based process has recently been completed.

What emerging technologies beyond AI and cloud will shape Comviva’s platform roadmap in the coming years—are you focusing on areas like edge computing, federated AI, or quantum security?

We are a software product company building solutions across multiple technology domains. As quantum computing becomes commoditised, we expect major improvements in processing power, AI-driven interactions, and the ability to handle complex tasks. Our approach is to build value through an interconnected ecosystem of products.

In FinTech, we offer wallet and lending solutions. On the MarTech side, we provide customer lifecycle management platforms covering loyalty and rewards. In Digitech, our product supports catalog and order management. In RefTech, we deliver communication platforms such as CPaaS, NPAaS, and API marketplaces. Together, these solutions form an ecosystem relevant to any enterprise.

We recently implemented this model for a large non-telco client in the US, combining products through a shared AI layer to increase client and organisational value. Our technology roadmap focuses on agentic AI, as it plays a central role in the next two to three years of innovation. Alongside, we track developments in federated AI and quantum computing, which are progressing toward commercial maturity and will reshape how industries operate.

---