In today’s rapidly shifting technology landscape, enterprise systems such as ERP and CRM are no longer just transactional engines—they are becoming the foundation for agility, intelligence, and growth. Rajesh Pawar, Managing Director at Riveron, believes the true challenge lies not in the technology itself but in how organizations adopt and evolve with it. In this conversation with CIO&Leader, he shares insights on the AI-powered transformation of enterprise systems, the rise of dynamic business intelligence, the growing importance of cybersecurity, and India’s emergence as a global hub for innovation and talent in digital transformation.
Managing Director
Riveron
CIO & Leader: Enterprise systems, such as ERP and CRM, often become bottlenecks in digital transformation. How do you see emerging technologies reshaping these systems to be more agile, intelligent, and future-ready?
Rajesh Pawar: From my perspective, technology is never the real bottleneck—it’s how it’s adopted. Bottlenecks occur when organizations fail to keep pace with the times. For example, many organizations still run ERP systems implemented nearly a decade ago, while the world has transitioned from on-premises to cloud-based and now AI-enabled platforms.
The reasons can vary—inertia, cost, or adoption challenges—but the real issue lies in the decision-making process, not the tool itself. If you have a clear vision with defined short-term, medium-term, and long-term goals, you can address these bottlenecks in a structured manner.
AI today is embedded everywhere—from personal apps like Uber to enterprise workflows. In finance, it can highlight customer risks; in operations, it powers forecasting and data visualization. Enterprise systems, such as Salesforce, now proactively suggest customer preferences, creating a better overall experience.
AI itself is evolving too, moving from human-triggered automation to reasoning systems that preempt what you need. These small but powerful applications are reshaping day-to-day business operations.
CIO&Leader: AI is moving from pilots to production across industries. What are the most impactful AI use cases you’re seeing in the office of the CFO and CIO today, and what challenges remain in scaling them?
Rajesh Pawar: What excites us is how quickly the role of the CFO and CIO is evolving with technology. It began with automating AP processes to eliminate redundant tasks, then moved to reminders for collections and transaction planning. Today, we’re seeing far more intelligent use cases—prompt-based queries, such as, “What does my business look like under current economic conditions?” AI models can now incorporate geopolitical trends and historical sales data to provide real-time predictive insights.
This takes us beyond operational efficiency to forecasting and business modeling. Decisions that once required months of analysis can now be made today with the aid of predictive models. Accuracy will only improve: what may be 50% accurate today could reach much higher levels within 12–18 months as systems refine themselves on more data.
The first challenge is clarity. AI is often confused with machine learning, automation, or agents—terms that are not interchangeable. CFOs don’t need to get technical, but they should understand applications. Consider travel: an autonomous agent could plan and book an entire five-day trip to Delhi based on user preferences, while a simpler one presents curated options. Once leaders see these examples, they can extrapolate how AI can be applied to their business.
The second challenge is defining the AI journey. AI is powerful, but it needs a plan: understanding your current state, ensuring data readiness, identifying short- and long-term goals, and starting with low-hanging fruit to achieve quick wins. With a clear roadmap, organizations can scale AI meaningfully rather than piecemeal.
CIO&Leader: With data volumes exploding, how are enterprises leveraging business intelligence differently now? What role do AI-driven insights play in enabling faster and more accurate decision-making at the executive level?
Rajesh Pawar: With the advent of systems specializing in business processes, things have changed significantly. If I look at the evolution of applications over the last 15–20 years, we’ve gone from completely manual processes to siloed applications—an AR application, a core finance application, a CRM application, each working independently.
From there, we transitioned to horizontal products that covered all aspects of the business, albeit with moderate depth across various process areas. Now, we’re seeing best-in-class products that focus intensely on particular business functions, with integration layers enabling them to work together seamlessly.
AI systems embedded within BI tools are taking this further. Earlier, BI relied heavily on static, historical data—such as factoring in seasonality, knowing that June would be a big month. Today, it’s far more dynamic. Depending on the datasets and inputs, AI augments BI by detecting patterns, enabling scenario forecasting, and even providing prescriptive recommendations.
For example, in large companies with thousands of vendors, an AI tool can instantly determine procurement strategies: if one vendor’s lead time is too high, which alternate vendor can deliver on time? These kinds of permutation-combination exercises once required days of manual work; now, AI can resolve them in minutes.
As systems evolved, data availability exploded. Earlier, each system had its own reporting function. Now, BI systems sit on top of all these layers. Concepts such as data warehouses and data lakes emerged to help compute and consume vast volumes of structured and unstructured data.
This is where robust BI tools embedded with AI have accelerated growth. Systems are transitioning from static dashboards to dynamic ones—such as heat maps that display real-time sales performance. For example, I can now view product sales across all states in India: high-performing states are marked in green, while low-performing ones are marked in red. Behind the scenes, multiple systems compute transactions, while AI identifies what’s moving, what’s not, and why. This ability to visualize, analyze, and act in real time is the power of embedded BI with AI.
CIO&Leader: As enterprises adopt cloud-first strategies and AI-driven automation, how should they rethink cybersecurity and risk management in mission-critical ERP and CRM environments?
Rajesh Pawar: Cybersecurity and risk management become very critical when we talk about the cloud. The first significant change from on-premises systems is that your data no longer resides within office premises—it’s now in the cloud, whether private or public, depending on the product you choose.
For enterprise applications—be it CRM, ERP, or others—the bedrock today is security. That’s what matters most as organizations move further into the cloud: how secure is my data? Transaction processing is now a given; these systems have evolved over the past decade. But if I’m moving to the cloud, the robustness of security will be one of the deciding factors.
That’s why platforms today are built on zero trust principles, identity-first access, and continuous monitoring. The goal is simple: to provide the correct information to the right person at the right time. SOP compliances, anti-phishing protocols, and other safeguards are now non-negotiable.
AI is being embedded not only into core products but also into security. Systems now use AI for threat detection and anomaly monitoring to preempt risks. Securing data centers is part of the offering, even if the end user never sees it. From physical security to technology safeguards, AI is enhancing monitoring and enabling automated responses that strengthen risk management and mitigation.
Most ERP and CRM systems store sensitive data, making compliance mandatory, as mandated by regulations such as MCA, GDPR, and others. These applications are designed with security features to operate across geographies and industries, ensuring data is breach-proof and protected against unauthorized access. As AI models themselves become targets of influence, embedding countermeasures at the application layer has become absolutely crucial.
CIO & Leader: India, particularly Pune, is becoming a central hub for global innovation efforts in AI and digital transformation. What unique strengths does India bring to the worldwide tech landscape, and how do you see this role evolving?
Rajesh Pawar: If we look at how India’s IT landscape has evolved, it started as being largely service-oriented. For a long time, India was the back office for global enterprises. But over the last 5–6 years, I’ve seen a fundamental shift.
India has a robust engineering and technology talent pool. We can develop high-quality code at lower costs, which has enabled us to move from a service-driven model to an IP and product development ecosystem. The rise of AI and tech startups across India, as well as the investments global tech giants are making in their product development centers here, clearly signal this transition.
If you look at our digital public infrastructure—systems like Aadhaar or UPI—they’re now being studied and even adopted by other countries to streamline their own operations. The fact that these platforms run so seamlessly in a country with 1.4 billion people, where a considerable portion actively uses them, speaks volumes about India’s native capability to design and scale world-class products.
CIO&Leader: Talent shortages, especially in AI, cybersecurity, and cloud, are often cited as barriers to digital transformation. How can enterprises reimagine their talent strategies — including reskilling, automation, and new operating models — to overcome this challenge?
Rajesh Pawar: Talent shortage—especially in AI—is a real challenge. Technology is advancing much faster than academic institutions can keep pace. Every six to twelve months, a new product emerges, and it’s nearly impossible for the education system to update curricula at that speed.
Talent gaps, however, can be mitigated in two ways. First, we are integrating these evolving platforms into our education ecosystem. When I was in college, there was no degree in analytics or AI. Today, my son studies these subjects formally, which shows that education is catching up at a fundamental level.
Second is what organizations themselves are doing to improve workforce skills. You may hire fresh graduates or lateral talent with specific skills, but continuous reinvention is essential. That’s why we invest heavily in training, reskilling, and cross-functional programs to ensure our talent pool remains aligned with market needs.
I look at it as the “three I’s”: Initiate, Incentivize, and Install.
- Initiate programs by making training platforms easily accessible.
- Incentivize employees to upskill, shifting the mindset from “I’m losing my core skill” to “I’m adding new capabilities.”
- Install the new skills into day-to-day operations so employees see direct impact on efficiency.
At Riveron, as we embed AI into our external offerings, we also utilize AI-driven processes internally—whether in finance or delivery. This builds credibility because employees and clients know we practice what we preach.
On India’s global reach: post our acquisition by Riveron, Pune has become our global capability center. We’re expanding our tech footprint here by adding people with expertise across various technologies. The reasons are clear—India offers a strong engineering talent pool fresh out of colleges, combined with cost advantages, which makes building sustainable offerings possible.