CIOInsider India Magazine
Architecting the Future: Scaling AI in the Modern Enterprise
Rajan is an experienced Chief Executive Officer with a demonstrated history of working in the management consulting industry. He is a strong business development professional with a Master of Business Administration (MBA) focused in Business Administration, Management and Operations from Indian Institute of Management, Calcutta.
While big data becomes increasingly prevalent in organizations globally, coordinating processes, personnel, and expertise presents significant challenges, particularly in today's artificial intelligence era. Despite business leaders acknowledging data's significance, there exists a clear disconnect between recognition and actual implementation of AI-powered data utilization in their operations. With data-centric solutions continuing to revolutionize companies across the globe, Rajan Sethuraman, Chief Executive Officer of LatentView Analytics, guides us through existing obstacles in corporate data frameworks, strategies for cultivating data-focused organizational cultures, and the impact of evolving data technologies on future customer interactions. He clarifies the underlying causes of these critical issues and provides guidance on how organizations can achieve strategic outcomes through their data programs.
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As enterprises scale their AI and GenAI initiatives, what are the most significant challenges you come across? What potential bottlenecks are foreseen in the current data architecture?
One of the most significant challenges we see in scaling AI and GenAI initiatives for Fortune 500 companies is the transition from proof-of-concepts to production. The bottleneck isn't the model itself; it’s the lack of a scalable, secure, and governed data foundation. A great GenAI model on a shaky data foundation is a non-starter.
The current data architectures of large enterprises, which were built for traditional business intelligence, are not equipped to handle the demands of next-gen AI. And the primary bottleneck is data silos. Information is often trapped in disconnected systems such as CRM, ERP, and marketing platforms, making it impossible to create the unified, high-quality data source that GenAI requires for context-aware responses. This fragmentation prevents a holistic view of the customer or business operations. Ensuring data quality, security, and compliance across these fragmented systems is another complex undertaking, leaving the risk of an AI model hallucinating because of poor data.
The one aspect that enterprises ignore in solving these challenges is data engineering, which addresses the foundational requirements for large-scale AI deployment by focusing on building a unified data fabric with a semantic layer on top that adds a business-friendly context to the data so it can be used by employees at all levels.
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What is the strategy for creating a truly data-driven culture, and how do you measure its success beyond just implementing analytics tools?
A data-driven culture is first about embedding data into the DNA of the organization. The strategy goes beyond technology and focuses on people and processes. As a first step, enterprises need to empower employees to ask questions and find insights. This means providing accessible tools, offering training in data literacy, and fostering a culture where data is a shared asset, not just a technical function. Second, the onus is on the
leadership. The C-suite leaders must be vocal champions of data. They should use data to inform their own decisions, ask data-backed questions in meetings, and measure their growth with data-driven insights. This approach helps shift from using data to explain what happened to using it to predict what will happen and proactively take action, a must in the agentic AI era.
To measure the success of this culture, it is important to look for tangible business outcomes, such as whether teams are making faster decisions or if there is a quantifiable impact, for instance, an increase in customer lifetime value from personalized recommendations. Tracking employee engagement and adoption of AI-powered data tools are other aspects to it.
How do you help your clients strike a balance in investing in cutting-edge technologies like AI while maintaining and optimizing existing data infrastructure?
Instead of chasing the latest tech, we ask organizations to start with the business problem. The choice of approach, technology, usage of AI/ GenAI should be determined more by the nature of the problem. Not every challenge requires the most advanced model — often the simplest, well-governed solution delivers the most value. Otherwise, you risk falling into the trap of a problem in search of a solution rather than the other way around.
The most successful enterprises will be those that design hybrid systems where humans and AI complement each other.
For those starting out in their digital transformation journey, what helps is having clarity in what they want to achieve. Today, when thinking about AI tools, the major investment has to be on maintaining and optimizing the current infrastructure. The gains of this part can be used for developing and adopting next-generation cutting-edge technologies that can spur innovation.
What are some emerging data technologies that could have the most potential to
transform the customer experience overall?
I see a profound shift underway in how data will shape the future of customer experience. We’re moving beyond static dashboards and rule-based personalization into a world where autonomous, data-driven systems can sense, decide, and act in real-time. The emergence of Agentic AI is particularly exciting because it fundamentally changes the cadence of customer engagement. We are no longer waiting for insights to be analyzed and acted upon by teams. Instead, intelligent agents will continuously interpret signals across the customer journey, predict intent, and take proactive actions, whether that’s preventing churn, optimizing a recommendation, or even dynamically adjusting pricing and offers.
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However, for this to happen at scale, companies must break free from data silos. This is where semantic layers and modern data fabric come in, bringing together information from different sources to create a single, connected view of the customer. When paired with privacy-preserving techniques, this unified intelligence enables hyper-personalized experiences while staying compliant with regulations and respecting consumer trust, creating a critical balance as people become more aware of how their data is used.
What could be the biggest challenges for data analytics industry leaders in the next few years? How should they prepare the team and strategy for those changes, in your
opinion?
The next few years are a critical inflection point when GenAI and agentic AI will fundamentally reshape the way enterprises use data to drive decisions. The challenge will be about deploying autonomous, decision-making AI agents that can take intelligent, context-aware actions across functions, supply chain, finance, innovation, and customer experience, while staying aligned with enterprise strategy and ethical boundaries. This shift brings immense opportunity but also significant complexity.
The velocity of innovation will demand rapid adoption of GenAI-driven automation to stay competitive. On the other hand, leaders must ensure governance, transparency, and trust are embedded so these agents don’t become unregulated “black boxes”. A big aspect of resolving the emerging complexity will be around where the human in the loop plays a critical and necessary role. GenAI/ AgenticAI are enablers rather than an end in itself. The most successful enterprises will be those that design hybrid systems where humans and AI complement each other.
The talent ecosystem will also evolve dramatically, requiring not just engineers and data scientists but also AI ethicists, domain strategists, and hybrid leaders who can bridge technical innovation with business outcomes and compliance.
