AI is no longer an emerging technology—it’s a boardroom mandate. Across industries, enterprise leaders are under growing pressure to demonstrate results, not just run pilots.
AI is no longer an emerging technology—it’s a boardroom mandate. Across industries, enterprise leaders are under growing pressure to demonstrate results, not just run pilots.
By the end of 2024, more than 78% of Indian enterprises had implemented AI in at least one function, reflecting rapid adoption, according to McKinsey.
“People didn’t believe in AI until they saw it. But once they did, the conversation changed from ‘can it do this?’ to ‘can it do more? Now they ask, ‘Can it do more?'” says Vinod Khode, Group CIO at Varroc Engineering.
However, a global study by BCG reveals that 74% of organizations continue to face significant challenges in scaling AI beyond initial pilots. This highlights a critical gap—not in willingness to adopt AI, but in the ability to operationalize it across business functions.
For Indian CIOs, the challenge is not about experimenting with AI—it’s about scaling it across complex, legacy-laden environments, aligning it with business goals, and ensuring it’s both responsible and resilient. The real test lies in moving AI from innovation labs and test beds into the operational core of the enterprise.
For enterprise CIOs, the transition from experimentation to enterprise-wide execution remains one of the most complex and strategic challenges, requiring alignment of people, processes, data, and infrastructure to deliver sustained business value. However, research from Accenture highlights that organizations that have managed to scale AI effectively can outperform their peers by as much as 2.5X in both revenue growth and productivity.
We look at how leading CIOs in India are addressing these challenges. From manufacturing and digital commerce to automotive and engineering, these leaders are reimagining AI as an integrated, measurable, and secure business enabler. Their experiences provide a practical roadmap for enterprise technology leaders who are ready to move beyond experimentation and deliver enterprise-scale impact.
Moving from Ideas to Impact
Successful AI transformation begins with strategic intent, not pilot enthusiasm.
Turning AI pilots into enterprise-scale success requires more than technical capability—it demands clarity of purpose. Moving from ideas to impact begins with defining what success looks like and ensuring every use case is tied to business value. Strategic intent—not experimentation—must lead the way.
CIOs are realizing that without top leadership alignment and measurable outcomes, AI risks remaining a lab experiment. As organizations mature, they focus on achieving early wins, building executive buy-in, and preparing their teams for what comes next: designing the proper infrastructure to scale AI confidently, reliably, and with continuity across all functions.
Vinod Khode started with executive education before moving on to deployment. He invested early in change management, ensuring cultural readiness across teams.
Each use case should be mapped to specific business outcomes, such as cost reduction and increased productivity. “If it’s not aligned to business, it won’t earn trust—or budget,” Vinod Bhat of Tata AuoComp explains. “You don’t walk into the AI Olympics. You prepare for it, years in advance,” advises Bhat
Like many Indian enterprises, Varroc’s AI journey began with cautious curiosity. “We started with educating a focused core team,” Khode explains. Rather than rushing into implementation, the team started with focused group discussions, supplemented by consultations with Gartner to learn how similar organizations worldwide are applying AI, from operations and quality to HR.
A clear strategy and organizational readiness are essential to move AI beyond isolated pilots. When initiatives are aligned with business outcomes and supported by informed leadership, they are more likely to progress toward scale. The next step is building a foundation that enables reliable, secure, and scalable AI deployment across the enterprise.
Building the Foundation
AI requires a scalable and flexible architecture. After setting clear objectives, the next step in scaling AI is establishing a robust and flexible technology foundation. Scalable AI requires infrastructure that can support real-time data processing, seamless integration, and the ability to evolve with emerging technologies.
Modern AI workloads, especially those involving deep learning, rely heavily on high-performance hardware. GPUs (Graphics Processing Units) have become the industry standard for training complex AI models due to their parallel processing capabilities, which significantly reduce the time required for model development. On the other hand, CPUs (Central Processing Units), while slower for training, remain crucial for lightweight inference tasks and general-purpose computing. As enterprises move from training to deployment, many are adopting hybrid compute environments—leveraging GPUs for training and CPUs or optimized inference accelerators (like TPUs or FPGAs) for running models at scale across diverse workloads and environments. This strategic use of hardware ensures cost-efficiency without compromising performance.
Indian enterprises are prioritizing architectures that strike a balance between performance and adaptability, leveraging cloud-native environments, edge computing, and containerized services to enable agile deployment and scalability. These foundational choices not only accelerate the transition from pilot to production, but also prepare the organization for long-term growth. Once the infrastructure is in place, the focus shifts to managing the data layer effectively to support intelligent, scalable insights.
Indian enterprises are prioritizing architectures that strike a balance between performance and adaptability, leveraging cloud-native environments, edge computing, and containerized services to enable agile deployment and scalability. These foundational choices not only accelerate the transition from pilot to production, but also prepare the organization for long-term growth. Once the infrastructure is in place, the focus shifts to managing the data layer effectively to support intelligent, scalable insights.
At IndiaMART, CIO Nikhil Prabhakar has led the development of a future-ready technology architecture designed to support the evolving demands of AI at scale. The company has implemented a microservices-based framework, powered by Docker and Kubernetes, that operates within a resilient multi-cloud environment.
This approach enables greater flexibility, faster deployment cycles, and simplified maintenance. “Our architecture is modular and open-ended,” Prabhakar explains. “It’s designed in a way that allows us to integrate emerging AI innovations seamlessly, without having to overhaul or disrupt our core systems.” This foundation not only supports rapid experimentation but also ensures long-term adaptability as AI technologies mature.
At Force Motors, Group CIO Anand Deodhar has overseen the implementation of a hybrid edge-cloud architecture designed to meet the performance demands of AI-driven manufacturing. The company has deployed GPU-powered workstations at the edge to enable near real-time inference, reducing reliance on centralized cloud processing for time-sensitive tasks.
“We’ve built a hybrid edge-cloud architecture to support low-latency AI in production,” Deodhar explains. “From predictive maintenance to dynamic shift planning—latency matters.” This infrastructure enables AI applications to respond quickly to operational data, thereby enhancing decision-making on the factory floor and facilitating more efficient, intelligent production workflows.
A well-designed infrastructure enables AI systems to scale efficiently and adapt to changing business needs. With a flexible, modular, and performance-driven architecture in place, organizations are better equipped to support a diverse range of AI applications. The next phase involves focusing on data, ensuring it is reliable, accessible, and ready to drive intelligent outcomes.
Fueling Scalable Insights
Data readiness is a prerequisite. For AI to move beyond isolated success stories, enterprises must focus on data readiness, ensuring the right volume, quality, and governance of data is in place. This approach balances innovation with cost-efficiency and compliance.
Tata AutoComp has adopted a unified data strategy, incorporating real-time model monitoring and retraining frameworks, to ensure accurate and relevant outputs. “From clean pipelines to explainability audits—we’ve baked governance into the process,” notes Vinod Bhat.
At Varroc Engineering, Vinod Khode followed a leaner model by repurposing existing datasets and introducing automated labeling, enabling scale with control. As data systems mature, the focus naturally shifts to ensuring that AI insights can empower users across functions.
“Before acquiring new data, we repurposed internal datasets. Structured data governance and automated labeling followed,” says Vinod Khode.
A strong data foundation ensures AI models remain relevant, transparent, and effective over time. With governance, reusability, and retraining mechanisms in place, enterprises can scale insights reliably. The next priority is to integrate these insights into daily operations, ensuring that AI tools are accessible, practical, and applicable across all business teams.
Making AI Everyone’s Business
For AI to create value, it must embed into daily workflows. Once the infrastructure is in place, the next critical step is enabling AI systems with high-quality, accessible, and well-governed data. Scalable AI insights rely on continuous data flow, model retraining, and real-time feedback mechanisms to ensure relevance and accuracy.
CIOs are prioritizing unified data strategies, centralized governance, and clean pipelines to support sustained model performance across use cases. These practices enable AI systems to evolve in response to changing business needs, providing a reliable foundation for deployment. As organizations harness the full potential of data-driven intelligence, the next challenge lies in embedding AI into the daily decision-making fabric across functions and teams.
At Dixon Technologies, AI is integrated across various functions, including quality checks, inventory planning, and training. “Our AI-powered planning engine forecasts procurement needs, reducing material waste,” says Pradhan. “We’ve also deployed AI dashboards that empower shop floor teams to make real-time, data-backed decisions.”
Force Motors uses AI to inform shift planning and machine utilization. “We don’t treat AI as a separate project. It’s embedded into our plant DNA,” says Deodhar. “In manufacturing, success is when the machine learns—and then teaches the team.”
With structured, scalable data frameworks in place, enterprises will be better positioned to generate consistent and actionable AI insights. Ensuring model performance through ongoing retraining and clean data pipelines is essential. The next phase is integrating these insights into routine workflows—making AI accessible, usable, and valuable across business functions.
Measuring What Matters
According to Deloitte, most enterprises take over a year to achieve ROI from AI. The CIOs profiled here show that the right metrics can accelerate this curve. As AI moves into production, enterprises are shifting focus from experimental outputs to real-world outcomes—whether it’s improved customer experience, operational efficiency, or cost optimization.
IndiaMART, for instance, uses KPIs such as responsiveness, innovation impact, and security enhancements to evaluate AI initiatives. Force Motors links AI outcomes directly to production performance indicators, such as uptime and energy efficiency. With the right metrics in place, enterprises can accelerate ROI and scale with confidence
IndiaMART’s Prabhakar shares that their WhatsApp-based IM Insta bot tripled responsiveness, while PhotoSearch 3.0 reduced search latency across millions of products. “Every AI initiative is evaluated not by hype, but by impact: CX improvement, innovation, and security. GenAI and Agentic AI are not just ideas for us—they’re delivering measurable ROI,” says Prabhakar
Force Motors tracks AI’s contribution to uptime, throughput, and energy usage through machine performance scorecards. As AI becomes embedded in business operations, the next priority is to ensure that systems are not only effective but also trusted.
Measuring AI initiatives against relevant business KPIs enables organizations to track value, enhance decision-making, and support broader adoption of AI. When performance indicators reflect real impact, AI can scale with purpose. The next focus is to strengthen trust through transparency, governance, and responsible deployment across all layers of the enterprise.
Building Trust into AI
WIth AI becoming deeply embedded into business operations, trust is emerging as a non-negotiable foundation for scale. According to IBM, 85% of executives believe trustworthy AI is essential for business success. CIOs are responding by implementing robust governance frameworks to prioritize transparency, fairness, and security from the outset.
Varroc Engineering’s early AI pilots were grounded in solving practical business problems, helping build trust in the technology across teams. From a conversational BI tool that empowers leaders with instant insights to a 24/7 virtual HR assistant, and an AI guide for onboarding through PLM systems, each use case delivered clear, reliable outcomes. As employees experienced tangible benefits, confidence in AI grew organically, laying the foundation for broader enterprise adoption.
At IndiaMART, building trust in AI is a core priority. They have established a robust internal AI governance framework to ensure our systems are ethical, explainable, and free from bias or hallucinations, especially as they integrate Generative AI into key functions, such as lead verification and communication. IndiaMART is also advancing the convergence of AI and cybersecurity, using intelligent models that proactively detect threats, prevent fraud, and mitigate abuse before it affects users.
At Maruti Suzuki, the foundation of effective and trustworthy AI lies in clean, structured, and well-labeled data. Whether leveraging small or large language models, the focus remains on aligning high-quality data with clear business objectives. Each AI deployment is guided by robust governance frameworks designed to mitigate risks such as hallucination and bias. By prioritizing strong data management and embedding safeguards at every stage of development, the organization aims to ensure that AI systems are not only intelligent but also reliable, responsible, and capable of delivering real value across the enterprise.
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“It’s how we make our enterprise more human, efficient, and future-ready. Customers don’t want tools. They want intelligent solutions,” says Dr. Tapan Sahoo, Executive Officer – Digital Enterprise & Cybersecurity at Maruti Suzuki.
AI That Scales Is AI That Succeeds
While AI experimentation is widespread, actual business value lies in scale, not pilots. According to a recent NetApp survey, 81% of global organizations are currently piloting or scaling AI initiatives, but only 4% have successfully industrialized AI across multiple business functions. This gap between experimentation and enterprise-wide execution highlights a critical challenge.
As Indian enterprises increasingly adopt AI, the focus must shift from “How do we start?” to “How do we scale responsibly and sustainably?” The CIOs featured here prove that scaling AI is not a leap of faith—it’s a disciplined climb. One that requires infrastructure, clean data, measurable goals, and cultural readiness. When done right, AI becomes not just a technology initiative but a business engine.
For India’s CIOs, the opportunity lies in leading that climb—with clarity, control, and conviction. AI isn’t the future of enterprise. It is the present, and the scale at which you deploy it will define the future of your business. Yet, as Deloitte’s findings suggest, it will be a long slog, and realizing tangible ROI from AI will take more than a year—underscoring the need for long-term strategy, firm foundations, and operational integration.