Prosenjit Sengupta, Group Chief Digital and Information Officer (GCDIO) at ITC, shares exclusive insights on AI, the top challenges faced by CIOs, and their key focus areas.
CIO&Leader recently sat down with Prosenjit Sengupta, Group Chief Digital and Information Officer (GCDIO) at ITC, for an exclusive discussion on AI, the top challenges CIOs face, and the key technology priorities shaping their agendas. With over two decades of experience, Sengupta leads ITC’s digital strategy, leveraging technology to drive growth, enhance customer experience, and streamline operations.
Challenging the conventional perception of AI as a disruptor, Sengupta reframes it as an enabler—augmented intelligence that amplifies human capabilities rather than replacing them. He delves into the complexities of AI adoption, from securing sensitive data and integrating emerging technologies to bridging the growing skills gap in the workforce.
According to Sengupta, successful AI implementation goes beyond technology—it requires a culture of continuous learning, collaboration, and strong governance. Aligning people, processes, and policies is crucial to unlocking AI’s full potential and ensuring a sustainable, human-centric future.
Read on to explore his insights on how organizations can navigate AI adoption, ensuring technology complements human expertise.Excerpts from the interview:
GCDIO, ITC
CIO&Leader: As AI takes center stage, it brings along challenges such as complexity, data security, and workforce retention. How can CIOs and IT leaders navigate AI adoption while effectively balancing these risks?
Prosenjit Sengupta: We’ve witnessed rapid technological advancements over the past few years. This took off with OpenAI’s introduction of ChatGPT and large language models (LLMs). Since then, we’ve seen a surge in startups and established companies offering AI-driven point solutions across various business areas. The challenge for large enterprises like ours is determining how many of these solutions can realistically be integrated into our ecosystem. Absorbing 10, 15, or 20 different AI-driven solutions would complicate our technology landscape.
Another primary concern is data security. Many AI solutions require companies to upload sensitive data onto external cloud platforms. But the question is: Will the data remain secure? Could it be used for purposes beyond our knowledge? Could insights gathered from our data be shared with competitors? Our customers, employees, and stakeholders are responsible for ensuring that our data remains secure and compliant, whether it’s employee information, customer records, process data, or business transactions.
A third challenge is talent. The growing number of AI solutions has created a knowledge gap, leaving organizations without enough skilled professionals to evaluate and implement the right technologies. Many organizations remain stuck in endless PoCs and pilots, finding it difficult to scale due to security and integration concerns. Meanwhile, employees who upskill in AI often leave for better-paying opportunities, making it even harder to build internal AI expertise. Companies find themselves in a perpetual cycle of proof-of-concept (PoC) and pilot testing but struggle to progress with full-scale implementation due to a lack of confidence in security and integration.
Employees who invest time in upskilling—especially in AI and emerging technologies—often leave for startups or companies offering higher pay and better growth opportunities. This talent drain makes it even harder for organizations to build a robust internal AI competency.
CIO&Leader: How do you approach change management, particularly in cultural shifts and workforce integration?
Prosenjit Sengupta: One of the most significant cultural challenges organizations face today is managing a multi-generational workforce. We have Gen X professionals—many of whom are in senior leadership positions or nearing retirement—alongside Gen Y, who are in mid-management roles, and Millennials, who are just joining the workforce. With the rise of AI and automation, we’re introducing yet another “generation” to the workforce: intelligent machines and AI-driven systems.
Previously, robotic process automation (RPA) handled repetitive tasks, but now, generative AI takes automation a step further by analyzing data, drawing insights, and even making decisions. Essentially, we’re integrating a new ” worker ” type into the enterprise that operates differently from any human employee.
Managing such a diverse workforce requires significant changes in organizational culture and leadership ideas. Senior employees, who have spent decades relying on traditional decision-making frameworks, must now adapt to AI-driven insights. Mid-career professionals must bridge the gap between legacy systems and cutting-edge technology. At the same time, younger employees—often more adaptable to AI—must learn to navigate corporate structures and governance.
The expectations of a 25-year-old from a company are vastly different from those of previous generations. Whether job satisfaction, the relationship between employees and their managers, working conditions, working hours, learnability, or career aspirations, everything varies significantly across generations. This makes cultural and change management an essential consideration.
The key to successful change management is developing a culture of continuous learning and collaboration. Organizations must invest in training programs that upskill employees at all levels, ensuring that AI and automation complement human expertise rather than replace it. Leadership also needs an environment where knowledge sharing is encouraged across generations, allowing experienced professionals to mentor younger employees while learning from them.
CIO&Leader: Given these challenges, how should enterprises balance adopting AI-driven innovations and maintaining operational stability?
Prosenjit Sengupta: Striking the right balance requires a phased approach. Enterprises should start by identifying high-impact areas where AI can deliver tangible value—customer service automation, predictive analytics, or supply chain optimization. Instead of deploying AI across multiple business functions simultaneously, companies should prioritize targeted implementations that align with their strategic goals.
Another critical aspect is governance. Clear policies should guide AI initiatives around data privacy, compliance, and ethical considerations. Enterprises must establish robust frameworks to ensure that AI solutions are used responsibly, mitigating bias, security breaches, and regulatory compliance risks.
Collaboration with trusted technology partners is also essential. Rather than experimenting with multiple standalone solutions, companies should seek partnerships with vendors who offer scalable, secure, and enterprise-grade AI capabilities. It not only simplifies integration but also ensures long-term sustainability.
Finally, leadership commitment is critical. CIOs and business leaders must actively champion AI initiatives, encouraging a culture that welcomes change rather than resists it. By investing in people, processes, and technology in a balanced manner, organizations can implement AI-based solutions effectively while maintaining stability.
CIO&Leader: That’s a strategic perspective. As AI adoption accelerates, what key insights or best practices would you offer CIOs and IT leaders still navigating AI integration in their organizations?
Prosenjit Sengupta: My advice would be to start small but think big. AI adoption doesn’t have to be an all-or-nothing approach. Begin with pilot projects in specific areas where AI can demonstrate clear value. Monitor results, learn from early implementations, and gradually scale up based on success.
CIOs should also focus on building AI literacy within their organizations. This means upskilling technical teams and educating business leaders on AI’s potential and limitations. AI should be seen as an enabler rather than a disruptor—it’s about augmenting human intelligence, not replacing it.
Additionally, companies must prioritize ethical AI practices. Transparency, accountability, and fairness should be at the core of AI deployments. Organizations can mitigate risks and build stakeholder trust by establishing strong governance frameworks.
Most importantly, IT leaders should stay agile. The AI landscape is evolving rapidly, and organizations remain flexible and open to change.
CIO&Leader: With your expertise and insights from new-age leaders, how do you see the CIO’s role evolving over the next five to six years? What new challenges might CIOs encounter as Gen AI reshapes enterprises beyond those we’ve discussed?
Prosenjit Sengupta: The most significant challenge for future leaders will be integrating technology into every business process. There are a few key aspects to consider:
- Data management – Understanding data, whether at rest or in motion, will be critical. With the upcoming DPDP (Digital Personal Data Protection Act), enterprises must navigate vast amounts of structured and unstructured data while ensuring data protection and compliance with regulations. Managing and securing organizational data will be a top priority for CIOs.
- AI-driven decision-making – Until now, AI was primarily used to process information, with humans making the final decisions. However, in the future, there will be more automated decision-making. CIOs must determine which decisions can be fully automated, which require human oversight, and how to manage AI hallucinations effectively. Striking the right balance between unsupervised automation and necessary human intervention will be crucial.
- Factory automation and Industry 4.0—Although we’ve been discussing Industry 4.0 and IoT for years, full-scale implementation across industries is still in its early stages. Over the next five to ten years, factory automation will become a significant priority, transforming shop floors with advanced technologies.
- Sustainability and compliance – Sustainability is becoming an essential part of corporate reporting in India. The government has already mandated sustainability disclosures for the top 500–1000 companies, and in the next decade, all businesses will be required to demonstrate sustainable practices. CIOs will be key in measuring and managing sustainability metrics, ensuring compliance, and integrating sustainability into business processes.
- The SAP transition – Another major shift in the next five to six years will be the transition to SAP S/4HANA. SAP has clarified that the current generation of ECC systems will reach end-of-life by 2027–2030. This means that companies must migrate to the new platform within this timeframe. Organizations will face a critical decision: either transition to SAP’s next-generation offerings or explore alternative enterprise solutions.
These transformations will redefine the role of the CIO and demand a deeper focus on technology integration, regulatory compliance, and business sustainability.
CIO&Leader: How important is mentorship in leadership? Should it focus on successors or the whole team? Should CIOs take a broader approach to drive growth and innovation?
Prosenjit Sengupta: Mentorship is essential at all levels because different types of guidance are needed at various career stages. We’ve discussed how the expectations for a new CIO change dramatically—they must shift from a technical mindset to a business leader’s perspective, align with the CEO’s vision, and understand the company’s broader strategy.
A CIO must also have deep expertise in technology—past, present, and future—and strong capabilities in change management, cultural transformation, and leadership. Beyond reporting to their immediate boss, CIOs often engage with the CEO and, at times, the board, making their role increasingly complex.
So, while mentorship is critical for aspiring CIOs, it is equally essential for those below the CIO level. However, it is especially crucial for CIOs, given the diverse stakeholders they interact with and the complexity of their decision-making.
CIO&Leader: Are Indian enterprises truly AI-ready? With the rapid pace of change and gaps in mentorship and skills, do they have what it takes to manage AI effectively?
Prosenjit Sengupta: AI readiness depends on data, but most companies aren’t prepared. Poor data quality, collection issues, and security concerns hinder adoption, especially for generative AI, which relies on clean, structured data.
Another challenge is the skills gap. While startups and tech firms have strong AI talent, many large enterprises, especially OEMs, lack deep expertise.
Many working in Gen AI are early in their careers—skilled but inexperienced in managing risks. Meanwhile, having navigated past tech shifts, established firms are better at risk management. Startups drive innovation but often lack foresight. Leaders must guide and collaborate with them to balance innovation with responsible AI adoption.