78% of Indian Executives Ready to Increase Generative AI (GenAI) Spending in 2025

In 2025, the C-suite in India is expected to act boldly on artificial intelligence, particularly in the impactful area of Generative AI (GenAI). A strong indication of this shift can be seen in the fact that 78% of India's senior executives are planning to increase their spending on GenAI initiatives. This growing confidence reflects both technological optimism and a new strategic direction in Indian organizations. While markets elsewhere seek intelligent automation, Indian executives are leaning in as leaders who want to evolve their organizations to develop new levels of competitive advantage, innovation, and productivity with AI.

GenAI is no longer seen as a side investment or pilot project. Instead, for many Indian organizations, GenAI is becoming part of the core of their digital transformation plans. Not only are executives seeing GenAI as an enabler of hyper-personalized customer experiences, effective decision making and scalability, they see it as a technology that is mission-critical, not a nice-to-have. As organizations continue exploring GenAI, they will also need to reconcile their gaps in structure--in training, adoption, and risk management--understanding these gaps will influence the real outcomes of their GenAI investments.

1. The Strategic Pull of Generative AI in Indian Boardrooms:

Central to India's GenAI surge is a major strategic pull. Firms see GenAI as a technology that can dramatically improve operations—and outcomes—across functions; in marketing and customer service, in HR and legal, in supply chain and so on. The pull is based in part on GenAI's potential to generate content faster than before, to make workflows more efficient, to write software faster, and to quickly and deeply analyze unstructured data for insights.

What is even more compelling is that GenAI is being considered not just as a cost down tool, but as a force multiplier. Executives believe GenAI will enable new business models, new revenue channels, and enable teams to do more with less. In conversations unfolding in board rooms throughout India, this technology is being deliberated in the same sentence as the earlier revolutions of cloud computing and mobile - thus signalling how quickly it has entered the conversations regarding the vision of ready-to-use future enterprise.

The psychological change is noteworthy as well. The push for AI adoption isn't just about the fear of missing out. It is about readiness. Executives believe they have the right internal systems, security structures and leadership alignment to infuse GenAI into their businesses. This is important because most Indian businesses don't want to test AI, they want to scale it.

2. The Tech-Talent Imbalance:

While the excitement around GenAI is elevated, a closer analysis indicates a potential imbalance emerging. Many Indian companies are committing substantial time and resources to GenAI technologies and infrastructure, but demonstrating less commitment to the people's readiness to fully leverage it. There is a disconnect in understanding between executive leadership and the broader workforce with respect to GenAI.

Executive leadership may be having a solid grasp on the potential of GenAI and embedding it in their decisions, but as you will read later in this report, far fewer employees do. There seems to be a distance between the high aspirations of executive leadership and the daily experience of the frontline employees. This distance is not merely cultural; it is also operational. Without engaging with employees through training, they cannot fully engage with the tools and technology deployed.

The talent gap is also being demonstrated in the practical difficulties employees are facing. Many employees also articulated concerns about accuracy, access to resources and lack of availability in existing platforms. On top of this, some employees hold fears about whether their jobs are even going to exist or whether they will have a defined role. Even among those employees who are keen, there is a general lack of clarity as to how GenAI fits into their existing work. If it is going to be an integral part of the way they work now and into the future, companies need to embrace more than basic training and facilitate an AI literacy culture right across the whole organization.

3. India’s GenAI Ecosystem Is Evolving

India's ecosystem is also lending itself to the further adoption of GenAI in the private sector. New age Indian startups have developed extremely cost-effective and enterprise-grade GenAI tools for Indian companies. In addition, these developments have allowed industries like finance, retail, and health to advance the technology of AI (artificial intelligence) and GenAI (Generative AI) where only cost constraints would have previously inhibited such advances. 

The government is also providing a leading role with policies and infrastructure to enable secure and scalable adoption of AI. As India's ambition on the global technology stage increases, it is also looking to build sovereign capability - where AI models will use data that is local, will learn in local languages, and will be based on India's unique social and economic realities. 

The enthusiasm of both the public and private sectors for AI is leading to a spirit of experimentation. However, most implementation remains in the pilot or prototyping phases with only a small percentage of Indian enterprises introducing GenAI models widely across their departments. Moving beyond experimentation to enterprise wide adoption will depend not only on the level of ambition, but also on the governance framework being created to support the risk management, performance management, and ethical issues involved.

4. Confronting the ROI Dilemma

One of the key challenges currently faced by Indian businesses is providing evidence of measurable return on GenAI investments. With so much money being invested into AI initiatives, there is pressure for executive boards to justify the expense. What was previously enthusiasm around GenAI is now a pragmatic consideration of what is delivering value and what success looks like for our organisations.

Part of the problem is expectation management. GenAI creates a lot of hope, but developing a proof of value can require heavy lifting in terms of backend reconfiguration, data hygiene, meta data management, and change management. The invisible costs of these aspects delay the monetization of realised value and complicates how we assess ROI.

In addition, measuring ROI in relation to GenAI is more complex than some might think. It is certainly important to understand whether cost savings or increases in productivity provide ROI, but there are also many intangible value outputs of GenAI projects—such as employee productivity, brand consideration, and increased speed of innovation. By only looking at value with a financial lens, firms can miss the broader value GenAI initiatives provide. Taking a more holistic approach and combining quantitative and qualitative measures is important to uncover the value of GenAI initiatives.

5. Building Trust, Mitigating Risk

While rapid adoption comes with increased opportunity, it also comes with increased risk. The issue of reliability and ethical behavior by GenAI tools today is perhaps one of the most glaring ethical concerns faced by any organization using AI-powered tools. Recent incidents of hallucinated AI content, biased output as a result of the AI training data, and compromised data (or worse, insecure models) have made headlines, even in India. 

In order to build trust, organizations must create proper oversight regarding how GenAI is trained, used, and monitored, through internal processes and governance structures. Oversight can include institutional support such as creating cross-functional ethics committees, performing bias audits on the output of GenAI tools, and ensuring AI is developed using explainable and/or transparent models. Organizations are also responsible for training employees to use the AI outputs consciously and responsibly—they must train their employees in the ethical use of AI, particularly when their work involves a decision that may affect someone directly, content generation, or consumer engagement. 

Last, but not the least, an organizations cybersecurity must take centre stage. As organization begin increasingly using GenAI systems in their workflows, data privacy risks will inevitably increase with artificial intelligence. Information traditionally kept in silos will be processed through generative models, where there is risk of leakage, illicit use and data compliance risk. To mitigate these risks, organizations need a disciplined view of security, building security across every component of their GenAI stack  - from access to credentials and controls to model training data.

6. The Road to Meaningful Implementation

For GenAI to effectively transform the business in India, implementation plans must move from the exciting use cases and adopt operational discipline. Successful transformations stem from leadership alignment. Leaders must create clarity about what GenAI means for the corporate mission, vision, and values. Leaders must then create measurable outcomes across every department. 

Equally, employee enablement is crucial. GenAI transformation relies upon employee trust not just executive decree; training should be role based, contextualized to business processes, and a consistent experience for the employee. Incentives should promote responsible use when using GenAI technologies. Communications should be open, transparent, and inclusive sharing that employees are not merely passive participants but active actors in their capability transformation. 

Technology decisions need to be practical ones. Firms should practice technology discipline and not chase the latest and greatest tech. Firms should prioritize compatibility, extensibility, and control in their GenAI application. Locally trained, open sourced frameworks coupled with multiple deployment architectures, for multi platform inter-operatability offer clearer alternatives to believing in a monumental mistake of buying a GenAI program designed for everyone's use case. These decisions will also promote vendor independence and will contribute to data sovereignty.

7. Looking Ahead: From Adoption to Leadership

The coming will act as a significant milestone for India’s GenAI journey. A large majority (around 80%) of C-suite leaders say they are keen to dive deeper into investment, as we have momentum in this space. However, the journey and progress made will not be guaranteed, and the path ahead will require a balanced approach that finds the sweet spot between innovation and inclusion, ambition and caution, speed and structure. 

India not only has the potential to simply adopt GenAI, but to pioneer its responsible and scalable application. This will mean making tough, sometimes difficult decisions about budgets, workforce plans, vendor relationships and ethics frameworks. It will also mean embracing bold vision, redesigning business models, re-skilling our people, and committing to long-term change and transformation. 

But while GenAI is revolutionary, it is not a sprint. It is a marathon of change – across technology, talent and trust. Businesses in India that embrace this reality, and build thoughtfully towards it, will not only emerge as successful adopters, but global leaders in this new chapter of AI-enabled enterprise.

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