AI is at an inflection point. Evolving from pilots and proof-of-concept experiments confined to innovation labs or IT backrooms, AI now impacts business strategy and outcomes. AI is beginning to reshape how companies operate, compete, and grow. In India, Chief AI Officers (CAIOs) are increasingly taking a strategic seat at the leadership table. According to a recent IBM Institute for Business Value study, 77% of CAIOs in India report strong C-suite support, indicating strong organisational alignment to scale AI effectively.
However, the optimism is tempered with caution, since challenges do exist.
The first phase of AI adoption has been largely universal. While automation has delivered early gains, Indian organisations are still figuring out how they can use AI as a sustained growth engine. Most organisations have deployed AI to automate repetitive tasks, improve turnaround times, and reduce costs. While these efforts are delivering quick wins, eventually organisations find their returns plateauing as efficiency hits a ceiling.
To gain sustained value, AI must be embedded into core workflows, decision-making, and business models. That shift—from pilot to platform—is where many companies stumble. It demands strong data foundations, robust governance, workforce adoption, and leadership alignment. Importantly, organisations benefit greatly from learnings gleaned from those who have already navigated similar complexity. Even when the challenges are sometimes industry-specific, most solutions are fairly horizontal.
The case for cross-sector learning
India is home to a wide range of industries that grapple with their own unique challenges. Various sectors - banking, manufacturing, telecom, energy, public services – are modernising at distinct, differing paces. In addition, India’s thriving digital public infrastructure as well as government-led AI initiatives is driving a cost-conscious innovation mindset. The amalgamation of all these factors offers some unique opportunities for industries to learn from each other.
For example, consider banking. Financial institutions operate under intense regulatory scrutiny and zero tolerance for failure, but have high risk exposure. Therefore, investing heavily in governance, explainability, validation, and human-in-the-loop systems is a key aspect of responsible AI scaling. Therefore, there is an opportunity for other industries such as public sector, healthcare, and manufacturing that value safety, compliance, and accountability to borrow best practices from banking.
In manufacturing, operational AI in the form of predictive maintenance, digital twins, computer vision, etc. delivers maximum value in the form of greater uptime and yield. There might be learnings here for energy companies, logistics providers and infrastructure operators among others, to learn from manufacturing to reduce downtime and build resilience in volatile environments.
Rather than experimenting in isolation, Indian organisations can potentially compress learning cycles dramatically if they make an effort to borrow insights from proven AI patterns across sectors. Not only does this approach potentially reduce risk, but it also drastically accelerates time to value. This allows leaders to focus less on experimentation and more on execution.
There are some strikingly consistent patterns across industries. For instance, irrespective of the technology or playbook, most successful AI initiatives have strong digital cores, clear governance frameworks. Needless to say, a relentless focus on business outcomes and deep investment in people adoption are also non-negotiable.
A clarion call for leaders
Leadership plays a crucial role in scaling AI. The key is to treat AI not as an IT upgrade but as a growth multiplier. The question isn’t just about which model or platform to choose. Rather, it is about learning from peers across industries to determine how similar problems have been addressed. By taking a closer look at journeys of other organisations, there is a lot to learn about change management, workforce upskilling, and funding practices.
India is home to a diverse mix of legacy enterprises, digital-native challengers and public digital platforms. Given the universal focus on scale-driven economics, India has an opportunity to leapfrog the slow, siloed AI journeys seen elsewhere. Businesses that learn fast, adapt smartly, and collaborate effectively will shape the next chapter of India’s AI journey.