Beyond experimentation: How enterprises can scale AI for real business impact

India has swiftly embraced AI technology to harness its benefits. But new research shows that scaling it for transformative business impact will need a different approach to both the workforce and business operating models.

India’s AI focus

The past year has seen the Indian government proactively build an AI ecosystem to ensure affordable access to computing power, graphics processing units, and cutting-edge research. Under the IndiaAI Mission, launched in 2024, it has committed over USD 1,100 million over five years to bolster the country’s AI capabilities.

AI centres of excellence have also been established to boost innovations across prominent sectors such as agriculture and healthcare. AI-focused accelerators are in place to support startups with mentorship, resources, and infrastructure tailored to their needs. Efforts are also underway to create skilling centres to equip the youth with future-ready AI skills.

The drive to adopt AI is not just coming from the government. A 2024 study revealed that approximately 59% of enterprise-scale organisations in India, with over 1,000 employees, have already integrated AI into their business operations. AI is being applied in a variety of industries, be it agriculture, where AI-powered precision farming tools are helping monitor crop health; manufacturing, where it is being used for predictive maintenance of machinery; or logistics, where it is being used for route optimisation.

Not just a tech revolution

While most countries are concerned about having enough science, technology, and engineering skills to support their AI aspirations, India is well placed to benefit from the AI revolution. Thanks to its large tech-focused workforce, which supports a booming industry for both large and small tech businesses.

But the AI revolution will not just be about the tech or tech skills and expertise. Recent research has found that an organisation’s engagement with its workforce and its operating model transformation are the most critical factors for succeeding in AI. Organisations can boost their chances of AI success when they have well-established change management and AI training programs in place and actively involve employees in AI-related decision-making. It was also found that many of the successful AI use cases are those that require a large amount of transformation to their operating model or data architecture. The message is clear: organisations need to change their business, particularly if they want to do AI that is industry-specific and needs them to be more competitive.

The main hurdle is that while organisations are getting better at experimenting with, developing, and deploying AI technologies, they struggle to transition from isolated proofs of concept to enterprise-wide AI adoption that drives measurable value. Overcoming these challenges and scaling AI will require a strategic approach. Organisations will need to integrate AI deeply into their operations, ensure it is not just an add-on but a core business enabler, and make necessary changes to their operating model and employee engagement to support their journey.

How organisations can scale AI

While successful AI use cases are often those backed by significant investment, many use cases emerge opportunistically. That said, it is imperative for organisations to align initiatives with business goals before scaling them so they can be linked to specific outcomes to realise business value. Securing executive buy-in for AI projects helps ensure business and strategy alignment, along with continual support from leadership.

Organisations need to establish a responsible AI task force accountable for data governance, ethics, and compliance related to AI use to reduce related risks and maintain reliability and trust. Creating an AI foundry helps encourage a culture of innovation while ensuring it is within the limits of the prescribed guidelines.

Adopting a product-centric operating model, where employees are accountable for project success, empowers teams to maximise experimentation and agility. This approach not only accelerates AI scaling but also fosters a continuous improvement mindset essential for adapting solutions to evolving business needs.

Enabling cross-functional collaboration between AI teams and the business can help ensure that the right use cases are chosen for AI deployment, saving time spent in trial and error and allowing organisations to move with the speed needed to scale AI.

Organisations must focus on engaging employees effectively to scale AI successfully. This involves continuous training on the use of AI tools, building awareness of how AI will impact their work, addressing concerns such as job displacement, and implementing structured change management to drive AI adoption, maximise its value, and scale it.

By shifting focus from experimentation to execution and scaling through these measures, enterprises in India, empowered with their AI investments and talent, can unlock AI’s full potential, achieving efficiency gains, competitive advantages, and long-term business transformation. Those who get it right will not just implement AI, they will redefine how business is done.

Authored by Balakrishna D. R. (Bali), Executive Vice President, Global Services Head, AI and Industry Verticals, Infosys