Is AI Actually Delivering Returns?

For years, AI in India lived in the pilot zone. Companies launched proof-of-concept projects, built “AI labs,” and chased innovation optics. But with tight budgets and expectations rising, enterprises are asking, is AI actually delivering returns?

A recent industry report from Bain & Company suggests the answer is mixed. Average ROI from AI initiatives hovers around 5-7%, yet certain sectors are beginning to show double-digit gains. The gap, say experts, lies not in the technology itself but in how organisations embed it.

“Most enterprises have moved from AI adoption for optics to AI adoption for outcomes,” said Anirudh Bhardwaj, chief technology officer at Recur Club, a fintech platform that uses AI to streamline revenue-based financing. “ROI today is measured not just by cost savings but by speed and accuracy of decision-making.”

At Recur Club, that shift is measurable. “In lending, AI-driven underwriting cuts deal evaluation time from weeks to days while improving credit accuracy,” Bhardwaj explained. 

This gives a peek into enterprises’ approach towards AI adoption. The first phase was about automation , replacing repetitive tasks and driving efficiency. The next phase, experts say, is about intelligence, building systems that learn, self-correct and continuously improve.

“AI payoffs follow an S-curve,” Bhardwaj noted. “Short-term gains come from automation, faster execution and fewer errors. Long-term ROI comes from intelligence, that is systems that learn and self-correct over time.”

According to him, organisations that plan for both time horizons, quick wins and compounding intelligence are the ones seeing sustainable returns. “The smart enterprises budget for both: near-term wins to prove value, and patient investment in model retraining, data quality, and workflow redesign for long-term benefits.”

According to Ankur Khare, senior solution advisor at SAP America, enterprises that treat AI as a core capability, not a side project, consistently outperform their peers.

“In my experience, organisations that view AI as a core business capability rather than a standalone technology achieve substantial returns,” Khare said. “Typically, these enterprises realise productivity improvements of 20–40% in targeted functions within the first 12–18 months.”

Industry data supports this. Companies with deep AI integration report an average ROI of 10.3x, compared to just 3.7x for those with limited adoption. “The most enduring value comes not from automation alone, but from how effectively employees adopt and trust AI in their daily workflows,” Khare explained. “When change management and user training are prioritised, we see tangible financial impact through faster process cycles, reduced rework and smarter decision-making.”

The Sectoral Divide

Certain sectors have already cracked the ROI code. Financial services, logistics, and customer support are clear frontrunners. These industries have structured, data-rich processes that AI can easily optimise.

“Tangible returns are emerging anywhere there’s high data volume and repeatable decision logic,” Bhardwaj said.

Khare adds that in manufacturing and supply chains, the payoffs can be dramatic. “Siemens achieved a 20% reduction in maintenance costs and a 30% decrease in unplanned downtime through AI-driven predictive maintenance,” he added. “In financial services, Bank of America’s AI handled over a billion customer interactions, reducing call centre load by 17%.”

Customer experience and automation projects also tend to yield early, visible wins, directly reducing response times and operational costs. But not every industry is keeping pace. Sectors with high regulatory complexity and fragmented legacy systems such as healthcare and public services continue to struggle.

“Many of these challenges aren’t technical,” Khare explained. “They’re about data readiness, governance, and the maturity of user adoption. While 90% of hospitals now use AI for diagnosis and monitoring, integration and change management remain major hurdles.”

Why Some Companies Still Fall Short

Despite the success stories, many enterprises still fail to see meaningful returns. Bhardwaj points to a recurring pattern: companies deploying models without a clear value hypothesis. “They jump to implement AI without defining measurable outcomes or re-engineering legacy processes. AI adds little value when it’s layered on top of inefficient workflows.”

“ROI strength depends on four factors,” Khare said, “leadership alignment, data quality, change management, and continuous iteration.” Clean, integrated, contextual data can increase ROI by up to 10x, he says adding that the best ROI comes from treating AI as an evolving capability, not a one-off deployment.

The Maturity Shift

India’s enterprise AI landscape is maturing fast. What once were isolated innovation labs are now giving way to fully AI-native systems, where intelligence is baked directly into core products and operations.

“Companies are embedding AI into their products, not running pilots on the side,” Bhardwaj observed. “We’re seeing three big trends: outcome-linked AI, agentic systems automating workflows across departments, and cost-aware AI that builds with open models and APIs to stay scalable.”

This new cost-conscious mindset, driven by open-source and API-first architectures, could democratise AI for mid-sized enterprises — a group long seen as lagging in digital transformation.

“Cost-aware AI is how adoption becomes sustainable,” Bhardwaj adds. “You don’t need massive proprietary models to get business value.”

When Returns Become Real

So, how long before enterprises see actual payback? 

Khare says the first measurable outcomes appear within six to 12 months, particularly in pilot programs, while full-scale ROI takes about 18–24 months.

“The speed of returns depends heavily on how early and effectively organisations invest in change management, user training, and AI literacy,” he explained. “Companies embedding these from the outset can accelerate ROI by up to 30%.”

Bhardwaj agrees that AI is a journey, not a project. “The enterprises that win are the ones that iterate, measure, and keep refining. ROI compounds when systems and people learn together.”

The conversation around AI in Indian enterprises has matured dramatically. The obsession with “doing AI” has given way to a pragmatic focus on measurable outcomes. 

As Bhardwaj puts it, “The new ROI lens is productivity per dollar spent — time saved, decisions improved, and revenue accelerated.”

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