As India’s enterprises accelerate their investments in artificial intelligence, a critical question emerges, are these innovations truly driving operational value, or are they becoming experiments without outcomes? According to Infor CEO Kevin Samuelson, the answer lies not in how advanced AI is, but in how relevant it is to the business it serves. Samuelson emphasized that industry context, not scale will determine the winners of the AI race.
While AI promises to redefine productivity, decision-making, and efficiency, he noted that success depends on how well organizations embed AI into their unique workflows and data ecosystems. “Companies that implement generic AI tools without industry-specific context will lose valuable time and money they often can’t afford,” said Kevin Samuelson, underscoring the danger of adopting one-size-fits-all AI solutions.
Context is king: The case for industry-specific AI
Unlike horizontal AI models that aim for broad utility, Infor’s approach focuses on micro-verticals—specific business segments such as healthcare, automotive, food processing, and logistics—each with its own operational DNA. Samuelson explained that by understanding these differences deeply, AI can move from theoretical insight to practical value. “If you take a dairy company versus an automotive OEM, the processes for receiving and billing are entirely different,” he illustrated. “Embedding these processes into AI systems reduces implementation risk, minimizes training, and ensures recommendations are actionable from day one.”Echoing this, Soma Somasundaram, President and CTO of Infor, added that enterprise AI must prioritize relevance over data overload. “It’s not about collecting every possible attribute. It’s about focusing on the financial, customer, employee, and product-level data that truly drives performance,” he said.
For India, where businesses range from traditional manufacturers to next-generation startups, such micro-vertical specificity allows organizations to gain faster ROI and minimize project risks. The idea is simple: contextual AI equals operational advantage.
From ROI to ROV: Measuring what truly matters
As more boards and leadership teams evaluate AI performance, Samuelson argued that the conventional focus on ROI (Return on Investment) misses the broader picture. Instead, organizations should shift to ROV (Return on Value), a metric that captures operational efficiency, speed, and overall impact. “Boards are increasingly focused on value delivered, not just dollars spent,” he said. “ROV helps measure how AI improves workflows, decision cycles, and service quality, not just financial outcomes.”
This thinking is particularly relevant for mid-sized Indian companies that often operate with limited IT budgets. Rather than chasing every AI trend, Samuelson suggested focusing on projects that create measurable operational improvement, be it reducing supply chain disruptions, optimizing staffing, or enhancing forecasting accuracy.
Open architectures and the power of integration
Another pillar of enterprise AI success, according to Samuelson, is open architecture—a framework that allows seamless integration between AI tools and existing systems like ERP, CRM, and HR platforms. “Each tenant is a non-permeable layer, one customer cannot access another’s data,” Samuelson clarified. “Open architectures allow AI to integrate securely and flexibly across functions.”
This flexibility not only enhances data interoperability but also ensures scalability across industries. By maintaining an open and layered design, enterprises can innovate faster without compromising on governance or security, two major concerns for India’s heavily regulated industries.
Strategic partnerships play a major role here. “We ask two questions before partnering: Does it create tangible value for customers? Does it deliver a differentiated technology story?” Samuelson shared. Such collaborations, he added, accelerate the path to operational maturity for enterprises exploring AI transformation.
A framework for real-world AI impact
For Indian enterprises striving to derive real operational benefits from AI, Samuelson and Somasundaram outlined a practical framework to ensure measurable outcomes:
- Focus on relevant data that directly informs business decisions rather than overwhelming systems with excess information.
- Embed AI in industry-specific workflows to generate insights that can be immediately acted upon.
- Measure initiatives not just by ROI, but by ROV—the operational value and efficiency gained from implementation.
- Leverage open architectures and trusted partnerships to enable secure, scalable integration across business systems.
These principles, they said, can help Indian businesses move from experimentation to execution, turning AI from a buzzword into a productivity engine.
A ground for applied AI
India’s enterprise ecosystem, with its vast diversity of sectors and operational complexities, offers fertile ground for industry-focused AI adoption. From manufacturing to healthcare to logistics, each industry’s operational reality differs significantly, making generic AI less effective.
As organizations reimagine their data strategies, the emphasis must shift from “more data” to “better data.” Those that align AI with specific workflows, measurable outcomes, and open platforms will be better positioned to scale sustainably and deliver real value. “AI should not replace decision-making, it should empower it,” Samuelson remarked, summarizing the underlying philosophy that guided discussions.
Note: This article is based on insights shared by Kevin Samuelson, CEO of Infor, and Soma Somasundaram, President and CTO, at the Infor Velocity Summit 2025. It reflects the company’s broader observations on enterprise AI adoption, emphasizing context-driven innovation, operational value measurement, and industry specificity as key enablers.
- Published On Oct 27, 2025 at 02:21 PM IST
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