As Indian enterprises increasingly invest in artificial intelligence (AI), a pressing question arises: are these innovations delivering tangible operational value or simply serving as unproductive experiments? Kevin Samuelson, CEO of Infor, asserts that the crux of the matter lies not in the sophistication of AI but in its relevance to the specific business it supports. He emphasized that the winners in the AI domain will be determined by their industry context rather than the scale of their technologies.
Samuelson posits that while AI holds the potential to redefine productivity and decision-making, the success of these technologies hinges on how effectively organizations integrate AI into their distinctive workflows and data environments. “Companies that implement generic AI tools without industry-specific context will lose valuable time and resources,” he warned, highlighting the risks associated with adopting one-size-fits-all AI solutions.
Infor’s strategy diverges from broad horizontal AI models by focusing on micro-verticals—targeting specific business sectors such as healthcare, automotive, food processing, and logistics, each with its unique operational framework. Samuelson illustrated this by contrasting the operational processes of a dairy company with those of an automotive original equipment manufacturer, emphasizing that integrating sector-specific processes into AI systems decreases implementation risks, minimizes training, and ensures actionable recommendations from the outset.
Soma Somasundaram, Infor’s President and CTO, echoed this sentiment, stressing that enterprise AI should prioritize relevance over excessive data accumulation. “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 asserted.
In the diverse landscape of Indian businesses, which range from traditional manufacturers to innovative startups, micro-vertical specificity can offer quicker returns on investment and reduced project risks. The underlying message is clear: contextual AI translates to operational advantage.
As corporate boards and leadership teams increasingly scrutinize AI performance, Samuelson contends that the traditional emphasis on Return on Investment (ROI) fails to capture the complete picture. Organizations should consider shifting to Return on Value (ROV), a metric that gauges operational efficiency, speed, and overall impact. “Boards are increasingly focused on the value delivered, not just dollars spent,” said Samuelson, adding that ROV helps assess how AI enhances workflows, decision-making cycles, and service quality, rather than merely financial returns.
This perspective is especially pertinent for mid-sized Indian companies that typically operate with constrained IT budgets. Instead of chasing every AI trend, Samuelson advises prioritizing initiatives that yield measurable operational improvements—whether through reducing supply chain disruptions, optimizing staffing, or enhancing forecasting accuracy.
An essential component of successful enterprise AI is open architecture, which facilitates 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 explained. This type of architecture allows for secure and flexible AI integration across various business functions.
This adaptability not only improves data interoperability but also allows for scalable solutions across industries. By maintaining an open and layered structure, companies can innovate rapidly while addressing governance and security concerns, especially critical in India’s heavily regulated sectors.
Strategic partnerships are vital in this context. Samuelson indicated that before forming alliances, two questions should be asked: “Does it create tangible value for customers? Does it deliver a differentiated technology story?” Such collaborations can expedite operational maturity for enterprises navigating AI transformation.
To ensure significant real-world advantages from AI, Samuelson and Somasundaram propose several guiding principles for Indian enterprises:
- Concentrate on data that actively informs business decisions while avoiding overwhelming systems with unnecessary information.
- Embed AI within industry-specific workflows to generate insights that can be immediately acted upon.
- Measure initiatives not only by ROI but by ROV—the operational efficiency and value derived from implementation.
- Leverage open architectures and established partnerships to enable secure, scalable integration across business systems.
By adhering to these principles, Indian businesses can transition from experimentation to execution, transforming AI from a buzzword into a genuine driver of productivity.
India’s enterprise ecosystem, characterized by a myriad of sectors and operational nuances, presents fertile ground for targeted AI adoption. Each industry’s specific operational context renders generic AI solutions less effective.
As organizations strive to redefine their data strategies, the focus should shift from accumulating more data to enhancing data quality. Companies that align AI with specific workflows, measurable outcomes, and open platforms will be better positioned to achieve sustainable scale and real value. “AI should not replace decision-making; it should empower it,” Samuelson remarked, encapsulating the foundational philosophy guiding these discussions.
Note: This article reflects insights from Kevin Samuelson, CEO of Infor, and Soma Somasundaram, President and CTO, shared at the Infor Velocity Summit 2025. Their observations emphasize the importance of context-driven innovation, operational value measurement, and industry specificity in successful AI adoption.
Published on October 28, 2025.
