AI Must Escape Labs to Deliver Real Enterprise Value
Artificial Intelligence will generate minimal value for companies if it remains confined to isolated laboratories and limited proof-of-concept projects. According to experts speaking at a recent webinar, genuine value emerges only when organizations completely reimagine their workflows with AI integration. True transformation occurs when AI becomes embedded into the very fabric of the enterprise as part of a comprehensive, CEO-led cultural change initiative.
Rethinking Workflows from the Ground Up
Sangeeta Gupta, Senior Vice President and Chief Strategy Officer at Nasscom, emphasized that too many companies continue to approach AI as a simple plug-in for existing processes. "If you have to get the value of AI and agentic AI, you have to rethink workflows end-to-end," she stated. This fundamental shift requires moving beyond incremental adjustments to complete operational redesign.
Tapan Sahoo, Executive Officer for Digital Enterprise at Maruti Suzuki India, provided a compelling real-world example. Recognizing that most vehicle owners never read their manuals, Maruti collaborated with a startup to develop an AI tool that allows customers to photograph dashboard warnings or faulty parts and receive step-by-step troubleshooting guidance. "It's perfect," Sahoo remarked about this transformation from manual lookup to AI-assisted problem-solving. The solution has already been deployed in India and may expand to Japan.
From Experimentation to Business Foundation
Sanjay Chalke, CEO for India at Capgemini, delivered a blunt assessment: "AI has to stop being something we experiment with and start something we run the business on. It's not about proof of concept, but it's about proof of impact." This requires embedding AI into core business functions including pricing strategies, customer service operations, supply chain management, and executive decision-making processes.
Chalke stressed that leadership must own this transformation. "It starts top down," he explained. "It's actually a CEO-level cultural shift. It's not a technology upgrade." Sahoo offered a powerful analogy, questioning whether companies treat AI as just another application or "like oxygen – silent, invisible, but indispensable." For true embedding, AI must progress from shopfloor implementations to boardroom strategy, creating value across the entire organization rather than improving isolated pockets.
The Critical Need for Enterprise Discipline
All panelists agreed that the primary barrier to AI adoption isn't lack of enthusiasm but rather insufficient enterprise discipline. Gupta observed that the corporate world has divided into leaders and laggards. Forward-thinking CEOs personally investigate AI's potential for their businesses, while others delegate responsibility to data science teams, treating AI as a peripheral project often hampered by concerns about hallucinations and reliability.
This is where AI governance becomes essential. Sahoo challenged the misconception that governance stifles innovation, arguing instead that it enables speed. "In a car, the brake is there for you so that you can accelerate faster," he illustrated. Using another automotive analogy, he noted that safety features are built into the chassis from inception rather than added after accidents – the same principle should apply to AI guardrails.
At Maruti Suzuki, different AI applications receive appropriate scrutiny levels. Drafting assistance emails undergo less rigorous evaluation than models predicting safety failures. The company has implemented a comprehensive risk evaluation matrix through which all AI projects must pass before deployment.
Overcoming Data and Talent Challenges
Data quality remains a central obstacle. Enterprises struggle with legacy systems and siloed information that hinder scaling AI initiatives across functions. Chalke identified seven critical levers for AI readiness: infrastructure, process architecture, talent development, performance metrics, business data quality, decision authority structures, and risk controls. He emphasized that "if data is poor, you can use any platform, any tool, you will never be successful."
Talent requirements have also evolved. Chalke noted that organizations need "AI native" professionals who are fundamentally comfortable working with artificial intelligence, not merely specialists trained in specific tools. "We can always say that everybody is trained," he acknowledged, "but what's more important for us is, how many of them are AI native."
Gupta reinforced that AI implementation must focus not just on efficiency and cost reduction but also on enhancing human intelligence in decision-making processes. The consensus among industry leaders is clear: AI's potential will remain unrealized until companies embrace comprehensive cultural transformation led from the executive suite.
