Indian firms lead in AI uptake, but scaling hits roadblocks

Indian enterprises are spearheading global AI adoption, with 40% reporting significant or full implementation—well above the global average of 28%—and nearly all organizations actively engaged in AI initiatives. This rapid uptake is reflected in the widespread integration of AI across critical business functions such as product development (62%), strategy and operations (56%), marketing and sales (55%), and supply chain (48%), signaling AI's central role in driving growth and operational efficiency. Deloitte India's 2026 State of AI in the Enterprise report underscores this leadership, while Dun & Bradstreet's survey confirms that 73% of Indian firms are already realizing measurable returns from their AI investments.

Investment momentum in AI among Indian enterprises remains robust, with over 90% expecting to increase AI spending in the coming year and no firms planning to reduce budgets, highlighting a strong commitment to AI-driven transformation. According to McKinsey's Global Tech Agenda 2026 and Dun & Bradstreet data, AI has overtaken cybersecurity and infrastructure modernization as the top technology investment priority, reflecting a strategic shift from viewing AI as a standalone tool to embracing it as a core capability underpinning productivity, resilience, and business transformation.

Despite widespread experimentation—nearly 90% of organizations—and early measurable returns with 69% expressing satisfaction with AI ROI, Indian enterprises face a pronounced gap in scaling AI beyond pilot projects. Only 7% have successfully scaled AI enterprise-wide, and a mere 4% possess data infrastructure fully ready to support such scaling, revealing significant workflow and data readiness challenges. This disconnect is further complicated by a prevalent measurement failure, with 57% unable to determine if AI investments yield returns, underscoring the need for more disciplined process management and strategic prioritization as only 17% currently adopt a holistic AI approach.

The AI journey in Indian enterprises is evolving from isolated experiments toward broader operational integration, with about 30% using AI in day-to-day operations and 26% embedding it into core business functions or deploying agentic AI capabilities. This progression is supported by 64% of technology leaders actively piloting or deploying agentic AI, signaling a shift toward more autonomous AI-driven workflows and new human-AI collaboration models that enable employees to focus on higher-value decision-making, as highlighted in Microsoft's 2026 Work Trend Index. However, despite this forward momentum, cautious funding persists, with over half of organizations allocating less than 10% of their IT budgets to AI, reflecting ongoing constraints amid ambitious productivity goals.

A critical barrier to scalable AI deployment in Indian enterprises is the pervasive lack of AI-ready data, with only 4% of firms considering their data infrastructure fully prepared for enterprise-wide AI, according to a Dun & Bradstreet survey. This shortfall stems not just from poor data quality but also from fragmented ownership and inconsistent business processes, which cause AI models to learn from flawed or misaligned definitions—such as differing interpretations of “active customer”—thereby replicating errors at scale and undermining trust in AI outcomes.

Maximizing AI’s business value hinges on investing heavily in clean, accessible data infrastructure rather than AI technology alone, as emphasized in the analysis 'Maximizing AI ROI by Investing in Clean Data and Partnerships.' Enterprises must prioritize data governance, assigning clear accountability for datasets much like budget lines, and establish shared business definitions to ensure data integrity before scaling AI models. This foundational work enables AI to move beyond simple automation to interactive decision-making that uncovers hidden opportunities and cost savings.

The challenge of scaling AI is compounded by legacy and fragmented data infrastructures that lack secure knowledge management and expose enterprises to vendor lock-in risks, especially in sensitive domains like legal AI. As noted in 'Legal AI's Biggest Threat Is Infrastructure And Vendor Lock-In,' winning vendors will be those who provide secure, compliant environments with robust access controls, enabling enterprises to safeguard proprietary know-how while integrating the latest AI models seamlessly. This infrastructure modernization is essential to build trust and operationalize AI at scale.

Successful AI adoption requires enterprises to modernize and standardize their data infrastructure and business processes, akin to building smooth roads for vehicles, as highlighted in marketing analytics contexts. Without consistent data definitions and aligned methodologies across business units, even accurate data can cause confusion and slow AI scaling. This analogy underscores the need to 'go back to basics' by fixing foundational data issues before layering AI capabilities, ensuring that the data 'wheel is spinning correctly' to support reliable and scalable AI deployment.

Despite Indian employees being largely AI-ready, with 78% of organizations using AI in at least one business function, entrenched enterprise workflows and outdated operating models significantly hinder scaling AI across organizations. As highlighted in The Economic Times and corroborated by Brookfield's deployment experience, the value of AI depends heavily on foundational workflows and systems, necessitating upfront operational transformation rather than simply layering AI onto existing processes. This underscores that technology alone is insufficient; rather, redesigning workflows to leverage AI capabilities is critical to unlocking true business value.

A major barrier to AI adoption is workforce resistance rooted in fears about job security and unclear career impacts, which stalls initiatives more than technology limitations. Experts emphasize the need for candid communication about how AI will affect roles, pay, and status to foster genuine buy-in. Bausch & Lomb’s CEO advocates embedding AI literacy as a core business skill and cultivating a culture of curiosity and adaptability through continuous learning programs like their partnership with Coursera, which empowers employees across functions to identify AI-driven improvements and share knowledge, thereby accelerating workflow redesign and adoption.

Successful AI scaling demands leadership-led organizational transformation that reshapes culture, governance, and decision-making beyond mere technology implementation. As Brookfield’s experience shows, AI initiatives thrive when driven by CEOs, CFOs, and COOs rather than IT alone, with governance tailored to the company’s unique risks to avoid 'governance theatre.' Leaders must orchestrate ecosystems integrating humans, AI agents, and digital workflows, shifting focus from task automation to designing intelligent decision systems aligned with strategic priorities, customer trust, and compliance, ultimately measuring AI success by innovation, customer experience, and business resilience rather than efficiency alone.

Bridging the gap between AI experiments and real business value hinges on treating AI adoption as a continuous, outcome-oriented learning journey rather than a one-off training event. Companies that excel are those investing in building capable people who apply AI effectively within business contexts, emphasizing project-based learning that improves customer experience and reduces risk. This shift from technology acquisition to workforce capability development is essential to overcoming workflow constraints and ensuring AI initiatives move beyond hype and failed pilots to deliver consistent, resilient operational improvements.

A critical barrier to scaling AI in Indian enterprises is the lack of genuine senior leadership buy-in and transparent communication about AI’s impact on employees. As highlighted in multiple analyses, AI initiatives often stall not due to technology but because leaders fail to honestly address how AI adoption will change roles, careers, and pay, leaving employees fearful of job loss rather than seeing personal benefit. Brookfield’s experience underscores that AI projects led by CEOs, CFOs, or COOs—not just IT—are far more successful, as top executives can drive the cultural and operational shifts necessary for adoption.

Tailored governance models with clear accountability are essential to move beyond 'governance theatre'—where policies exist but fail to ensure safe, effective AI use—and to embed AI responsibly within business operations. Indian enterprises face a governance gap, with 81% citing data privacy and compliance as top concerns but only 19% feeling prepared under India’s DPDP Act. Experts like Jatinder Singh emphasize that boards must treat AI investments as implicit risk acceptance, integrating AI governance alongside capital expenditure and cyber risk oversight, and requiring documented governance structures before scaling AI beyond pilots.

Effective AI scaling demands strategic coordination that rethinks workflows around AI capabilities rather than superficially adding AI to existing processes. Brookfield’s approach of replicating successful AI use cases across its portfolio companies demonstrates how ecosystem-wide synergy can accelerate value creation. This operational transformation involves designing intelligent decision systems aligned with business priorities and compliance, shifting leadership focus from mere automation to orchestrating ecosystems of employees, AI agents, data systems, and digital workflows working in concert.

Ultimately, the challenge in scaling AI lies in reshaping culture, accountability, and governance rather than technology adoption alone. Indian enterprises struggle to measure AI’s true business impact, with only 12% able to prove significant ROI, reflecting a measurement failure rather than performance failure. Leaders must therefore establish clear metrics tied to strategic value—such as innovation, customer experience, productivity, and resilience—moving beyond task automation efficiency to ensure AI delivers meaningful, sustainable business outcomes.