By Purushothaman KG- Partner and Head of Technology Transformation and AI, KPMG in India
Artificial intelligence (AI) has firmly established itself at the center of business strategy discussions worldwide, increasingly shaping the priorities and investments of organizations across every industry. As of mid-2026, however, the enthusiasm surrounding AI has transitioned into a period marked by heightened expectations and closer scrutiny. Many enterprises that once raced to adopt AI now face a pressing challenge: how to convert significant investments into tangible, sustainable returns. While AI’s potential remains undimmed, the journey from hype to true business value demands a disciplined approach and a willingness to rethink both execution and measurement.
Insights from KPMG International’s 2026 Global Tech Report indicate a substantial evolution in how organisations approach AI. The era of scattered experimentation characterised by launching numerous pilots and proof-of-concept projects has given way to a more strategic mindset. Today, successful organisations are narrowing their focus to high-impact use cases that directly influence revenue growth, operational efficiency, risk mitigation, compliance requirements, and improvements in customer experience. This shift reflects a growing recognition that AI must serve as an engine for measurable outcomes, not just as a showcase for technological prowess.
Crucially, the metrics used to gauge AI’s success are also undergoing transformation. Businesses are moving beyond surface-level indicators such as the number of pilots initiated or technologies trialed. Instead, they are prioritising rigorous metrics that speak to real business value: cost savings, reductions in process times, enhanced fraud prevention, improved accuracy and quality of decisions, and clear advancements in customer satisfaction and trust. These metrics provide a more accurate picture of AI’s contribution to the bottom line and help build the case for continued investment.
Despite these advances, the report identifies several persistent obstacles that undermine the realisation of strong AI returns on investment (ROI). Among the most significant are the lack of comprehensive governance structures, insufficient accountability at both executive and operational levels, fragmented and siloed data, and unmanaged risks associated with AI models such as bias, drift, or lack of transparency. In regulated industries like finance, healthcare, and energy, these issues are especially acute, as the stakes for compliance and ethical operation are exceptionally high.
Leading organisations are addressing these challenges by embracing a disciplined, methodical approach to AI implementation. They invest in robust governance frameworks that clearly define roles, responsibilities, and escalation paths for AI oversight. Responsible AI practices are embedded throughout the lifecycle, from data acquisition and model development to deployment and ongoing monitoring. This includes the adoption of explainable AI systems, which allow stakeholders to understand and trust the decisions being made by algorithms, an essential requirement for regulatory acceptance and public confidence.
The foundation for successful AI transformation is built on strong digital infrastructure. High-quality, well-governed data is the lifeblood of effective AI solutions. Without it, even the most advanced models struggle to deliver reliable results. Organisations are therefore prioritising investments in data modernisation, cloud-native platforms, and cybersecurity measures that both enable and safeguard the scaling of AI across the enterprise. Rather than treating AI as a standalone initiative, leading companies weave it into the fabric of their core operations i.e., Integrating intelligent automation, analytics, and decision support directly into business processes.
The human element remains a decisive factor in delivering AI ROI. Technology alone is insufficient; business ownership, clear alignment of roles and responsibilities, and tailored incentives are necessary to ensure sustained engagement with AI systems. Equally important is workforce readiness. Employees at all levels need to understand not only how to use AI tools, but also when to rely on their outputs and when to critically question them. Continuous learning, transparent communication, and a culture that values both experimentation and accountability foster the conditions where AI can thrive.
In summary, 2026 marks a clear turning point for AI in business. The organisations that distinguish themselves are those that move beyond ambition, embracing a culture of discipline and operational excellence. By embedding AI into core processes, focusing on high-value applications, establishing strong governance, and keeping people engaged and accountable, enterprises position themselves to deliver sustainable returns. As scrutiny from regulators, customers, and investors intensifies, and as margins for error shrink, only those who combine vision with rigorous execution will emerge as true AI leaders.