The corporate treasury landscape in India is experiencing a seismic shift. For decades, treasury teams have operated as guardians of liquidity and risk, managing cash flows and compliance through largely manual, spreadsheet-driven processes. Today, that model is becoming obsolete. According to the EY India Corporate Treasury Survey 2025, which analyzed responses from 85 treasury leaders, nearly 50% of Indian treasury and banking leaders now rank automation as their top investment priority[1][2]. This represents far more than a tactical upgrade—it signals a fundamental reimagining of how corporate treasuries will operate in the coming decade.
The urgency behind this shift is clear: economic volatility, regulatory complexity, and rapid digitization are forcing treasury teams to do more with less. They must automate without losing control, manage risk while enabling growth, and deliver predictive, real-time insights for strategic decision-making. For Indian banks and financial institutions, the question is no longer whether to invest in automation and AI, but how quickly they can implement these technologies to remain competitive.
The Current State: From Experimentation to Execution
Indian treasuries are at a critical inflection point in their AI adoption journey. The data is striking: 82% of organizations are either planning or actively progressing toward AI adoption[1][2]. This represents a dramatic acceleration from earlier phases of tentative experimentation. Treasury teams are no longer asking whether AI works; they’re asking how to scale it across their operations.
What makes this transition significant is the shift in mindset. Treasury leaders have moved beyond pilot projects and proof-of-concepts. They’re now focused on execution, aiming to reduce operational bottlenecks, improve accuracy, and free up resources for strategic decision-making. This represents a maturation of AI adoption in the treasury space—from exploring possibilities to implementing concrete solutions.
Key AI Use Cases Gaining Traction
Several AI applications are emerging as high-impact priorities for Indian treasury teams:
Cash Forecasting and Liquidity Management: This is the leading AI use case, with 26% of respondents already piloting AI-led models[1][2]. Cash forecasting is particularly well-suited for AI because it relies on historical data, operates on a daily cadence, and has direct business impact. By predicting inflows, outflows, and surpluses more accurately, AI-enabled cash forecasting helps treasuries optimize working capital and reduce unnecessary borrowing costs.
Foreign Exchange Risk Management: Early-stage AI applications in forex risk management are gaining momentum, with 9% of respondents piloting these solutions[1]. AI can analyze complex currency patterns, identify hedging opportunities, and automate routine forex transactions, reducing both manual errors and operational delays.
Trade Finance and Working Capital Optimization: 8% and 6% of respondents respectively are exploring AI in trade finance and working capital optimization[1]. These use cases reflect growing ambitions to integrate AI across core treasury processes, potentially unlocking significant efficiency gains and supporting faster, data-driven decisions.
Anomaly Detection: Beyond specific functional areas, anomaly detection powered by AI is becoming a critical tool for identifying fraudulent transactions, unusual payment patterns, and operational irregularities in real-time[1].
The Automation Imperative: Why Now?
Breaking Free from the Spreadsheet Trap
One of the most revealing findings from the survey is this: over 70% of treasury teams still rely on disparate spreadsheets and disaggregated historical data[2]. Despite decades of enterprise software development, spreadsheets remain the backbone of treasury operations in many Indian organizations. This creates multiple problems: data silos, version control issues, limited scalability, and vulnerability to human error.
The cost of this manual dependency is substantial. Treasury teams spend disproportionate time on data consolidation, reconciliation, and reporting instead of focusing on strategic analysis and decision-making. Automation addresses this directly by integrating data sources, standardizing processes, and enabling real-time visibility across treasury operations.
The Talent and Skills Challenge
Another critical driver of automation investment is the evolving talent landscape. Treasury teams recognize that 49% of respondents report a 50:50 split between functional and technology roles, while 35% favour a 70:30 split toward functional expertise[1]. This reflects a fundamental shift in how treasuries must be staffed—they need professionals who can navigate both finance and technology.
However, upskilling and talent transformation are lagging behind technological investments. Nearly two-thirds of treasury teams cite weak reporting and dashboarding as a core challenge, pointing to a gap in real-time visibility[2]. By automating routine tasks, treasuries can free internal teams to focus on higher-value activities like strategic planning, risk analysis, and stakeholder engagement. This also makes treasury roles more attractive to talent, as they shift from transaction processing to value creation.
Real-Time Decision-Making as a Competitive Advantage
In an environment of rapid market movements and regulatory changes, the ability to make decisions based on real-time data is increasingly a competitive advantage. Traditional treasury operations, constrained by manual processes and delayed reporting, cannot respond quickly enough to emerging opportunities or risks. Automation and AI enable treasuries to operate on real-time data and intelligent systems, allowing them to anticipate risks, accelerate decision-making, and capture strategic opportunities faster than their peers[1].
The Operating Model Evolution: Hybrid and Modular Approaches
Automation isn’t just about technology—it’s also driving a fundamental redesign of treasury operating models. Indian organizations are increasingly adopting modular and hybrid structures that combine in-house expertise with outsourced services.
The data reveals clear patterns: 35% of organizations are partially or fully outsourcing treasury technology application maintenance, 25% are outsourcing back-office accounting, and 11% are outsourcing front-office dealing operations[1][2]. These moves free internal teams to focus on strategy and value-added activities while reducing the risk of technology mismanagement and operational inefficiency.
Emerging use cases like electronic bank account management (eBAM) and POBO/ROBO structures are becoming high-potential areas for managed support, helping organizations drive faster maturity, enhanced controls, and round-the-clock treasury coverage. This hybrid model allows treasuries to scale without proportionally increasing headcount—a critical consideration in an environment where skilled treasury professionals are in short supply.
Critical Gaps That Must Be Addressed
Despite the optimism around automation and AI, the survey identifies several critical gaps that could hinder progress if left unaddressed:
Data Infrastructure Fragmentation: With over 70% of treasury teams still relying on spreadsheets, the foundation for AI implementation remains weak. Organizations must invest in integrated data platforms and governance frameworks before AI can deliver its full potential[2].
Reporting and Visibility Deficits: Nearly two-thirds of treasury teams cite weak reporting and dashboarding capabilities. Real-time visibility is essential for AI-driven decision-making, yet many treasuries lack the tools to aggregate and visualize data effectively[2].
Skills and Change Management: Upskilling and talent transformation are lagging behind technological investments. Organizations must invest in training programs, hire professionals with hybrid finance-technology skills, and manage the cultural shift required for successful automation adoption[2].
The Treasury of 2030: A Vision of Digital Maturity
Looking ahead, the survey outlines a compelling vision for the treasury function in 2030. The future treasury will be digitally native, operating on real-time data and intelligent systems, staffed by cross-functional specialists fluent in finance, technology, and transformation[1]. This is not a distant aspiration—it’s a roadmap that organizations must begin executing today.
In this future state, treasury will transcend its traditional role in liquidity and compliance management. Instead, treasuries will anticipate risks, shape capital allocation decisions, and safeguard organizational resilience. They will operate as lean, exception-driven functions supported by intelligent automation, freeing human expertise for strategic analysis and decision-making.
Organizations that invest in platforms, processes, and people today will be positioned to capture strategic opportunities faster than their peers. Conversely, those that delay automation investments risk becoming operationally constrained and strategically disadvantaged.
What This Means for Indian Banks and Financial Institutions
For Indian banks and corporate treasuries, the message is clear: automation is no longer a cost-reduction initiative—it’s a core design principle for treasury operations. The 50% of leaders ranking automation as their top investment priority[2] reflects a collective acknowledgment that traditional, manual-heavy operations cannot support the speed, scale, and complexity of the decade ahead.
The path forward requires action across three dimensions:
Technology: Organizations must invest in integrated treasury management systems (TMS) and AI platforms that can consolidate data, automate processes, and deliver real-time insights. The traditional TMS model is no longer sufficient; modern treasuries need flexible, modular platforms that can evolve with their needs.
Processes: Automation is an opportunity to reimagine treasury workflows from the ground up. Rather than simply automating existing manual processes, organizations should redesign their operating models to be exception-driven, data-centric, and aligned with strategic objectives.
People: The most critical success factor is talent. Organizations must invest in upskilling existing treasury professionals, hiring specialists with hybrid finance-technology expertise, and creating a culture that embraces continuous learning and innovation.
Conclusion: The Time to Act Is Now
The transformation of corporate treasury in India is not a future possibility—it’s happening now. With 82% of organizations actively progressing toward AI adoption and 50% ranking automation as their top priority, the trajectory is clear[1][2]. The question is not whether your organization will undergo this transformation, but how quickly and effectively you’ll execute it.
The data is compelling, the business case is strong, and the competitive pressure is mounting. Treasury teams that embrace automation and AI today will operate more efficiently, make better decisions, and create more value for their organizations. Those that delay risk falling behind, constrained by legacy processes and unable to respond to market changes with the speed and precision that modern finance demands.
For Indian banks and financial institutions, the treasury of 2030 is being built today. The time to invest in automation, upskill your team, and redesign your operating model is now.
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