From insight to execution: how AI is reshaping corporate treasury decision making

September 2026  |  SPOTLIGHT | ACCOUNTING & FINANCE

Financier Worldwide Magazine

September 2026 Issue

For corporate treasurers, uncertainty is no longer episodic – it is a constant operating condition. Liquidity can shift across markets overnight, foreign exchange exposures can change as supply chains move, and geopolitical or regulatory developments can alter funding assumptions with little warning. In this environment, the challenge is not a lack of information, but the ability to connect data from across the organisation, global markets and banking relationships quickly enough to make confident decisions.

This is where artificial intelligence (AI) is beginning to reshape the treasury agenda. By combining large volumes of internal and external data, AI can identify emerging risks, surface opportunities and support faster, better-informed decisions. Rather than replacing treasury professionals, today’s most practical AI applications improve visibility, forecasting, risk monitoring and workflow efficiency, enabling treasury teams to become more proactive and capital efficient while preserving appropriate human oversight.

Better visibility leads to better decisions

Cash management is emerging as one of the clearest use cases for AI in corporate treasury. For multinational organisations, liquidity is often fragmented across subsidiaries, currencies, accounts and banking partners, making it difficult to maintain a current view of available cash or deploy it efficiently. AI-enabled tools can consolidate this information into a near-real-time picture of global liquidity, particularly when supported by banking infrastructure that connects activity across markets and currencies. That visibility allows treasury teams to identify idle cash, improve cash sweeping and optimise where funds are held to maximise returns. The results are tangible: more cash in higher-yielding accounts, lower operating costs and more efficient treasury processes.

Forecasting is undergoing a similar transformation. Traditional forecasts often rely on historical trends and periodic updates that quickly become outdated. AI can incorporate richer operational data, including sales pipelines, customer demand and regional business activity, to generate a more dynamic view of future cash flows.

Those improvements have direct implications for risk management. More accurate forecasts enable treasury teams to structure currency hedges more precisely, reducing the need to unwind positions when expected cash flows fail to materialise or to execute additional trades when business outperforms expectations. AI can also strengthen scenario analysis, helping treasurers understand how changes in sales, interest rates or market conditions could affect liquidity and funding needs before those changes occur.

From information overload to actionable intelligence

Treasury professionals must process far more than financial data. They also rely on economic research, market commentary, internal policies and information from banking partners to inform decisions. AI is increasingly helping teams cut through that complexity.

Today, its greatest value lies in organising and synthesising information, rather than making decisions independently. AI can summarise research, surface relevant insights and direct users to underlying source material, reducing the time spent searching for information and allowing treasury professionals to focus on interpreting it.

The technology also enables much faster responses to changing market conditions. AI can monitor exposures, identify significant market moves, update hedge valuations and alert treasury teams when predefined thresholds are reached. During periods of volatility, that speed can improve decision making without removing human judgment from the process.

Global banking partners are also becoming AI-enabled advisers. By combining market intelligence with transaction, liquidity and treasury data, they can help clients develop a more integrated view of their financial position and support more informed discussions around liquidity, funding and risk management. As these capabilities mature, the relationship between banks and corporate treasurers is likely to become increasingly proactive, with greater emphasis on strategic insight, cross-border perspective and disciplined execution alongside transaction delivery.

Ultimately, AI is changing how treasury teams spend their time. As routine analysis and information gathering become more efficient, professionals can devote more attention to evaluating strategic options, supporting business growth and making higher-value decisions. AI supplies the analytical speed, but treasury professionals provide the judgment.

The road from insights to execution

While AI is already delivering measurable value, its role in treasury is likely to evolve in stages rather than through a single technological leap.

The first stage focuses on organising institutional knowledge. Banks and corporations are using AI to make research, product information and internal documentation searchable and easier to access, reducing the time employees spend hunting for answers. The next stage builds on that foundation by applying AI to analytics and decision support, helping treasury teams monitor liquidity, forecast cash flows, identify exposures and evaluate potential responses to changing market conditions. And the third stage sees AI becoming more deeply embedded in treasury workflows, supporting activities such as cash management, valuations and risk monitoring.

Looking further ahead, organisations may eventually become comfortable allowing AI to execute certain routine treasury actions automatically, such as initiating cash sweeps or implementing pre-approved hedging strategies within tightly defined parameters. That final stage, however, remains largely aspirational. While technology continues to improve rapidly, most organisations are not yet prepared to delegate financial decisions directly to AI. Regulatory expectations, governance requirements and confidence in data quality must evolve before widespread autonomous execution becomes practical.

The continued importance of governance

For all the excitement surrounding AI, many of the biggest implementation challenges are fundamental.

Success depends first on the quality of the underlying data. Organisations must invest considerable effort in organising, tagging and validating information before AI can reliably interpret it. Internal research, market commentary and treasury data must all remain current and accurately indexed so that AI draws conclusions from the most relevant information rather than outdated material. As many organisations are discovering, preparing data for AI often requires far more work than deploying the technology itself.

Accuracy is equally critical. Consumer AI applications may tolerate occasional errors, but treasury cannot. Decisions involving liquidity, funding or risk management require a much higher standard of reliability, meaning organisations must rigorously test AI models before deploying them into production environments.

Human oversight therefore remains indispensable. Treasury teams need clear guardrails governing what AI can access, what recommendations it can make and where human approval remains mandatory. As banks and corporate treasuries begin sharing information through AI-enabled tools and agents, protecting confidential data, maintaining appropriate controls and operating within regulated environments will become even more important. Accountability for financial decisions should continue to rest with treasury professionals, even as AI assumes a greater analytical role.

Organisations can improve their chances of success by starting with focused, measurable use cases. Rather than attempting to transform the treasury function all at once, many are finding value by solving specific problems like improving cash forecasting, optimising liquidity or streamlining reporting. Demonstrating tangible benefits helps build organisational confidence, justify additional investment and create momentum for broader adoption.

The future of AI in corporate treasury

AI is reshaping corporate treasury not by replacing treasurers, but by augmenting their ability to make faster, better-informed decisions. As repetitive analytical work becomes increasingly automated, treasury professionals will have more capacity to focus on strategic priorities, including capital allocation, risk management and supporting business growth.

The organisations that derive the greatest value from AI are unlikely to be those pursuing automation for its own sake. Instead, they will be the ones that combine AI-powered insights with high-quality data, strong governance and experienced human judgment. For treasury teams operating across markets, currencies and regulatory regimes, the real opportunity will be to pair emerging technology with trusted financial expertise and disciplined execution.

Thomas Kikis is the head of markets for the US and Americas at Standard Chartered.

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Thomas Kikis

Standard Chartered