Treasury transformation must be built on accountability and trust

By Tony Schweiss and Anderson Olson

Treasury modernization carries ambitious goals. Organizations pursue it to improve liquidity and risk management, increase efficiency, reduce idle capital, and enable better planning for growth, capital investment, and mergers and acquisitions.

Technology plays a central role in making that progress possible. Treasury management systems connected to enterprise platforms are shifting treasury from periodic reporting toward real time visibility into cash, liquidity, and risk exposure. Emerging capabilities such as advanced analytics and artificial intelligence can add speed, insight, and decision support.

Taken together, these tools create a compelling vision for treasury’s evolution from a largely transactional function into a more strategic partner to the business, but technology alone does not deliver that outcome.

How far an organization takes that transformation depends on its priorities, competitive pressures, and technical and financial readiness. Regardless of the path, modernization reshapes how treasury operates and how it supports the enterprise.

In organizations we work with, outcomes depend less on the sophistication of the tools and more on whether the operating model keeps pace with the technology. Two closely related factors consistently determine success: trust and accountability.

The human element determines success

Consider a scenario where advanced analytics recommends the timing, size, and structure of a foreign exchange hedge. The recommendation is based on predicted market movement, identification of natural hedges across the business, and simulations of cost versus earnings risk.

If that decision produces an unfavorable outcome, who owns it?

Is responsibility held by the treasury leader who approved the action, the team that configured the model, the technology group that selected the tool, or the vendor that built it?

Or consider a different situation. In a multinational organization with near-real-time treasury capabilities, automated systems rebalance cash across global subsidiaries to optimize liquidity. Who oversees those decisions? What controls are in place? When does human judgment step in, and how is that handoff defined?

Across enterprise transformations, these questions surface quickly once automation begins to influence material financial decisions. In both scenarios, accountability is the central issue. Without clear ownership, trust erodes. When trust breaks down, organizations lose confidence in the technology and in the people responsible for it.

Where accountability must land

Organizations that want to reduce that risk need to evolve their decision-making and governance in parallel with automation. As processing and analysis move closer to real time, governance often needs to shift from periodic review to more continuous oversight.

Building trust starts with clarity. Leaders should define responsibility before new tools are deployed, not after something goes wrong. That clarity includes decision ownership, escalation paths, and checks and controls that match the speed and impact of the new environment.

This is difficult work. It takes discipline to say, “This decision was mine, and it did not deliver the result we expected,” especially when the presence of new tools makes it easy to blur responsibility.

Trust can grow, especially when outcomes fall short — but only if teams avoid nonproductive blaming and focus on learning and productive critique. Process owners and impacted stakeholders should work together to understand what happened, identify root causes, and adapt tools, processes, or controls so the organization is stronger the next time.

Understanding uncertainty builds confidence

Training plays an important role in establishing trust. Formal education matters, but shared conversation and practical reinforcement matter just as much.

Business leaders need to understand that analytics and predictive models operate within probabilities, not guarantees. A model may show high confidence in a likely outcome, but many decisions still involve narrow margins and uncertainty.

The organization does not need to understand the mathematics behind every model. It does need confidence that treasury leaders understand the inputs, assumptions, limits, and tradeoffs, and that they apply judgment when making decisions.

Better tools reduce uncertainty. They do not eliminate it.

Trust grows through shared outcomes

As treasury transformation matures, improved visibility and more timely data lead to better decisions overall. Over time, these results reinforce trust across the organization.

Equally important, trust grows when there is a shared understanding of how decisions are made, how accountability works, and how the organization responds when outcomes miss expectations.

Senior leaders play a critical role in setting that foundation. When executives make accountability explicit and support treasury leaders as decision ownership shifts, they create the conditions for trust to grow alongside adoption.

Technology enables treasury transformation. People make it work.

As treasury continues moving into a more strategic, real-time role, organizations should invest as intentionally in trust, governance, and accountability as they do in systems and analytics. When accountability is clear and ownership is shared, technology becomes an amplifier of better decision making rather than a source of uncertainty.

Tony Schweiss is a managing partner, and Anderson Olson is a managing consultant and data services leader with The Gunter Group, a management consulting firm that partners with organizations to navigate complex transformation through grounded leadership, practical execution, and strong relationships.