Building Trust in AI: The Next CFO Leadership Imperative
AI is reshaping finance, but trust is the differentiator. Here’s how CFOs and CIOs can govern, explain, and scale AI without losing control.
By Marko Kling, Vice President of Solution Architecture at Serrala
AI has moved from pilot projects to mission-critical finance operations. What once lived in planning tools or experimental tests now shapes forecasting, controls, and operational decisions that influence cash flow, compliance, and risk. This shift is no longer limited to early adopters.
Gartner’s latest survey found that 59% of finance leaders are currently using AI within the finance function. KPMG research shows that roughly two-thirds of companies are using it in accounting and financial planning, while nearly half are applying it in treasury and risk management.
As AI moves deeper into these core systems, a new leadership challenge emerges: trust. For years, CFOs have been stewards of financial accuracy and compliance. Today, that responsibility expands. As artificial intelligence in finance influences outcomes at scale, finance leaders become guardians of digital trust, accountable not only for results but for how those results are generated, understood, and governed. For CIOs and IT leaders, this evolution introduces both pressure and opportunity to work directly with CFO leadership in an AI-driven environment.
From hindsight to foresight requires cultural change
Finance organizations have traditionally operated in hindsight, reporting what already happened. AI promises foresight: predictive insights, scenario modeling, and early signals that guide decision-making. Realizing that promise requires more than new technology or emerging AI finance automation capabilities. It demands a cultural shift in how data is shared, governed, and trusted across the enterprise as part of a broader digital finance transformation.
That shift often begins with breaking down silos. AI solutions depend on broad, high-quality datasets that cut across business functions. When finance data remains isolated, models lose context and credibility. Trust grows when teams become more curious about the full financial picture and less protective of individual data domains, enabling meaningful finance process optimization rather than isolated gains.
Transparency is no longer optional
As AI-driven outputs influence forecasts or recommendations, leaders must be able to explain them. Black-box models undermine confidence, especially in regulated or high-impact environments. Explainability is not just a technical concern. It is a leadership requirement as organizations respond to accelerating finance automation trends and growing compliance demands.
Practical steps help make AI systems more transparent and auditable. Clear documentation of data sources, assumptions, and model behavior provides a baseline for accountability. Strong data governance ensures accuracy and consistency over time. Just as important, investing in AI literacy enables finance and IT teams to ask better questions and challenge outputs constructively rather than accepting them at face value.
Auditability also depends on resilient infrastructure and controls. Secure access, traceable changes, and monitored performance allow organizations to demonstrate how decisions were made, even as systems evolve. Trust erodes quickly when accountability cannot keep pace with automation.
Efficiency and compliance can reinforce each other
Automation often gets framed as a trade-off between speed and control. In practice, the two can strengthen each other when governance is designed from the start. Ethical standards, regulatory requirements, and regional rules provide a framework that guides responsible automation rather than slowing it down, particularly as artificial intelligence in finance becomes more embedded in operational workflows.
Leadership plays a critical role here. Clear principles set by executive teams help align innovation with accountability. When expectations around ethics, data use, and oversight are explicit, teams can automate with confidence instead of caution. Over time, this clarity reduces risk while allowing organizations to scale AI finance automation more predictably.
Trust is built across functions, not within them
No single department can build a trustworthy AI ecosystem alone. Finance, IT, and data science each bring essential perspectives, and isolation creates risk. Data teams may optimize accuracy without explainability. IT may prioritize stability in ways that limit flexibility and openness for data inclusion. Finance may focus on outcomes without visibility into underlying processes.
Frequent, honest collaboration closes these gaps. Successful organizations treat AI as a shared responsibility, where technical outputs translate into financial outcomes and governance expectations apply across teams. CFOs and CIOs play a pivotal role by aligning incentives, setting shared standards, and reinforcing collaboration from the earliest stages of AI initiatives — an increasingly critical aspect of modern CFO leadership.
Trust pillars for AI in finance
| Pillar | What it enables | Why it matters in finance |
| Governance | Clear ownership, controls, and accountability for AI-driven decisions | Ensures AI outputs meet regulatory, ethical, and audit expectations as automation scales |
| Transparency | Explainable models, documented assumptions, and traceable decisions | Builds confidence in forecasts, recommendations, and controls, especially in high-impact or regulated contexts |
| Collaboration | Shared responsibility across finance, IT, and data teams | Prevents siloed decisions and ensures technical outputs translate into trusted financial outcomes |
A shared leadership mandate
As AI becomes embedded in core financial operations, trust becomes a strategic asset. CFOs increasingly act as stewards of that trust, while CIOs ensure the systems behind it are secure, transparent, and resilient. Together, they define how automation serves the enterprise rather than obscuring it, reinforcing the long-term goals of digital finance transformation.
The next phase of AI adoption will not be judged by how advanced models become, but by how confidently organizations can rely on them. In that sense, building trust is not a constraint on innovation. It is what allows innovation to endure.
Marko Kling is the Vice President of Solution Architecture at Serrala and leads the global pre-sales strategy, ensuring seamless integration and solution alignment across all business entities. With over 17 years at Serrala, he has been instrumental in shaping solution strategies, business development, and partner management while overseeing a global team of process and solution experts across EMEA, the Americas, and APAC.