Martijn Gribnau is Chief Customer Success Officer at Quant
The financial sector is in an era of rapid automation, real time analytics, and increasingly autonomous systems. Agentic AI, which can independently analyze data, make recommendations, and initiate tasks, is fast becoming central to everything from fraud detection to liquidity management. But, adoption is slow and trust is hard to come by. Only 21% of people in the finance world trust agentic AI.
In an industry where decisions carry regulatory, ethical, and economic weight, even a small lack of trust will slow adoption, decrease ROI, and create hesitation.
Building trust in agentic AI will be an organizational challenge. It takes all of leadership working together to improve transparency, strengthen data governance, and build internal early adopters and champions of the technology. Financial institutions that take the trust challenge head on will position themselves on the starting line pole position of the most advanced technology to ever benefit finance.
Enhance Transparency and Explainability
Trust begins with understanding. When a team doesn’t know how AI works, or how it makes decisions then skepticism is natural and understandable. Traditional AI models struggle with explainability, so when you add agentic AI to the mix it’s a whole nother layer of complexity that your team will have to understand before accepting. In an industry of strict regulations and audit requirements, lack of transparency is unacceptable.
Explainable AI will be the foundation of trust. Institutions should invest in systems that can clearly show how decisions were made, what data was used, and which rules or thresholds guided the outcome. This level of clarity is essential for resolving customer disputes, supporting compliance teams, and giving risk managers confidence that the system behaves predictably under pressure.
Every AI driven decision should be accompanied by a detailed record that documents the logic behind it so the trail can be audited wherever desired. Traceable records allow institutions to provide regulators with a clear chain of reasoning, turning AI from a black box into a verifiable partner.
Clean, open, and honest communication is vital. Outcomes must be explainable in terms and vocabulary that both employees and customers understand. Dashboards, reporting, and visuals should be used to demystify the process and help stakeholders see that AI is not arbitrarily making choices but operating within a well defined framework.
Implement Robust Data Governance and Security
Because financial institutions handle some of society’s most sensitive information, concerns around data handling and security are particularly scrutinized. Then, take into account that only 41% of people trust AI and it is easy to see why staff would be unlikely to embrace agentic AI.
Strong data governance is the first and most critical step to building trust and keeping your customers and employees secure. To build a solid wall around data you have to deploy encryption, anonymization, and secure storage to safeguard data integrity and build confidence that AI systems adhere to privacy standards. This will help ensure that AI powered decisions remain compliant and that sensitive information cannot be accessed.
Security must be prioritized at every layer of your AI stack. Enterprise grade security controls and strict access management reduce the risk of data leakage or unauthorized use. Privacy techniques such as federated learning and synthetic data can further build trust by giving AI a safe place to train without exposing raw customer information.
Foster Employee Understanding and Buy In
Confidence in AI ultimately depends on the people who use it. If employees don’t understand agentics, or feel threatened by them, then mistrust will spread like wildfire throughout your organization.
Employees need education on how agentic AI functions, what problems it solves, and how it will support, not replace them. Create opportunities for hands-on experimentation to build familiarity. When staff can test tools in a low pressure environment, they will develop a better understanding of what it can and cannot do.
Another powerful approach to trust building is creating an advocacy team comprised of people from different departments and different levels of your organization. Requiring human review and approval for critical decisions lets companies develop a proper balance between automation and accountability. Employees then learn that AI enhances their role rather than replacing it, which will ease concerns about job security.
Frame AI as augmentation, not substitution.
Reinforce through Repetition and Close the gap for Good
Trust is not built once. It must be maintained via continuous validation. This ensures AI systems remain fair, accurate, and aligned with both regulatory and business standards. Regular audits allow institutions to identify bias, performance anomalies, and unintended behavior.
Agentic AI will play a defining role in the next decade of financial services, but its success depends on trust. By combining transparent systems, strong data governance, employee engagement, and continuous validation, financial institutions can build confidence in AI driven decision making. For leaders willing to take these steps, the trust gap moves from being a barrier to an opportunity to create a more resilient, compliant, and forward looking organization.
Agentic AI is here. Don’t waste time debating if you should implement it now or later. It’s important to focus on closing the trust gap, but not at the cost of moving forward. The AI wave forces businesses in a compressed timeframe. Ensure that creating buy-in doesn’t take so much effort that you miss your chance to grab your future by the reins and win the next decade.
Martijn Gribnau is Chief Customer Success Officer at Quant, which develops cutting edge digital employee technology. He is a former insurance and banking CEO.