Operationalizing Trust: Bridging the AI Trust Gap in the Future of Finance

Manish Vrishaketu
By Manish Vrishaketu
Manish Vrishaketu

Manish Vrishaketu

Chief Customer and Operating Officer

Manish Vrishaketu as Chief Operating Officer, brings over 18 years of extensive payments and fintech experience to the company. Vrishaketu is responsible for establishing and maintaining key banking and payment partnerships, while leading Tipalti’s global customer success, client onboarding, payment operations and support organizations. Most recently, Vrishaketu served as President of Americas at GoSwiff, a leader in mobile payments in emerging markets and prior to that, he was VP of Business Development and Product Strategy at Fiserv (Nasdaq: FISV) leading new market expansion initiatives in business to consumer (B2C) disbursements, bill payment and other electronic payment categories. Before Fiserv, Vrishaketu served as General Manager of CashEdge, a consumer and business payments technology provider to banks, where he introduced leading consumer applications to money movement and risk management and led their India division. During his tenure, the company grew its revenue tenfold, processing over $50B in payment volume annually, before it was acquired by Fiserv.

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Updated December 10, 2025
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Finance trends shift fast—explore 5 key processes & tips to stay ahead.

I’m excited to share insights from our recently commissioned study, The State of AI in Finance: Exploring the AI Trust Gap. The message from the research survey of 500 finance professionals across the US, Canada, and the U.K. is clear: AI is no longer a question of if, but a question of how soon and how much control.

AI is already transforming the finance function, offering a path to improving accuracy, speed, and decision quality for the business. However, mainstream AI adoption across all roles within the finance function is in the process of overcoming concerns around trust in the accuracy and reliability of the data, ethical considerations, and organizational readiness.

High Hopes and Lingering Trust Concerns

The finance industry is remarkably optimistic about AI’s potential, with 55% of all respondents reporting they are “extremely optimistic” about the benefits. They are eager for AI to take over the repetitive, tedious parts of their jobs , freeing them to focus on more strategic analysis and value-add tasks.

The key drivers for AI adoption are centered on practical, measurable improvements, confirming that finance professionals lead with a pragmatic mindset. The most pressing goals are:

  • To save time (64%)
  • To improve the quality of work/accuracy (62%)
  • To enhance decision-making (58%)

These factors—productivity and quality—outrank even cost savings as the main motivation for adoption.

However, this optimism is moderated by a healthy level of caution. Nearly half of all respondents (48%) are either somewhat or extremely concerned about the risks associated with using AI. The biggest worries are the cornerstones of trust : data privacy, security, and compliance issues (31%). As one respondent put it, “If you can’t trace how an answer was made, you can’t trust it”.

The Trust Gap: Where Barriers to Adoption Cluster

The major barriers to widespread adoption aren’t a fear of being replaced by AI, but rather concerns about ensuring the technology actually works within the existing structure and context of finance teams. The top barriers to adoption are:

  • Integration with existing/legacy systems (61%)
  • Data quality and standardization (57%)
  • Lack of in-house AI expertise (41%)

Notice a theme? The top three barriers are entirely about the ability to trust the underlying data and technology. These issues—integration, data quality, and expertise—are all addressable. Organizations that invest in explainability, auditability, and education will progress faster and sustain adoption longer.

From Automation to Elevation: The Journey of AI Usage

Today, AI in finance is heavily concentrated in practical tasks focused on efficiency. We see the highest regular usage in areas where repeatable data patterns exist:

  • Financial analysis/benchmarking (63.5%)
  • Generating reports and insights (62%)
  • Cash flow forecasting/predictive modeling (58%)

While many organizations are automating reporting and fraud detection, they have yet to fully leverage AI for more strategic uses like strategic planning or predictive finance.

The good news is that those who have adopted AI are seeing quantifiable benefits: 61% of finance professionals can easily quantify AI’s ROI. It’s saving them time, improving work quality, and enhancing decision-making. These tangible benefits are what motivate teams to overcome the barriers.

What Finance Needs: Visibility, Configuration, and Control

When we asked finance professionals what attributes they value most in an AI product, the answers underscored the need for human oversight and confidence. The capabilities rated as “extremely important” reflect a desire to remain in the driver’s seat:

  • See and review the action AI takes (55%)
  • Custom-configure AI to automate specific tasks in a specific way (55%)
  • Have my AI advise me and offer proactive recommendations (54%)
  • Not lose control of decisions and processes to AI (50%)

Interestingly, the ability for AI to operate autonomously and take action on its own ranked the lowest at 46%. Trust is built over time.

The Path to Trust: Organizational Takeaways

The path forward is clear: 2026 is about operationalizing trust in AI systems. This is the new maturity phase, defined by governance, integration, and visibility. Finance professionals are explicitly asking for specific improvements to bridge the trust gap:

  • Stronger governance frameworks (ethics, transparency) (52%)
  • Clearer accountability for AI decisions (47%)
  • Improved data lineage and quality controls (45%)

The future of finance will be written by those who are first to overcome this AI trust gap. Governance is no longer an afterthought. It is a prerequisite for scaling AI responsibly. This is about creating an organizational architecture—systems, skills, and norms—that enables AI to operate with transparency and confidence.

Recommendations for Finance Professionals and Teams

Based on the findings of The State of AI in Finance, here are two critical takeaways that finance professionals should consider to successfully operationalize trust in AI:

  • Look for Visibility and Control in AI Workflows: When selecting and implementing new AI solutions, treat explainability and auditability as non-negotiable requirements. Prioritize tools that allow your team to see, review, and override automated actions. Insist on detailed audit trails and the ability to custom-configure how AI automates specific tasks. This focus ensures that human expertise remains central, directly addressing the core need for confidence and control.
  • Make Foundational Readiness a Strategic Priority: Do not skip the necessary groundwork. Finance teams must proactively address the largest barriers to adoption by focusing on data quality and system integration first. Furthermore, actively seek out and utilize role-specific training. Embrace AI not as a threat, but as an opportunity to uplevel your team’s skills and shift headcount from mundane transactional tasks to higher-value decision support and business partnering.

The true promise of AI lies not just in the technology itself, but in the strategic power it unlocks for finance teams who learn how to trust and use it most effectively. The finance organizations that operationalize trust now will not only secure a distinct competitive advantage but will also help redefine the future of the finance function for years to come.

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