Tipalti: Finance's AI Trust Gap Is Real - Here's How to Close It in 2026
AI use in finance is rising, and the results are hard to ignore. According to Tipalti's new report, 98% of finance professionals say AI is important to their function, and over half are highly optimistic about its potential. Still, momentum is slowing as teams grapple with trust, data quality, and integration with legacy systems.
The takeaway: AI is delivering value, but leaders are hesitating to scale without clearer oversight, visibility, and control.
AI is paying off - when teams can trust it
Among regular users, AI is already improving quality of work (98%), decision-making (97%), and cost savings (96%). Practical use cases are well established: financial analysis (63%), reporting (62%), forecasting (58%), and fraud detection (57%). The real benefit is time back for higher-value work instead of repetitive tasks.
The trust gap that stalls scale
Even with 61% able to quantify ROI, 58% still worry about AI-related risks. The biggest roadblocks: data privacy and security, integration with older systems, and limited in-house expertise. As Tipalti's Chief Customer and Operating Officer Manish Vrishaketu put it, "trust is now the gating factor between incremental automation and true, strategic transformation."
Finance teams say trust grows with control and visibility: the ability to review AI actions (55%), custom-configure workflows (55%), and ensure humans don't lose decision control (54%). As AI embeds across reporting, compliance, payments, forecasting, and planning, the gap between teams that can audit and scale AI - and those that can't - will widen.
2026: Turn AI trust into operational reality
The report signals a clear agenda for the next 12 months: make trust operational. That means governance, better data quality, transparency, and targeted upskilling. Specifically, finance professionals are calling for the following investments:
- Stronger AI governance frameworks (52%) and clear approval paths.
- Defined accountability for AI decisions (47%) with audit-ready logs.
- Improved data lineage and quality controls (45%).
- Role-specific training for finance teams (43%).
Helpful frameworks can speed this up. See the NIST AI Risk Management Framework for practical guidance on controls and oversight: NIST AI RMF.
Practical checklist for finance leaders
- Set measurable AI KPIs: cycle-time reduction, exception rates, explainability coverage, fraud loss rate, and audit completion time.
- Demand vendor transparency: SOC 2/ISO 27001, data residency options, detailed action logs, explainability, and API-first integration with your ERP.
- Keep humans in the loop for approvals on material transactions, policy exceptions, and model changes.
- Clean the data pipeline: standardize vendor/supplier records, codify master data ownership, and enforce quality checks before models run.
- Upskill by role (AP, FP&A, Treasury, Risk) so teams can review AI outputs and challenge assumptions. For structured, finance-focused upskilling, explore: AI courses by job and curated AI tools for finance.
Why this matters now
AI's value isn't the debate anymore - trust is. The edge in 2026 will go to teams that standardize oversight, measure real impact, and keep human expertise at the center of key decisions. Do that, and AI becomes a dependable engine for growth, compliance, and sharper judgment.
Read the full report: The State of AI in Finance: Exploring the AI Trust Gap.
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