Financial Census 2026
AI adoption is accelerating. Trust hasn't caught up.
90% of finance teams require human approval for AI decisions.
Nearly half admit those rules aren't written down.
AI has moved beyond experimentation.
Finance teams are using it to automate approvals, analyze spending, forecast cash flow, and make recommendations every day. Many are already planning for autonomous, agentic AI to take on even more responsibility.
But while adoption is accelerating, trust is not.
The Medius Financial Census 2026 found that finance leaders increasingly rely on AI, but many still lack clear governance around who approves AI decisions, when humans should intervene, and who is accountable if something goes wrong.
AI and Trust: The governance gap
Finance teams are acting on AI recommendations—often without a rulebook in sight. Our 2026 Financial Census exposes the accountability vacuum growing inside organizations as agentic AI spreads faster than the governance meant to manage it.
Finance trusts AI…to a point.
Nearly every finance organization still requires humans to remain in the loop.
90%
require mandatory human approval for AI decisions.
46%
admit approval thresholds are understood but not formally documented.
45%
say their teams often act on AI recommendations without human intervention.
26%
say they almost always act on AI recommendations.
The result is a contradiction.
Finance professionals trust AI enough to influence decisions every day—just not enough to clearly define when it should make those decisions on its own.
Agentic AI is coming faster than many organizations expect.
The conversation around AI has shifted.
Organizations no longer ask whether they'll use AI, they're asking how autonomous that AI should be.
The Census found:
50%
plan to deploy agentic AI within the next 12 months.
38%
already have agentic AI operating within some finance processes.
54%
believe 10-25% if finance decision-making will become fully autonomous within three years.
69%
of those expecting greater autonomy say they're excited about it.
Finance leaders clearly see autonomous AI becoming part of the future operating model. The challenge is preparing governance for that future before technology gets there.
The biggest question isn't accuracy. It's accountability.
When asked who would be responsible if AI caused a financial loss or compliance issue, respondents were almost evenly divided.
28%
blamed the technology team.
27%
blamed the finance leader.
27%
blamed the employee who acted on the recommendation.
No single answer emerged.
That lack of ownership may become one of the biggest barriers to expanding AI across finance.
What would make finance trust AI more?
Finance professionals are not asking for smarter algorithms alone. They want confidence that AI decisions can be understood, governed and controlled.
The top trust builders were:
35%
Full explainability of AI decisions.
33%
Clear regulatory or audit framework.
32%
Human override at any time.
32%
Industry peers adopting similar autonomy.
31%
Proven accuracy over time.
31%
Board or CFO endorsement.
30%
Clear vendor accountability.
What's holding finance back?
The biggest barriers to AI trust aren't about capability. They're about confidence.
Finance leaders cited:
34%
Data quality concerns.
33%
Fear of accountability.
33%
Leadership skepticism.
33%
Organizational resistance to change.
29%
Limited explainability.
29%
Regulatory uncertainty.
Even more concerning, 23% of finance leaders believe employees are already using AI tools that haven't been formally approved.
This shadow AI is becoming a governance issue of its own.
Closing the AI governance gap.
Define human approval thresholds.
Document where AI can operate independently and where human approval is required.
Make every AI decision explainable.
Finance teams need to understand why AI reached a recommendation—they can’t simply accept the output.
Assign accountability.
Ownership should be established before deployment, not after an incident.
Standardize AI policies.
Approved tools and acceptable-use and compliance requirements should be consistent across the organization.
Build trust through transparency.
Audit trails, confidence scoring, and real-time monitoring help finance leaders adopt AI with confidence.
AI should increase confidence, not uncertainty.
At Medius, AI is designed to eliminate repetitive work while giving finance teams complete visibility into how decisions are made. That means intelligent automation with explainable recommendations, transparent workflows, and the controls finance leaders need to move faster—without giving up oversight.
Discover AI-powered AP automation built for finance governance.
AI governance in finance FAQs
AI governance is the framework that determines how artificial intelligence is used, monitored, and controlled within finance. It defines when AI can make recommendations, when human approval is required, who is accountable for outcomes, and how decisions are documented for audit and compliance purposes.
Finance teams are rapidly adopting AI for approvals, forecasting, spend analysis, and other workflows. The 2026 Financial Census found that 50% of organizations plan to deploy agentic AI within the next year, while 38% already use it in some finance processes. Governance helps ensure AI adoption scales without increasing operational or compliance risk.
Most organizations view AI as a decision-support tool rather than an autonomous decision maker. According to the Financial Census, 90% of finance professionals require mandatory human approval for certain AI decisions regardless of AI accuracy. Human oversight helps reduce financial, regulatory, and reputational risk.
Agentic AI refers to AI systems that can plan, make decisions, and complete multi-step business processes with limited human intervention. Rather than simply generating recommendations, agentic AI can execute tasks while operating within defined governance and approval boundaries.
The Financial Census found that trust depends less on AI capability than on governance. Finance leaders cited data quality, accountability, leadership buy-in, organizational resistance, and explainability as the biggest barriers to expanding AI autonomy.
Organizations should define accountability before deploying AI. Governance should clarify the responsibilities of finance leadership, technology teams, business users, and software vendors so ownership is clear if AI contributes to a financial or compliance issue.
Finance professionals ranked explainability, clear regulatory frameworks, human override capabilities, proven accuracy, and executive endorsement as the biggest factors that increase confidence in AI. Together these findings suggest governance builds trust more effectively than automation alone.
Shadow AI refers to employees using AI applications that have not been approved by the organization. These tools may introduce compliance, privacy, and security risks because they operate outside established governance processes.
Organizations should establish approval thresholds, document AI policies, implement explainability, define accountability, monitor AI decisions continuously, and ensure employees understand when AI can act independently and when human review is required.
Strong AI governance allows finance teams to automate invoice processing, approvals, and exception handling while maintaining transparency, auditability, and control. It enables organizations to gain efficiency without sacrificing oversight or compliance.