Artificial intelligence is no longer on the horizon for finance teams. It’s already inside the building. Two-thirds of finance professionals say they’re currently using or piloting AI, according to the Yooz 2026 AI in Finance Report, a survey of 500 finance professionals conducted in January 2026. Confidence is rising, and there’s a real appetite for more.

Translating that momentum into results that you can see in business outcomes requires more than deploying the right tools. It requires leaders across the organization to actively build the conditions for AI to succeed. That means removing the barriers that are actually slowing progress, putting the right governance and infrastructure in place, and making AI adoption a shared organizational priority rather than a technology department initiative.

The Barriers Are Internal, and Leadership Can Address Them

The most important finding in the Yooz data reveals what’s preventing teams stalled in AI progress. When finance professionals are asked to identify the single biggest barrier slowing AI adoption, 26% cite a lack of training or education. Another 25% point to a lack of trust in AI outputs. Budget constraints register at just 10%. Regulatory concerns come in at 12%.

This tells a fundamentally different story than the one that often gets told about AI hesitancy in finance. The roadblocks aren’t primarily about money or external regulation. They revolve around whether people feel equipped to use AI well, and whether they trust what it produces enough to act on it in workflows.

Both of those barriers are within leadership’s power to address, and addressing them is a people, process, and culture challenge. That requires visible, sustained commitment from leaders at multiple levels of the organization.

What Finance Leaders Need To Do

The survey reveals a striking gap in ownership. Only 13% of respondents say the CFO or VP of Finance is the primary driver of AI adoption in their organization. IT leads at 24%, and 22% say no one in particular is steering the effort.

That distribution needs to change. When IT drives the finance team’s AI adoption without active finance leadership involvement, the result is often tools that are technically available but not practically integrated. They end up disconnected from the workflows that matter, unsupported by the governance frameworks that build trust, and not clearly tied to the outcomes finance leadership actually cares about.

CFOs and finance leaders who want to accelerate AI adoption should start by taking explicit ownership of the agenda. They can start by setting priorities for where AI will be deployed first and why, and investing in the training and education that gives teams the literacy they need to use it confidently. They can also set standards to measure whether AI is reducing manual touchpoints, strengthening controls, and freeing up capacity for higher-value work.

What Functional Leaders Across the Organization Need To Do

Effective AI adoption in finance doesn’t happen in isolation. It depends on cross-functional collaboration with IT, operations, legal, and compliance to build the infrastructure and governance frameworks that make AI safe to deploy at scale.

IT leaders play a critical role in ensuring that AI tools integrate cleanly into existing ERP and financial systems, that data quality standards are maintained, and that the security and access controls appropriate to financial workflows are in place. Without that technical foundation, AI outputs will be unreliable, which feeds directly back into the trust barrier identified by the survey as one of the top obstacles to adoption.

Legal and compliance leaders need to be involved early in defining the guardrails governing how AI is used in financial processes, particularly in high-stakes areas such as fraud detection and payment approvals. When those guardrails are well-designed, they actively build confidence, enabling teams to extend AI into more sensitive and consequential workflows.

Operations and process owners across the finance function have a role to play in ensuring that the workflows AI is being asked to support are well-defined enough to support it. AI performs best when it operates within standardized, repeatable processes. Where those processes are inconsistent or poorly documented, building that foundation is a prerequisite for getting value from AI.

What The Data Says About the Opportunity

The upside of getting this right is significant. Only 19% of finance teams currently use AI for fraud detection, compliance, or risk management, despite payment fraud being a growing threat to the vast majority of organizations. Only 18% apply AI in accounts payable or receivable, even though these are precisely the workflows where AI-driven automation can catch errors before they become losses, and provide real-time visibility into cash flow and vendor risk.

32% of respondents cite time savings from manual tasks as the top benefit they’ve seen from AI so far. But 33% say they haven’t yet seen clear benefits. That figure reflects the reality that deploying tools in isolation, without process integration and leadership alignment, tends to yield isolated results.

To close this gap, organizations need to treat AI adoption as an enterprise capability-building effort rather than a series of departmental experiments. That requires leaders who are willing to invest in training, process design, governance frameworks, and cross-functional coordination that make AI adoption durable rather than episodic.