The 2026 Mandate: Why AI Governance and XAI Will Define Finance’s Future

As we look toward 2026, the question of whether or not to adopt artificial intelligence is over. The focus has firmly shifted to execution for finance and accounting teams. As companies struggle with constrained budgets and manual, time-consuming tasks, teams are intrigued by AI’s ability to help teams get more work done.  

But as we look ahead, these digital transformation efforts will no longer be measured by the speed of automation from the new tools. Instead, there will be a higher focus on the data integrity of AI deployments and questioning if they’re built on strong governance frameworks. 

Risks of AI adoption in finance

There will be a stronger focus on governance with the future of AI adoption. Finance and accounting departments have zero tolerance for inaccuracy, and rushing AI implementation without the right governance frameworks in place is risky and could present costly problems. 

If companies integrate AI without the proper foundations of data integrity and governance guardrails in place, it becomes incredibly difficult to verify AI outputs. Without that visibility, executives cannot confirm the source or reasoning process behind the AI’s outcomes. This lack of verifiability can result in significant financial losses, ranging from reputational damage and customer distrust to substantial regulatory fines.

Not only are AI models being deployed without the necessary foundations, but many organizations are also failing to take the critical steps to achieve the tools’ full value. Many spent massive budgets during the initial AI hype yet struggled to achieve return on investment. While top-performing companies are generating over 10x ROI on their AI investments, 61% of companies surveyed by McKinsey still report no enterprise-level financial impact, stuck in a costly pilot purgatory.

Pitfalls of unskilled adoption

Beyond the foundational governance risks, many organizations are struggling to achieve value because they are failing to address the critical human element of successful adoption. This rush to implement new technologies without upskilling teams to understand how these tools work and how to use them efficiently and effectively is a major factor in the lack of value. The full value of AI processes cannot be achieved if teams lack the knowledge to leverage AI’s opportunities and manage its risks.

Massachusetts Institute of Technology’s recent GenAI Divide: State of AI in Business report found that despite $30-40 billion in enterprise investment, 95% of organizations are seeing zero return on their AI initiatives. If companies continue investing budgets into AI without having the right foundations in place to receive value from the technology, it will continue to create an unsustainable infrastructure for AI spending that will likely fail in the near future.

Achieving successful AI adoption

To break free from this “pilot purgatory” and transform AI from a budget drain into an exponential investment, organizations must establish embedded, real-time internal controls and ensure full auditability of every AI action. This will build greater trust and transparency with the AI models, ultimately allowing teams to extract more value from the technology, validate outputs, and flag anomalies as they happen.

In finance departments, where accuracy is paramount, building a control layer on explainable AI (XAI) will help to ensure that the data and the way the automation is carried out is reliable and based on trusted information. XAI will be the key to successful AI integrations, as it provides an unbreakable chain of thought and a clear audit trail. These build trust with finance and accounting teams and protects them from expensive financial risks.

Sustainable AI infrastructure strategy

To transition to a truly sustainable AI infrastructure, organizations should follow these key strategies:

• Agentic AI employees: Organizations should design agentic AI to act as a digital extension of their most seasoned professionals. To achieve this, finance executives need to train the AI with the same materials and resources an organization would use to train a new hire. This includes formalizing frameworks on best practices, segmenting controls on what the AI can and can’t access, and implementing a continuous feedback loop where humans can validate, refine, and continuously teach the agent to ensure its outputs align with the business. This will help the organization’s agents evolve and become better at generating insights and answers based on everything the team knows.  

• Phased AI integrations: By slowly introducing AI in targeted, manageable chunks, organizations can deliver quick wins that justify further investment. This approach will also help executives and teams to become aligned with the tools, building the confidence to master them and achieve greater value while minimizing the risk of inaccurate outputs.

• XAI: By prioritizing XAI, organizations can ensure auditability and real-time internal controls of their AI deployments. They can see the reasoning behind each output, allowing every action to be fully traceable. This not only avoids the risk of regulatory fines and reputational damage but also helps organizations reduce inconsistency, leading to greater measurable results.

The ultimate payoff of auditable AI

As companies plan their AI integrations for next year, following these strategies can help organizations achieve full executive alignment on their AI investments and gain trust and transparency with executives in every AI action. Ultimately, this can help executives turn AI spend into a justified, exponential investment that supports the overall business mission. The finance and accounting teams that are able to successfully put the governance foundations in place for their AI integrations will be the ones building long-term sustainability with their investments. This commitment to auditable governance will allow organizations to confidently scale trusted agentic AI across the enterprise, paving the way for autonomous financial functions and a competitive edge.

ABOUT THE AUTHOR:

Tammy Coley is a visionary accounting leader with a deep understanding of how accounting processes intersect with modern technology. As chief transformation officer at BlackLine, she brings that vision and experience to customers as they transform their finance and accounting operations through the use of the company’s cloud software tools.

Formerly executive director of Enterprise Accounting and Internal Controls Governance at leading broadband communications company and long-time BlackLine customer Cox Communications, Tammy brings deep industry and product experience to BlackLine. Implementing a continuous accounting model, Tammy transformed the monthly accounting cycle at Cox generating more timely financial statements with greater consistency and accuracy while reducing costs. Prior to Cox, she began her career in public accounting with Ernst & Young. She also spent 12 years in progressive positions, including controller, at Sloan Financial Group.

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