Why Ambient, Agentic AI Is the Next Competitive Edge for Mid-Market CFOs

By Jeremy Ung, Chief Technology Officer at BlackLine

We’re seeing a large perception gap: 51% of mid-market CFOs believe they have fully adopted AI within finance; however, only 19% of their finance controllers agree. This divide stems from CFOs praising automated reports without realizing the manual labor required to support them.  

True automation can’t be achieved when AI is layered on top of broken processes. Traditional rule-based automation often fails at the ‘last mile’ of finance. It was designed to follow predefined rules and complete individual tasks, but it can create additional workarounds. While finance teams remain eager to augment their workforce, they must first address this foundational obstacle and bridge the gap to achieve the true agentic execution and value from their AI initiatives. Instead of simply automating a step in an existing workflow, organizations need to create the foundation needed for AI agents to orchestrate multi-step processes across systems, while being able to respond to changing conditions. 

The Fragmented Data Dilemma 

The root of the problem is that finance teams have traditionally worked across multiple ERPs, scattered data, and disconnected tools that were never designed to communicate with one another. This creates fragmented data that prevents AI from gaining a holistic view of financial operations.

According to a survey by Gartner, 63% of organizations either do not have or are unsure if they have the right data management practices for AI. Without the right data management practices in place, the AI is simply digitizing chaos rather than solving it. This increases the risk of untrustworthy data being produced, which can put finance organizations at risk for significant regulatory and compliance fines. 

But the risks extend beyond the data itself. As enterprises begin deploying specialized agents across various enterprise resource planning systems (ERPs), business process outsourcing providers (BPOs), and internal systems, a new governance challenge emerges. Each agent may have its own permissions, different activity logs, and varying risks associated with its actions and unmonitored behavior. Organizations need a unified view of all agent activity across the entire ecosystem, not just another dashboard for each individual AI tool.

If companies don’t have a single source of AI truth, finance teams have limited ability to verify outputs or understand how an agent reached a decision. In finance, 95% accuracy is 100% wrong. A single error can have significant financial, regulatory and reputational consequences. Without data integrity or a clear audit trail showing what autonomous agents are doing, what data they are using and what resources they are accessing, these systems become a liability rather than an asset.

Automation That Adds to the Workload

Beyond the risks of data integrity, this fragmentation also undermines the very promise of efficiency when using traditional automation.  While automation can take over repetitive tasks for employees, it doesn’t necessarily eliminate work. If a human still has to verify every AI output, the AI isn’t an assistant—it’s just another thing to manage, adding burdens for teams rather than eliminating them.

AI needs to meet workers where they are. When a finance professional must toggle between screens or manually move data from an ERP into a standalone AI tool, they aren’t saving time; they’re losing it. This could be why controllers often disagree that their companies have achieved true automation because they are still paying a ‘productivity tax’ instead of focusing on strategic, higher-value tasks. Agentic AI deployments shift this by allowing systems to orchestrate work across multiple steps, so that employees can focus on work that requires judgment. However, organizations need to redesign their workflows so that agentic AI can act in this way and properly execute workflows end to end.

Solving the Bottlenecks with Connected Systems

Building a scalable foundation for ambient, agentic AI can help eliminate many of these manual data bottlenecks and drive measurable business outcomes. Unlike standalone AI tools and standard point-solution automation, ambient AI lives in the background, natively integrated into every workflow across the enterprise. 

Fully connected systems enable AI to operate continuously within existing tools, such as ERPs, emails, and spreadsheets, allowing it to gather situational and operational context to act proactively, allowing finance teams to achieve a faster close with less reconciliation.

This holistic approach is exactly how organizations can bridge the trust gap. AI will have access to a single source of truth and include a clear, auditable trail, making it easier for controllers to verify that actions are accurate.  

 Building the Foundation for Agentic AI

Building the foundation for ambient AI and true automation requires AI deployments to be built on an uncompromising foundation of data integrity. By implementing a unified platform for financial data, AI can work with clean, reliable, proprietary data, enabling more confident and intelligent decision-making.  

Once this unified data layer is built, organizations must establish a centralized governance layer that can observe agents in real time, enforce policy restriction before execution occurs, and create a full audit trail. Rather than logging actions after the fact, organizations can ensure that every agent operates within strict financial controls and intervene before any consequential actions occur, maintaining control as agent deployments scale.

When preparing operations for agentic AI, it’s also important to consider whether to build the technology in-house or buy it. Building production-ready AI systems in-house requires much more than the initial engineering work. Organizations must also account for infrastructure and security, integrations, ongoing maintenance, governance, regulatory administration, audit requirements and the specialized talent needed to keep the system running safely and accurately. These are recurring responsibilities, not one-time implementation costs.

This distinction becomes even more important in finance, where agents need deep business-process context alongside technical capabilities. An agent operating across the financial close, for example, needs its own data connections, guardrails, testing, controls and audit trail, all of which must remain synchronized as processes and models evolve. The build-versus-buy calculation therefore needs to consider the total cost and risk of operating, governing and continuously improving an agentic ecosystem over time.

Organizations must also move beyond treating AI as a software installation and instead treat it as a digital member of the team that must be trained with the same rigor as a human hire. This involves embedding an AI blueprint, codifying institutional knowledge of an organization’s most seasoned finance professionals and the workplace’s best practices into the AI’s core logic. This can help to ensure that AI outputs are aligned with the company’s specific business goals and messaging.

Achieving true autonomy also requires rethinking the human role. Human-in-the-loop oversight must evolve beyond a simple “approve” button. When an agent flags an exception, controllers need visibility into why the decision was made, what data the agent evaluated, and what financial impact it could have. With that transparency, controllers can intervene in real time—pausing, steering, or overriding an agent’s decision when necessary, while still allowing the system to operate autonomously.

Treating AI as an Extension of the Team

As companies operationalize their agentic AI strategies, it’s important for both CFOs and their finance controllers to be aligned on what true automation looks like across the enterprise. By following these strategies, organizations can create the necessary foundation to move past point automation to build a safe, digital workforce where AI works effectively in the background. Agentic, ambient AI can become an equalizer for mid-market CFOs, enabling the companies to operate with the efficiency of Fortune 500 companies.

It allows teams to become force multipliers, completing significantly more work without increasing headcount. Breaking the link between business growth and administrative costs allows mid-market firms to scale operations more efficiently, gaining a competitive advantage.

The definition of a successful finance department will shift from how fast they can close the books to how effectively they can navigate agentic AI integrations. Achieving this will help finance organizations link AI projects to tangible business outcomes. The era of autonomous finance is near and successful mid-market CFOs will no longer be constrained but empowered to lead with speed and precision while building a sustainable, long-term AI foundation for finance.