Rethinking Finance: Where AI Acts, Explains, and Anticipates

For decades, finance automation has promised faster closes and fewer errors. But AI is now doing something fundamentally different: taking independent action, explaining its logic, and flagging risks before they occur. In this exclusive exchange with Minu Sirsalewala, Executive Editor, Dataquest, Jeremy Ung, CTO at BlackLine, outlines what separates automation from intelligence—and why the CFO’s relevance now hinges on how well they can partner with AI, not just monitor it.

Finance AI is evolving from automation to prediction and action. What’s enabling that shift—and what still stands in the way?

We’ve moved from “AI that flags issues” to “AI that manages outcomes.” These are no longer static tools—they’re agents operating within workflows. They reconcile, assess risks, and identify compliance gaps before they’re visible to humans.

This shift allows finance teams to move from reactive problem-solving to proactive financial management, optimising cash flow and ensuring compliance before issues arise.

A key challenge in making AI-driven finance truly reliable is data quality. Inconsistent or incomplete financial data can lead to inaccurate predictions. Additionally, AI’s decision-making process must be explainable to meet regulatory standards and earn stakeholder trust. Without transparency, finance leaders may hesitate to fully embrace AI-driven insights.

To overcome these obstacles, organisations must implement AI frameworks that align with regulatory requirements while incorporating human oversight. BlackLine envisions AI as a strategic enabler, helping CFOs transition from data consumers to AI-augmented decision-makers. By integrating AI-powered workflows across ERP, treasury, and payment ecosystems, finance leaders can expect automated reconciliation, predictive compliance, and proactive risk sensing. The future of AI in finance isn’t about replacing humans but enhancing financial intelligence, efficiency, and accountability.

Agentic AI is making autonomous financial decisions. Where does that leave human oversight?

AI autonomy doesn’t remove accountability—it reshapes it. Finance teams are no longer just reviewers; they’re scenario analysts. With AI handling the operational noise, humans focus on edge cases and escalation. Our approach at BlackLine ensures every AI decision is auditable. Nothing is hidden. That’s essential in a regulated industry. As Gartner notes, explainable AI will be a defining factor in adoption by 2026. That’s the direction we’re building toward—autonomy with assurance.

Is speed in AI-led finance at odds with compliance?

Not anymore. AI allows speed because it embeds compliance checks within the process. You don’t wait for quarterly audits—you get continuous assurance.

Ensuring regulatory compliance while maintaining agility is a key priority for finance leaders adopting AI-driven solutions. AI must not only generate financial insights quickly but also provide a clear, auditable trail that meets industry regulations. Every AI-driven action should be meticulously recorded to create a transparent audit trail, ensuring finance teams and regulators can verify compliance.

Structured AI frameworks with built-in compliance checks help achieve this balance. AI models should track and document every decision, allowing auditors to assess financial accuracy and regulatory alignment. For instance, AI-powered solutions analyse journal entries in real time, detecting trends, anomalies, and risks to strengthen compliance and streamline audits.

A phased adoption strategy is crucial. Organisations should start with lower-risk automation, such as invoice matching and reconciliations, where human oversight remains integral. As confidence in AI grows, organisations can look at increased automation and a stronger focus on risk-based analysis. This approach allows finance professionals to make strategic decisions with greater confidence while ensuring AI enhances—not compromises—accuracy, efficiency, and regulatory adherence.

With GenAI projected to boost productivity in India’s financial services by 34% to 38% by 2030, the role of AI in accelerating regulatory compliance and decision-making becomes increasingly vital.

Are finance leaders really ready for proactive risk sensing—or is it still aspirational?

Traditional AI applications in finance have focused on detecting anomalies after they happen. However, AI is now evolving into a predictive tool, allowing businesses to anticipate compliance risks before they materialise. Rather than merely highlighting discrepancies, AI-driven solutions analyse vast datasets to identify patterns and predict potential compliance issues early. A McKinsey report highlights that generative AI is set to transform financial risk management by automating and accelerating compliance processes, including climate risk control.

BlackLine’s AI solutions exemplify this shift. The Intercompany Predictive Guidance tool, for instance, scans financial transactions and forecasts risks that could disrupt financial close processes. By integrating with enterprise systems such as ERPs, treasury platforms, and audit tools, AI offers a holistic view of financial risks—enabling finance teams to address compliance concerns proactively.

This transformation is turning finance teams into strategic planners rather than reactive problem-solvers. AI assigns confidence scores to flagged risks, helping finance professionals prioritise their focus. While high-risk concerns are escalated for detailed review, lower-risk discrepancies can be auto-resolved, improving efficiency without compromising oversight. As AI continues to mature, its role in finance will shift from risk detection to proactive financial governance—allowing businesses to stay ahead of regulatory challenges.

Finance teams can assign risk levels and focus efforts where they matter. It’s a shift from volume to value. The result? Fewer surprises, more foresight.

How is GenAI changing the role of the CFO?

Generative AI is transforming finance leaders from data consumers into AI-augmented strategists. Traditionally reliant on historical data, CFOs can now access real-time insights, detect anomalies more quickly, and leverage predictive and prescriptive analytics—enabling faster decision-making and a more informed approach to planning for the future. With AI handling reporting and reconciliations, finance leaders can focus on interpreting data patterns, assessing market trends, and making strategic decisions

To fully leverage AI’s potential, organisations must equip finance leaders with the skills to interpret AI-generated insights while ensuring transparency, accuracy, and compliance.

This requires AI fluency—not technical coding, but the ability to ask the right questions, validate the AI’s logic, and act decisively. BlackLine and EY’s partnership advocates for AI as a collaborative tool, enhancing financial agility rather than replacing human expertise. By embracing AI, finance leaders can improve efficiency, drive innovation, and unlock new growth opportunities.

What’s holding back full-scale digital finance transformation—especially among CFOs?

CFOs often delegate AI to IT, thinking it’s a systems problem. But AI doesn’t just automate processes—it rewires how decisions are made. Finance leaders need to lead this change, not just approve it.

The gap is often strategic misalignment. CIOs chase scale, CFOs want control. Bridging that requires a common goal: autonomous finance that enhances—not replaces—human accountability.

What’s BlackLine’s approach to the cloud challenges many finance teams face?

Hybrid cloud is not a compromise—it’s a bridge. Enterprises often struggle with financial data silos when managing a mix of cloud and on-premises systems. To address this, BlackLine advocates for hybrid cloud finance architectures that seamlessly integrate legacy ERP systems with modern cloud applications through API-driven connectivity. This integration ensures real-time data flow, enabling AI-powered automation for reconciliation and transaction matching while maintaining compliance and auditability.

A phased approach to cloud adoption allows organisations to scale operations efficiently without compromising data governance. By automating lower-risk processes first, finance teams can build confidence in cloud-based solutions while ensuring regulatory compliance. The key to a successful cloud strategy lies in aligning migration decisions with business objectives, prioritising solutions that enhance efficiency and provide audit-ready insights.

Hybrid cloud environments can offer the best of both worlds—allowing sensitive financial data to remain on-premises while leveraging cloud-based tools for real-time reporting, automation, and scalability. Ultimately, BlackLine provides the best of both in a seamless approach that empowers organisations to move from on-premises to hybrid, and eventually to cloud-native—without having to change their underlying processes. This approach transforms financial operations into a more agile, intelligent function, enabling finance teams to make data-driven decisions with greater speed and accuracy.

Our job is to remove friction—so AI can act in real-time without losing compliance or visibility.

As APIs and cloud-native finance stack mature, what’s the new operating model?

We’re seeing finance functions behave more like continuous control engines—real-time inputs, real-time outputs. AI touches ERP, treasury, payments—detecting fraud, optimising working capital, and improving time-to-close.

The future isn’t just faster finance—it’s anticipatory finance. And that requires unified systems, clean data, and a clear AI governance framework.

Five years from now—what will surprise us most about AI in finance?

That it became normal. Reconciliations will run on their own. Risk will be scored continuously. And decisions will be made in real time—with full audit trails.

According to an EY report, The Aldea of India 2025: How much productivity can GenAI unlock in India, the intersection of AI, automation, and finance will be radically transformed. Autonomous finance will be the standard, with AI playing a crucial role in key decision-making areas such as reconciliations, cash flow management, and risk assessment. As financial process automation progresses through GenAI, predictive analytics will refine financial planning, minimise errors, and enable more informed decisions, leading to improved operational efficiency and cost savings.

CFOs will evolve into data-driven leaders, leveraging AI insights to anticipate trends and guide actions, moving beyond traditional reporting. Furthermore, AI that complies with regulatory standards will be crucial, featuring explainability, auto-generated audit trails, and adaptable workflows to keep pace with changing regulations. An AI-first approach will empower finance teams to scale automation, with the real value lying in autonomous, data-driven financial solutions, despite SaaS remaining a key model for delivery.