From Hype to Reality: How AI Is Transforming the Finance Back Office

Artificial intelligence has dominated business conversations for years, but in many industries, particularly finance and real estate operations, the story is only just beginning. While marketing and customer-facing technologies have sprinted ahead, back-office finance functions have moved more cautiously. But that caution may be precisely what makes the current wave of AI adoption more meaningful.

A recent webinar focused on Automating AP and Refunds featured David Stifter, Founder and CEO of PredictAP, and Chanin Ballance, CEO and Co-Founder of Roost, (view the recording here) explored where AI is truly delivering value in real estate finance operations, as well as where it still falls short.

The takeaway: success comes not from chasing hype, but from solving well-defined problems with practical tools and realistic expectations.

Why Back-Office AI Often Fails

Executives often say, “We need AI,” without clearly identifying what challenge they want to solve. The result is technology that’s searching for a purpose.

When organizations apply generalized AI tools to highly specific workflows, especially in areas like accounts payable or compliance, the disconnect becomes clear. A demo may look impressive, but real-world integration into existing workflows is where many tools falter.

Finance teams operate in environments with low tolerance for error. Even small inaccuracies can create compliance issues, financial risk, or reputational damage. This makes “human-in-the-loop” oversight and transparent decision-making essential components of any AI-driven system.

Where AI Is Actually Working

The most successful AI deployments are narrowly scoped and carefully defined. Rather than attempting sweeping transformation, effective implementations target repeatable, data-heavy tasks where automation can produce clear, measurable outcomes.

Examples include invoice coding, ledger validation, security deposit reconciliation, and compliance tracking, areas filled with repetitive tasks and nuanced rules. AI can process large volumes of information quickly, identify anomalies, and flag exceptions for human review. This allows finance teams to focus on higher-value work while maintaining control over accuracy.

In many cases, automation doesn’t eliminate human involvement entirely, but it changes where humans focus their attention; routine tasks may disappear, while oversight and exception management remain. Organizations often see significant time savings, but also gain improved visibility and consistency across operations.

Trust, Risk, and the Role of Governance

Responsible AI deployment requires strong governance, clear data handling policies, and security frameworks such as SOC 2 compliance. Transparency around how data is used and how AI decisions are made is critical for building trust with clients and stakeholders.

Equally important is acknowledging that AI is not infallible. Errors will occur, and organizations must design workflows that detect and correct them. Exception handling should be treated as a core feature, not an afterthought. Confidence scores, review dashboards, and human guardrails help ensure accuracy while preserving efficiency gains.

As AI becomes more accessible, some companies are tempted to build their own solutions. While simple prototypes can be created quickly, production-ready systems capable of handling real-world complexity require significant investment, data, and ongoing maintenance—think: multi-line invoice coding. Many organizations underestimate the resources needed to address edge cases, integrate systems, and maintain accuracy over time.

Partnering with specialized vendors allows focus on core business rather than becoming software developers. The decision ultimately comes down to strategic priorities.

Looking Ahead: Lessons and Predictions

In the final minutes, our industry experts reflected on what lies ahead, including ominous realities like the potential for fraud and malfeasance. Continuous improvement emerged as a central theme for Stifter. AI adoption is not a one-time project but an ongoing journey of experimentation, refinement, and adaptation. Organizations should begin with their own operational challenges and gradually integrate tools that deliver tangible value.

In 2026 and beyond, several trends are expected to shape the next phase of AI in real estate finance. Governance and regulation will likely increase as organizations mature their policies and risk management strategies. AI will remain a hybrid model, combining machine learning and algorithms with human oversight, rather than a fully autonomous solution in most financial applications.

The bottom line: AI is no longer just a buzzword. It is becoming a practical tool for solving persistent back-office challenges. The organizations that succeed will be those that approach AI with discipline—defining problems clearly, integrating thoughtfully, and maintaining human oversight. Watch the webinar to hear the full conversation.