Artificial intelligence (AI) is one of the topics you hear about the most every day. With competing AI platforms and tools, it can be challenging to cut through the noise to see which ones are worth the hype. As industries like healthcare and finance are already experiencing the transformative benefits of AI, commercial real estate (CRE) approaches AI cautiously, relying on manual, people-driven processes for underwriting, due diligence, and deal sourcing. Historically, growth in CRE meant hiring more analysts and portfolio managers rather than adopting new technology. However, the rise of remote work, digital infrastructure, and industry-specific software solutions has accelerated CRE’s openness to innovation. AI stands as the next frontier for the CRE industry, promising to enhance—not replace—the expertise that has long defined the industry.
Yet some claim AI will fully automate underwriting, removing the need for human expertise altogether. Others believe it’s just a flashy trend with little real-world application. With any buzzworthy technology, there’s always a gap between perception and reality. AI in CRE underwriting transforms lenders' risk assessment and evaluates deals to help make better decisions. Don’t just take our word for it. There are plenty of case studies showcasing the benefits of AI.
For credit officers & risk officers, and origination leaders, understanding what AI can—and can’t—do is essential for making informed underwriting decisions. So, let’s cut through the noise and separate the hype from reality.
Hype #1: AI Will Replace Underwriters
Reality: AI Enhances, Not Replaces, Human Decision-Making
A common misconception is that AI will completely automate the underwriting system, making human underwriters obsolete. When in reality, AI acts as an accelerator—automating the most time-consuming, manual parts of traditional underwriting while still requiring human oversight.
AI-powered underwriting systems leverage machine learning algorithms to analyze data sources quickly, extract key insights, and improve workflow efficiency. These AI solutions can process large amounts of structured and unstructured data, providing underwriters with a risk profile based on historical trends and current market conditions. The key here is that the users, such as the underwriters and portfolio managers, are providing feedback to the AI platform for it to improve its functionality.
However, final underwriting decisions still rely on human judgment.
Key takeaway: AI is not replacing underwriters—it’s giving them the tools to make smarter, faster, and more informed decisions. We wrote an article explaining how AI underwriting works if you want to understand that even more!
Hype #2: AI Underwriting Is Ambiguous
Reality: AI Can Be Transparent and Explainable
Many lenders worry that AI systems are ambiguous and operate as a “black box" where decisions are made without clear explanations. While early AI models lacked transparency, today’s best-in-class solutions are designed with explainability in mind. What do we mean by that?
Regulators, risk teams, and credit committees need visibility into how underwriting decisions are made. That’s why responsible AI solutions provide:
- Clear audit trails detailing how data was processed.
- Defined inputs and outputs that explain how conclusions are reached.
- Customizable risk scoring models allow lenders to tailor risk assessments to their specific lending.
- Human-in-the-loop verification provides a critical feedback loop that strengthens the AI system over time. How? By reviewing and validating outputs, underwriters can flag misclassifications or unusual scenarios the system may miss, helping to fine-tune future performance. This process also plays a role in identifying and addressing unintended biases, ensuring the AI aligns with internal credit standards and fairness expectations. Note: We have a digestible post explaining how human-in-the-loop works at Blooma.
This is particularly important in risk management, as models must ensure fairness while evaluating risk factors across different deal structures.
Key takeaway: AI-powered underwriting can and should be transparent, offering clear explanations for every decision. Executive leaders' full transparency on the underwriting workflow should be a standard in the CRE industry.
Hype #3: AI Won’t Work with Legacy Systems
Reality: Blooma is an insights engine designed to supercharge your existing workflows.
One of the biggest concerns for origination leaders and chief credit/risk officers is whether AI solutions can integrate with legacy loan origination systems (LOS) and existing internal databases. Many financial institutions rely on deeply embedded LOS platforms that have been in place for years—sometimes decades—raising questions about compatibility and implementation challenges.
The reality is that few modern AI solutions are designed to be flexible and interoperable, capable of working alongside traditional underwriting systems without requiring a full overhaul. Leading AI underwriting platforms integrate via APIs, cloud-based data processing, and machine learning algorithms, allowing lenders to enhance existing workflows rather than replace them.
But AI doesn’t have to replace what’s already working. Blooma’s origination intelligence is not a LOS, CRM, or data provider, but instead, it’s a purpose-built insights engine designed to sit on top of your existing systems and make them smarter. It connects fragmented processes, automates repetitive tasks, and delivers real-time, actionable insights to streamline deal screening and underwriting.
Blooma connects seamlessly with platforms like nCino, Encompass, and other core banking systems through flexible API integrations, ensuring auditability, regulatory alignment, and minimal disruption to your current workflow. Our platform ingests and analyzes data from multiple sources—including borrower financials, market comps, and internal documents—giving teams the intelligence they need to identify opportunities, mitigate risks, and move faster.
The result? A more connected, efficient, and intelligent workflow—without the burden of a full system overhaul.
Key takeaway: You don’t need to rip and replace your LOS to adopt AI. Blooma supercharges your current tech stack with enhanced automation, insights, and risk analysis that keep your team in control—and ahead.
Hype #4: AI Can’t Scale for Institutions of Different Sizes
Reality: AI Solutions Are Scalable for Global Banks and Regional Lenders
Another central question from CRE leaders is whether AI-powered underwriting systems can scale effectively for institutions of varying sizes—whether it’s a global financial institution with billions in CRE exposure or a regional bank focusing on middle-market lending.
The reality is that AI-powered risk management tools can be adapted to fit different lending environments. Large-scale lenders benefit from AI’s ability to automate high-volume underwriting and analyze risk factors across complex portfolios, while regional banks gain efficiency in deal screening and risk assessment without adding headcount.
Scalability also extends to compliance. Many lenders note that before adopting AI, they need buy-in from risk and compliance teams to ensure AI-driven underwriting decisions meet regulatory requirements. The best AI platforms address this by providing explainability, audit trails, and human oversight, allowing risk teams to trust AI’s role in the lending process.
Key takeaway: AI underwriting solutions are designed to scale—helping global banks handle high deal volume while giving regional lenders efficiency without added overhead. Compliance approval is a key step, but AI systems built for transparency can help drive buy-in.
Blooma now offers AI solutions for smaller teams through Blooma Pro. Explore our plans to see whether you're a small team optimizing origination or a large institution managing complex portfolios. Blooma has a solution for you.
Hype #5: AI in CRE Underwriting Is Still Years Away
Reality: AI Adoption Is Already Here
Some in the industry believe AI underwriting is a future technology rather than a present-day solution. In reality, leading CRE lenders already use AI systems to reduce underwriting time, improve risk analysis, and enhance portfolio monitoring.
For example, AI-driven case studies show how lenders have:
- Cut underwriting system processing time by 40% with AI-driven document parsing.
- Reduced loan processing times by up to 85%, increasing transaction capacity by 50% with the same headcount.
- Improved profitability by reducing manual errors and increasing loan approval speed. By 50%, enabling faster, higher-quality credit decisions.
- Leveraged generative AI and natural language processing to analyze borrower narratives, financial reports, and unstructured data at scale.
From automating data entry to providing real-time risk alerts, AI is actively reshaping how lenders operate. Institutions that embrace AI today will gain a competitive advantage—while those that delay may struggle to keep pace in an increasingly data-driven industry.
Key takeaway: AI underwriting isn’t theoretical—it’s actively driving efficiency and a competitive edge in CRE lending today.
The Bottom Line: AI Is a Competitive Advantage, Not a Replacement
For credit and risk officers and origination leaders, AI underwriting is not about replacing human expertise but enhancing it. The key to success lies in adopting AI responsibly:
✅ Use AI to eliminate manual inefficiencies so underwriters can focus on strategic decision-making.
✅ Ensure transparency and regulatory compliance by choosing AI models that provide explainability.
✅ Invest in high-quality data to improve AI-driven insights and minimize risk.
✅ Balance AI with human oversight, especially for complex or unique deals.
Lenders that understand and embrace AI’s role as a tool, not a replacement, will be best positioned to navigate the evolving landscape of CRE underwriting.
Is your team ready to harness the power of AI underwriting? Let’s talk about how Blooma can help.