5 Assumptions That Can Stall AI Progress in Real Estate

AI in commercial real estate is no longer a novelty. While the opportunity for gains in productivity is real, so are the hurdles. From our work with commercial real estate firms (CRE), the gap we see most often at First Line Software lies between reasonable executive real estate assumptions and the operational realities of running AI in production. Here are five assumptions we hear regularly that end up slowing (or stalling) progress:

1. “We proved it in a pilot — now it’s just a matter of scaling.”

Pilots live in clean, controlled environments. Scaling means plugging into Yardi, MRI, Salesforce, Argus, and Excel — each with their own quirks. Add the need for retraining when market conditions change, monitoring for drift, and maintaining uptime across portfolios, and the lift is far heavier than most teams expect. Even the most solid pilots stall here because production is far messier than proof-of-concept.

What we suggest: Bake integration, monitoring, and retraining into the roadmap from day one. Assign ownership and budget early, so the move from pilot to production doesn’t become a dead end.

2. “Our IT team is adding AI into the systems they already manage.”

IT teams are already under pressure to keep core systems stable and secure. AI isn’t “just more IT work” — it introduces GPU infrastructure, new data pipelines, retraining cycles, and governance requirements. When AI gets folded in without dedicated resourcing, it ends up competing with critical IT priorities and therefore slows progress.

What we suggest: Don’t rely on overextended IT teams to carry the weight of AI. Partner with specialists who can work alongside IT and business stakeholders to design, deploy, and maintain AI systems that scale. This keeps your core systems stable while ensuring AI initiatives move forward with the focus and technical depth they require.

3. “Our data warehouse is solid — AI can plug right into it.”

A warehouse that works for BI dashboards isn’t always ready for machine learning. In CRE, key inputs live in PDFs (leases), Excel files (rent rolls), Argus exports, and vendor portals. The challenge isn’t data volume — it’s identity resolution (property/tenant/unit), time alignment (lease terms vs. effective dates), and semantics (what “vacancy” or “concession” means across groups). Without this groundwork, models look great in a demo but degrade fast in production.

What we suggest: Architect an AI-ready data foundation that future models can rely on. Start by identifying the features that truly drive the use cases, pull them directly from system-of-record sources, and design pipelines that treat unstructured documents as first-class data with extraction and validation. The goal isn’t just “more data” — it’s building trusted infrastructure that endures.

4. “Our current risk and compliance framework should work for AI.”

Many large CRE firms already have strong compliance programs — from SOX controls for financial reporting, to GDPR obligations in global operations, to vendor SOC 2 requirements. However, AI requires a new set of obligations: documenting model purpose and limits, logging prompts and outputs, red-teaming models for bias or failure modes, putting human checks on sensitive outputs like pricing, and policing third-party model usage so “Shadow AI” doesn’t leak private information. Existing controls don’t often account for these realities.

What we suggest: Build a comprehensive, trustworthy AI governance framework that scales with your business. At minimum, it should include: (1) an AI acceptable-use policy, (2) concise model cards documenting each use case, (3) clear retention rules for logs, (4) a vendor/LLM due diligence checklist, and (5) a tested incident response playbook. Getting this right up front prevents costly surprises and builds the trust required to scale AI across the enterprise.

5. “We’ll run AI through our standard implementation process, just like our other systems.”

Unlike most software once deployed, AI models don’t remain static and can drift as markets move. A model that looked accurate in January may already be stale by June. Many companies lose trust in the system if they can’t challenge or override results. Without dedicated AI expertise to monitor performance, retrain on fresh data, and close the loop with users, adoption falters, even when the tech “works.”

What we suggest: Treat AI like a product, not a project. Define measurable outcomes (e.g., lease-up speed, forecast accuracy), assign an owner, and track results weekly. Build retraining cadences and user feedback loops. Remember: trust builds when AI models are monitored, retrained, and corrected quickly — giving users confidence that the system will evolve with them, not against them.

Ready to Move from Real Estate Assumptions to Action?

Don’t let common real estate assumptions stall your AI progress. Download The CRE Playbook for Managed AI — your roadmap for navigating integration, governance, and scaling challenges so your AI investments deliver measurable returns.