While IT, education, e-commerce, finance, and marketing sectors actively implemented and automated processes using AI, the real estate market faced numerous limitations.
Early models couldn't handle even basic tasks — only a small portion of processes could be automated. Only in the last 6-12 months have real opportunities for this direction emerged.
In this analysis, we've compiled key technical problems faced by AI enthusiasts and businesses when implementing AI solutions.
Technical problems from AI enthusiasts' side
- Lack of logic — AI handled clear specifications well, wrote and calculated, but struggled to connect information into logical chains.
- No long-term memory — forgot conversation context, critical for AI agents.
- Integration complexity — OpenAI was slow to enter B2B solutions and lacked ready-made API modules.
- API limitations — token limits, high request costs, unstable response speeds.
Technical problems from businesses implementing AI
- Unrealistic metric expectations — companies got rough results requiring refinement, testing, and investment.
- Lack of stability — models frequently "glitched," producing unpredictable results.
- No "out-of-the-box" solution — existing agents required lengthy integration with company infrastructure.
Important to remember:
You're not late to the party!
The real estate sector is just entering the active phase of AI technology adoption.
General solutions don't yet fit this niche's specifics and challenges, so the market remains open for those starting now.
What AI already handles well in real estate — we'll cover in the next post! Follow our #ai_realestate series in the Telegram channel RED LAB.
