How to get out of the slow lane in real estate AI

Real estate leaders are convinced artificial intelligence will revolutionise their operations. They're investing in testing and pilots, and chasing the promise of autonomous buildings, predictive maintenance and space planning based on algorithms. Seventy-five percent believe AI will transform how they run their properties within five years. Yet for all this activity, tangible results remain elusive for many of them.

The issue is misdirected ambition, experts say. While teams pour their resources into complex AI systems that may deliver results in years, they're overlooking the immediate opportunities sitting in their everyday workflows.

"Everything we do is a process or a workflow and so much of it is repetitive,” says Michael Taggart, executive director, Client Growth and Technology, APAC, JLL. “But we're too busy aiming for the stars with AI. It’s in these repetitive tasks where we can have quick and easy wins that are going to give us more time to focus on bigger, more strategic things,"

Corporate real estate runs on processes – work orders, access requests, health and safety incidents, room bookings and equipment maintenance. Most of these workflows are manual and follow predictable patterns. They require a person, an email, a phone call, and they drain productive hours from expensive resources.

Take one leading emerging markets bank which has identified that 40% of health and safety incidents, including requests for ergonomic chairs, reports of loose carpet tiles, and minor facility issues, followed identical resolution paths. Each require the same steps: ticket raised, routed to a coordinator, work order created, vendor assigned, task completed, ticket closed. All performed manually.

By deploying AI agents to triage, route and resolve these incidents autonomously, the bank is targeting a 25% reduction in health and safety incident management time and a 40% increase in self-service requests. This effectively removes 40% of routine work orders from one team's workload, freeing staff to focus on serious, complex cases that genuinely require human judgement. The bank is implementing systems to track progress in real time, turning targets into measurable outcomes.

AI excels at spotting repetition. When 50% of service requests follow the same format and produce the same outcome every time, an agent can handle them end-to-end without human intervention.

This approach reclaims the time teams currently spend on tedious, low-value tasks. When facilities staff spend 20% of their week processing identical requests, that time is quantifiable and the financial impact is immediate.

Consider another scenario: a power outage on a company's client-facing meeting floor requires 47 fully booked rooms to be rescheduled. Manually, this task takes a full day. With agentic AI, the enterprise workflow platform, ServiceNow can complete it in minutes.

AI can also turn raw occupancy data into operational forecasts, automatically scaling climate controls on quiet Fridays or flagging equipment anomalies before breakdowns occur. These capabilities add value, but workflow automation delivers faster, more tangible returns.

Ninety-two percent of companies are actively testing AI, yet only 5% have achieved their objectives. The barrier is infrastructure. Real estate operations typically run across multiple disconnected tools, so when maintenance history lives in one system, space booking in another and lease documentation in a third, AI cannot find the patterns it needs.

Taggart explains: "When you're stitching these reports together manually, you're ultimately defending your real estate budget to the CFO using little more than guesswork. It's a lot easier to measure success and equate it to a financial gain when you can literally see that people who were spending 20% of their week performing these tasks now have that time back."

Angela Barwick, executive director, Real Estate Technology Services, JLL, notes that 89% of Asia-Pacific companies are failing to get expected results from at least three of their technology platforms.

"When technology planning is treated as an afterthought to be sorted out during the office fit-out phase rather than at site selection, organisations face major infrastructure compatibility issues, cybersecurity delays and costly design reworks," Barwick says.

Resolving this requires a consolidated platform layer such as ServiceNow, Eptura and Corrigo, which can bring space, visitor and maintenance data together into a single, cloud-based workflow engine.

"Companies frequently rush to purchase expensive AI tools before resolving their underlying data silos, and this results in advanced algorithms running on completely unreliable numbers," observes Shimin Lim, director, Client Growth and Technology, APAC, JLL.

When organisations do build a proper foundation, the results are substantial. Royal London Asset Management deployed AI-powered building management across 12 properties to autonomously tune heating, ventilation and air conditioning systems based on real-time occupancy and weather patterns. This automated optimisation saved occupiers £1 million annually and delivered an average ROI of 330%.

Standing still on AI for workplace optimisation is the real competitive risk. The solution lies in addressing digital infrastructure first and capturing the immediate workflow automation opportunities that most organisations overlook.

By partnering with JLL, organisations can transform disconnected systems into a unified launchpad for intelligent technology. The quick wins are there – capture them before your competitors do.

Contact the JLL Technology Advisory team here.