Intro – Market Dynamics CEOs Can’t Ignore

In Dubai, more than 43,000 property deals worth AED 115 billion were recorded in Q1 2025, with nearly 70% off-plan — evidence of liquidity but also exposure to delivery and handover risks (CBRE). Average residential prices are still climbing, up 3.7% quarter-on-quarter and 18% above their 2014 peak (Knight Frank). Mortgage approvals are up 25% year-on-year, but average monthly payments have risen ~20% due to higher EIBOR rates (CBRE). What if handover delays or rising construction costs trigger a mismatch between off-plan promises and actual delivery? How do rising mortgage rates or regulatory cooling measures alter affordability curves, and which buyer segments will feel the pressure first? What is the risk of defaults at handover, and how exposed are developers’ cash flows to slippage in escrow releases?
In Riyadh, Grade-A office occupancy is near 98%, with rents up ~23% year-on-year to SAR 2,700 psm (JLL; Knight Frank). More than 600 RHQ licenses have been issued and office demand is forecast to rise another 15% by 2027 (MISA). The pipeline is expanding, yet absorption capacity remains tight. Can supply catch up fast enough to prevent overheating, and how should developers phase delivery to align with corporate demand? What role will flexible space or mixed-use integration play in smoothing demand spikes, and how might new PDPL regulations affect data-driven tenant targeting? How do CEOs balance the high margins of Grade-A with the emerging demand for Grade-B refurbishments as corporates seek affordable options?

In Doha, tourism remains the growth engine, with international arrivals surpassing 5.1 million in 2024 — a 25% rise (Qatar Tourism). Hotel occupancy averaged ~71% in Q1 2025 with 2.6m room nights sold, while ADRs have started to normalize (STR Global). Hospitality RevPAR rose nearly 30% in 2024 (STR Global). Which districts will capture the most resilient demand, and how can operators balance oversupplied segments with booming leisure demand? How much more can Qatar leverage niche tourism (MICE, wellness, sports) to sustain occupancy, and how should investors hedge exposure to softer residential demand with stronger hospitality flows? What is the breakeven occupancy needed for new hotel projects, and which segments are most at risk of missing it?

These dynamics are not backdrops; they shape how capital is deployed and risk managed. Data and AI are not cure‐alls, but when applied wisely they become strategic levers — enabling leaders to see around corners, test scenarios, and protect yield in long‐cycle markets.
However, launching data and AI projects requires investment, time, and discipline. In this article, we will explore a perspective on how to look at ROI from investing in data and AI projects within the real estate sector.

Why ROI from AI in Real Estate Looks Invisible

Unlike fast-moving industries, real estate operates on long cycles. Lease renewals typically run about a year in residential, three to five years in retail or offices, construction projects average 24–36 months, and sales pipelines stretch across multiple quarters (JLL; Knight Frank). This makes it difficult to link the impact of data and AI to immediate P&L results (Artefact Experience).

Executives often expect AI to behave like some marketing campaign — delivering results in weeks. In reality, the value of data initiatives in real estate builds more like compound interest: it starts quietly, then multiplies as processes improve, risks reduce, and yields strengthen.

Consider two examples:

  • A tenant-mix optimization model for large retail operators recommended changes that could lift footfall by 5–10%, raise overall rent levels, reduce vacancy, and ultimately improve yields by 3–5%. But execution depended on lease expiries and negotiations over several years, meaning ROI could only be captured gradually (Artefact Experience).
  • By contrast, a lead scoring, profiling, and look-alike use case for sales teams of a large diversified developer boosted campaign conversion rates by 20–30% within two months — showing that quick wins are possible, but are not the rule (Artefact Experience).

Measuring ROI in real estate data programs requires a different lens. Quick wins exist, but sustainable value comes from long-cycle levers — reducing asset yield risk, sharpening investor targeting, refining lease prices, and phasing CapEx of assets with more confidence.

The IT vs. Business Divide

A recurring lesson from experience is how much outcomes depend on who owns and drives the data agenda. Two stories illustrate different outcomes.

In one case, the mandate was handed to IT. Platforms were built, data stored, dashboards delivered — but adoption stayed low. Business units saw little connection to their priorities, and millions in investment became sunk cost (Artefact Experience).

In another, the program was positioned as a CEO-backed, strategy-led, and business-driven mandate. The analytics roadmap was tied directly to investor/customer acquisition, leasing velocity, and OpEx optimization. Within three years, adoption grew steadily as decisions reflected in the outputs. Risk management improved, returns improved, and portfolio steering became faster (Artefact Experience).

There are also examples where IT-led approaches have succeeded. This happens when IT operates with a forward-thinking mindset — embracing agile methods, digital-first practices, and a clear understanding that business needs must guide delivery.

The broader lesson is that success is rarely black or white. Data and AI are not only technology, and not only business — it is a strategic transformation that requires business leadership enabled by an IT function that is innovative and aligned. The most effective models are either data functions spearheaded by CDOs, or CEOs, or in the early stages of transformation, a hybrid approach, where strategy sets the direction and IT provides the scale and technical muscle to deliver.

External benchmarks support this view. A 2024 survey found that while 76% of firms had invested in new platforms, fewer than 30% reported strong adoption — with success rates doubling when the data function reported directly to the C-suite, while maintaining strong IT partnership (Deloitte).

A 3-Phase Playbook for CEOs

Real estate leaders often ask how to structure their data and AI journey. A pragmatic three-year playbook provides discipline and flexibility:

  • Phase 1 – Define the target state and build the foundations. Set the vision, craft a roadmap, and define the role of data & AI with clear success measures. Launch a data & AI factory to churn out use cases. Quick wins in the near term, ‘needle-moving-heavy-weights’ in the mid to long term, while embedding a test-and-learn mindset from the start. Put in place the enablers — platform, governance, operating model, ownership — piloted by a transformation office to orchestrate all the moving pieces, secure business buy-in, and manage change (Artefact Experience).
  • Phase 2 – Launch use cases and drive adoption. Expand into more advanced use cases, building on Year 1 foundations. Focus on change management, value realization tracking, and fostering a stronger data culture. Apply lessons from Year 1 to steer, re-think, and adjust the roadmap (Artefact Experience).
  • Phase 3 – Scale and capture compounding benefits. Scale into more complex analytics and AI, including AI agents, workflow automations, and self-service analytics where users generate their own insights under clear governance and security frameworks. Benefits multiply: improved capital allocation, faster strategic decisions, better operational efficiency, and smarter phasing of investments (Artefact Experience). At this stage, data shifts from support function to strategic lever shaping portfolio performance.

This rhythm ensures that data investments compound over time, aligned with how real estate value is created.

Illustrative Use Cases Driving Value

The playbook comes to life through use cases. In real estate, some consistently stand out, each solving a clear business problem with a data & AI solution and measurable impact:

Developer assessments

  • Business users: Regulators, licensing authorities, risk officers.
  • Business problem: Regulators and authorities lack visibility on developer performance and risks.
  • Analytics & AI solution: Risk scoring models combine commercial, buyers/renters, delivery, and compliance data into composite indices.
  • Potential impact: Stronger oversight, earlier detection of weak performers, and smarter allocation of incentives. Composite scoring can cut review times by 20–30% and flag at-risk developers up to 12 months earlier (Artefact Experience).

Forecasting simulators

  • Business users: Government planners, developers, market researchers, strategy teams.
  • Business problem: Market planners face uncertainty around supply, demand, and pricing.
  • Analytics & AI solution: Scenario modeling engines blend macro indicators, project pipelines, and transaction data to create interactive simulations.
  • Potential impact: More resilient planning and proactive risk management. Simulation tools can cut planning cycles by 25% and improve forecast accuracy by 10–15% (Artefact Experience).

Investor profiling

  • Business users: Regulators, developers’ strategy and product planning teams, marketing, investor relations.
  • Business problem: Many groups lack a unified view of investor segments, their behaviours, and demand characteristics, limiting targeting.
  • Analytics & AI solution: Predictive clustering and machine learning models segment investors by profile, risk appetite, transaction history, and investment goals.
  • Potential impact: Sharper targeting, more efficient campaigns, higher conversion rates. Profiling has improved engagement efficiency by 15–20% and boosted conversions by 10–15% (Artefact Experience).

Tenant-mix optimization (for malls)

  • Business users: Leasing managers, mall operators, asset managers.
  • Business problem: Unbalanced retail mixes erode footfall, rental income, and asset yields.
  • Analytics & AI solution: Optimization algorithms evaluate tenant performance, occupancy cost ratios, rent collections, footfall anchorage, and leasing cycles vs market averages and competition to recommend the mix.
  • Potential impact: 5–10% uplift in footfall, 3–5% higher yields, and reduced rent collection risks (Artefact Experience).

Churn prediction

  • Business users: Property managers, leasing teams, customer success.
  • Business problem: Anticipating tenant turnover is difficult.
  • Analytics & AI solution: Attrition models use payment history, occupancy trends, and sentiment data to generate early warnings.
  • Potential impact: Earlier interventions, reduced vacancy, stronger stability. Modeling can reduce vacancy periods by 20–25% and cut turnover costs by 10–15% (Artefact Experience).

Use cases illustrate how data transforms abstract ambition into measurable value, from regulatory oversight to retail operations.

Final Takeaway

The journey is less a set of isolated lessons and more a narrative of compounding value. Early signals may be faint, but — like land appreciating or a project advancing through phases — the payoff strengthens over time. When championed from the top, data shifts from background utility to lever, shaping leasing choices, investment pacing, and market positioning. With a steady rhythm of foundations, adoption, and scaling, small wins accumulate into structural advantage, while use cases show how insight can flow seamlessly from regulators to developers to operators.

Data behaves like real estate — patient capital that matures with time. Managed strategically, it compounds quietly, lowering risk, sharpening yield, and giving leaders an edge that endures.