70.8% of UAE investors trust AI to manage their money, survey shows

70.8% of UAE investors trust AI to manage investments

A recent survey headline reports that 70.8 percent of investors in the UAE trust AI to manage investments (2025-11-24 23:11:03). For management, this is a clear signal: clients are ready for AI-supported portfolios and advice-provided you build it with strong oversight.

Why this matters to management

Client trust is the final hurdle for new services. Crossing 70% means AI-backed offerings can move from side experiments to core products.

If you lead wealth, asset management, or finance operations, the window to pilot, control, and scale is open. Wait too long and you'll be reacting to client demand instead of setting the standard.

What investors likely expect

• Clear objectives and guardrails: What the AI can and cannot do, in plain language.
• Performance with accountability: Benchmarks, risk limits, and audit-ready records.
• Human oversight: A named manager with authority to review and override.
• Personalization without data creep: Use only what's needed, keep it secure, and explain why.
• Fair fees: Pricing that reflects real value and lower unit costs at scale.

Practical steps to introduce AI into the investment process

• Map key decisions end to end: research, screening, sizing, execution, rebalancing, and reporting.
• Pick one use case with tight scope and measurable outcomes (e.g., idea screening or cash management).
• Choose the approach: vendor robo stack, AI copilot for PMs, or a controlled in-house model.
• Set policy for data, approvals, and overrides before any client exposure.
• Design human-in-the-loop reviews at the right points: pre-trade, post-trade, and periodic checks.
• Run a limited pilot with a small client segment and pre-agreed risk limits.
• Communicate simply: what the AI does, how it's supervised, and how you measure success.

Risk, compliance, and controls

• Model risk management: Validation, challenge, version control, and change logs.
• Data governance: Source quality, retention rules, and access control with monitoring.
• Suitability checks: Profiles, constraints, and automated alerts on breaches.
• Stress tests: Regime shifts, liquidity shocks, and fat-tail scenarios.
• Vendor due diligence: Security, uptime, SLAs, and exit plans.
• Explainability: Plain-language rationales for trades and portfolio shifts.

Measure what matters

• Net returns vs a clear benchmark, after fees.
• Risk: maximum drawdown, tracking error, and exposure to concentration.
• Decision cycle time: research-to-trade and trade-to-report.
• Unit cost per account and analyst time saved.
• Override rate and reasons (data issue, model error, policy breach).
• Client satisfaction, complaint rate, and retention.
• Model performance stability: drift alerts and periodic revalidation outcomes.

30-60-90 day pilot plan

• Days 0-30: Select use case, define success metrics, lock governance, secure data access, shortlist vendors, and get legal sign-off.
• Days 31-60: Configure, backtest against history, run shadow mode beside human decisions, build dashboards, train staff, prep client messaging.
• Days 61-90: Launch to a small group with human review, run daily controls, publish weekly risk reports, complete post-mortem, then decide go/adjust/stop.

Questions to ask providers

• Which data feeds are used, how often are they updated, and how do you handle gaps?
• What controls exist for overrides, limits, and kill switches?
• How do you generate explanations for recommendations or trades?
• Where is data stored and processed, and can you meet local residency needs?
• Show testing results: backtests, forward tests, and adverse scenarios.
• What is the incident response playbook and notification timeline?
• How is pricing structured and what is the exit plan if we switch vendors?

Team and skills

Stand up a lean pod: portfolio lead, risk, compliance, data engineer, quant/ML, product manager, and client success. Keep decision rights clear and response times short.

Upskill managers and frontline teams on AI literacy, prompt quality, and oversight basics. Useful starting points: Courses by job and AI tools for finance.

What not to automate (yet)

• Final suitability approvals for complex client profiles.
• Trading in illiquid or hard-to-price assets without human checks.
• Exception handling for data errors, outages, or policy conflicts.
• Client communications on losses or major strategy shifts without review.

Bottom line

With 70.8% investor trust, the demand signal is clear. Start with a small, safe pilot, measure it tightly, and scale only what proves value under control.